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zNdFW(0>zd(Q#!)1G})yTz{sTw@sn!2WNKHo-iCd;C@zp+Hp=KNob05-X@bo}dupV> z#?XDnyW6N+?{)_tlhA}_?S}3Md-jZ}Ksw z1UuSzv?&24HC}#y*M3dd1Z^XuWA_raNDsFwspd)RWuUr(@OEmx!0shTfv5@m=8yn-JqN11sRMTL@gVS*bh{$g^EqgkG;ih zV<=_2JJa*?^8*K-+-PlYd9P#ph5q#k4GhMXL&mpX(s#3zx8AVoZZTCY%!V&P_(y?Z zy6L#QP0d$2l0#C2TV7K(kK&Ils#@Yq;eNC6E&brw_W z!S=(m6Wzd!!6C^nU$(_u@+^S!R+;R?#MhEAl)J69^<2p>adCE3VcOl6tcZuu$Uy$l zzL(T`9fO6*c_ANQ2**!LoL+#CIgm$SAXO<`SXjt`!z+Gi=~p_n>KYn1$*vtLx)8?e zXlM*;@wFZSvx*Pr*E58GLl<1$QnS>oKJ0bDOxaihKimDscaBMW_fSuobkx;Np>gyv z1czccg2cna(j5!1i1rbZ!^=OahY1DItoAlGHW3yE-GEYC2_~<+%haSG^3b)j%fV6u z+Ys*hJ~+LyFo>WQmMu^WmDjsKa8qj89gA25^i9i*a)DFc;)=KIH&$j>N;Hg(?Nigz z49OMl1c+U8AdWMHFlyi{Yv0 + + + + + + + + + +geowrangler - Area Zonal Stats + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Area Zonal Stats

+
+ +
+
+ generate vector zonal stat features for area data +
+
+ + +
+ + + + +
+ + + +
+ + + +

+
+

source

+
+

create_area_zonal_stats

+
+
 create_area_zonal_stats (aoi:geopandas.geodataframe.GeoDataFrame,
+                          data:geopandas.geodataframe.GeoDataFrame,
+                          aggregations:List[Dict[str,Any]]=[],
+                          include_intersect=True, fix_min=True)
+
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
aoiGeoDataFrameArea of interest for which zonal stats are to be computed for
dataGeoDataFrameSource gdf of region/areas containing data to compute zonal stats from
aggregationsList[]List of agg specs, with each agg spec applied to a data column
include_intersectboolTrueAdd column ‘intersect_area_sum’ w/ch computes total area of data areas intersecting aoi
fix_minboolTrueSet min to zero if there are areas in aoi w/ch do not containing any intersecting area from the data.
+
+
+

Test data

+
+

Simple squares

+

Given an aoi (simple_aoi) and geodataframe containing sample data (simple_data)

+
+
simple_aoi
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
geometry
0POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...
1POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...
2POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...
3POLYGON ((3.000 0.000, 3.000 1.000, 4.000 1.00...
4POLYGON ((4.000 0.000, 4.000 1.000, 5.000 1.00...
+ +
+
+
+
+
+
simple_data
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
geometrypopulationinternet_speed
0POLYGON ((0.250 0.000, 0.250 1.000, 1.250 1.00...10020.0
1POLYGON ((1.250 0.000, 1.250 1.000, 2.250 1.00...20010.0
2POLYGON ((2.250 0.000, 2.250 1.000, 3.250 1.00...3005.0
+ +
+
+
+
+
+
ax = plt.axes()
+ax = simple_data.plot(
+    ax=ax, color=["orange", "brown", "purple"], edgecolor="yellow", alpha=0.4
+)
+ax = simple_aoi.plot(
+    ax=ax, facecolor="none", edgecolor=["r", "g", "b", "orange", "purple"]
+)
+
+
+
+

+
+
+
+
+

The red,green,blue, orange and purple outlines are the 5 regions of interest (aoi) while the orange,brown, purple areas are the data areas.

+
+
empty_aoi_results = create_area_zonal_stats(simple_aoi, simple_data)
+
+
+
empty_aoi_results
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
geometryintersect_area_sum
0POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...0.75
1POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...1.00
2POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...1.00
3POLYGON ((3.000 0.000, 3.000 1.000, 4.000 1.00...0.25
4POLYGON ((4.000 0.000, 4.000 1.000, 5.000 1.00...0.00
+ +
+
+
+
+
+
simple_aoi_results = create_area_zonal_stats(
+    simple_aoi,
+    simple_data,
+    [
+        dict(func="count", output="sample_count"),
+        dict(func=["sum", "count"], column="population"),
+        dict(func=["mean", "max", "min", "std"], column="internet_speed"),
+    ],
+)
+
+
CPU times: user 61.4 ms, sys: 0 ns, total: 61.4 ms
+Wall time: 58.1 ms
+
+
+
+
simple_aoi_results
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
geometryintersect_area_sumsample_countpopulation_sumpopulation_countinternet_speed_meaninternet_speed_maxinternet_speed_mininternet_speed_std
0POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...0.751.075.01.015.00020.00.0NaN
1POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...1.002.0175.02.06.25020.010.07.071068
2POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...1.002.0275.02.03.12510.05.03.535534
3POLYGON ((3.000 0.000, 3.000 1.000, 4.000 1.00...0.251.075.01.01.2505.00.0NaN
4POLYGON ((4.000 0.000, 4.000 1.000, 5.000 1.00...0.00NaNNaNNaNNaNNaN0.0NaN
+ +
+
+
+
+
+
simple_aoi_results.population_sum.sum(axis=None)
+
+
600.0
+
+
+
+
corrected_aoi_results = create_area_zonal_stats(
+    simple_aoi,
+    simple_data,
+    [
+        dict(func=["sum", "count"], column="population"),
+        dict(
+            func=["mean", "imputed_mean", "raw_max", "raw_min", "raw_std"],
+            column="internet_speed",
+            output=[
+                "internet_speed_mean",
+                "internet_speed_imputed_mean",
+                "internet_speed_max",
+                "internet_speed_min",
+                "internet_speed_std",
+            ],
+        ),
+    ],
+    fix_min=False,
+)
+
+
CPU times: user 53.9 ms, sys: 956 µs, total: 54.9 ms
+Wall time: 50.1 ms
+
+
+
+
corrected_aoi_results
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
geometryintersect_area_sumpopulation_sumpopulation_countinternet_speed_meaninternet_speed_imputed_meaninternet_speed_maxinternet_speed_mininternet_speed_std
0POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...0.7575.01.015.00020.00020.020.0NaN
1POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...1.00175.02.06.2506.25020.010.07.071068
2POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...1.00275.02.03.1253.12510.05.03.535534
3POLYGON ((3.000 0.000, 3.000 1.000, 4.000 1.00...0.2575.01.01.2505.0005.05.0NaN
4POLYGON ((4.000 0.000, 4.000 1.000, 5.000 1.00...0.00NaNNaNNaNNaNNaNNaNNaN
+ +
+
+
+
+
+
aois_no_nas = create_area_zonal_stats(
+    simple_aoi,
+    simple_data,
+    [
+        dict(func=["sum", "count"], column="population", fillna=[True, True]),
+        dict(
+            func=["mean", "imputed_mean", "raw_max", "raw_min", "raw_std"],
+            column="internet_speed",
+            output=[
+                "internet_speed_mean",
+                "internet_speed_imputed_mean",
+                "internet_speed_max",
+                "internet_speed_min",
+                "internet_speed_std",
+            ],
+            fillna=[True, True, True, True, True],
+        ),
+    ],
+    fix_min=False,
+)
+
+
CPU times: user 34 ms, sys: 12.1 ms, total: 46 ms
+Wall time: 42.6 ms
+
+
+
+
aois_no_nas
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
geometryintersect_area_sumpopulation_sumpopulation_countinternet_speed_meaninternet_speed_imputed_meaninternet_speed_maxinternet_speed_mininternet_speed_std
0POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...0.7575.01.015.00020.00020.020.00.000000
1POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...1.00175.02.06.2506.25020.010.07.071068
2POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...1.00275.02.03.1253.12510.05.03.535534
3POLYGON ((3.000 0.000, 3.000 1.000, 4.000 1.00...0.2575.01.01.2505.0005.05.00.000000
4POLYGON ((4.000 0.000, 4.000 1.000, 5.000 1.00...0.000.00.00.0000.0000.00.00.000000
+ +
+
+
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/datasets_geofabrik.html b/datasets_geofabrik.html new file mode 100644 index 0000000..901746c --- /dev/null +++ b/datasets_geofabrik.html @@ -0,0 +1,848 @@ + + + + + + + + + + +geowrangler - Datasets Geofabrik + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Datasets Geofabrik

+
+ +
+
+ download Geofabrik data +
+
+ + +
+ + + + +
+ + + +
+ + + +

+
+

source

+
+

list_geofabrik_regions

+
+
 list_geofabrik_regions ()
+
+

Get list of regions from geofabrik index

+
+

source

+
+
+

get_osm_download_url

+
+
 get_osm_download_url (region, year=None)
+
+
+

source

+
+
+

get_download_filepath

+
+
 get_download_filepath (url, directory)
+
+
+

source

+
+
+

download_geofabrik_region

+
+
 download_geofabrik_region (region:str, directory:str='data/',
+                            overwrite=False, year=None,
+                            show_progress=True, chunksize=8192)
+
+

Download geofabrik region to path

+
+

source

+
+
+

download_osm_region_data

+
+
 download_osm_region_data (region, year=None,
+                           cache_dir='~/.cache/geowrangler',
+                           use_cache=True, chunksize=8192,
+                           show_progress=True)
+
+
+

source

+
+
+

OsmDataManager

+
+
 OsmDataManager (cache_dir='~/.cache/geowrangler')
+
+

An instance of this class provides convenience functions for loading and caching OSM data

+
+

source

+
+
+

OsmDataManager.load_pois

+
+
 OsmDataManager.load_pois (region, year=None, use_cache=True,
+                           chunksize=1048576, show_progress=True)
+
+
+

source

+
+
+

OsmDataManager.load_roads

+
+
 OsmDataManager.load_roads (region, year=None, use_cache=True,
+                            chunksize=1048576, show_progress=True)
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/datasets_nightlights.html b/datasets_nightlights.html new file mode 100644 index 0000000..8a26c10 --- /dev/null +++ b/datasets_nightlights.html @@ -0,0 +1,888 @@ + + + + + + + + + + +geowrangler - Night Lights + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Night Lights

+
+ +
+
+ utilities for caching and downloading night time lights datasets from Earth Observation Group (EOG) +
+
+ + +
+ + + + +
+ + + +
+ + + +

+
+

source

+
+

get_eog_access_token

+
+
 get_eog_access_token (username, password, save_token=False,
+                       save_path='~/.eog_creds/eog_access_token',
+                       set_env=True, env_token_var='EOG_ACCESS_TOKEN')
+
+
+

source

+
+
+

clear_eog_access_token

+
+
 clear_eog_access_token (save_file='~/.eog_creds/eog_access_token',
+                         env_var='EOG_ACCESS_TOKEN', clear_file=True,
+                         clear_env=True)
+
+
+

source

+
+
+

setup_eog_auth_headers

+
+
 setup_eog_auth_headers (headers, access_token, env_var, creds_file)
+
+
+

source

+
+
+

download_url

+
+
 download_url (url, dest=None, access_token=None, headers=None,
+               timeout=None, show_progress=True, chunksize=1048576,
+               env_var='EOG_ACCESS_TOKEN',
+               creds_file='~/.eog_creds/eog_access_token')
+
+

Download url to dest and show progress

+
+

source

+
+
+

unzip_eog_gzip

+
+
 unzip_eog_gzip (gz_file, dest=None, delete_src=False)
+
+
+

source

+
+
+

clip_raster

+
+
 clip_raster (input_raster_file, dest, bounds, buffer=None)
+
+
+

source

+
+
+

make_url

+
+
 make_url (year, viirs_data_type='average',
+           ntlights_base_url='https://eogdata.mines.edu/nighttime_light',
+           version='v21', product='annual', coverage='global',
+           process_suffix='c202205302300', vcmcfg='vcmslcfg')
+
+
+

source

+
+
+

make_clip_hash

+
+
 make_clip_hash (year, bounds, viirs_data_type='average', version='v21',
+                 product='annual', coverage='global',
+                 process_suffix='c202205302300', vcmcfg='vcmslcfg')
+
+
+

source

+
+
+

generate_clipped_raster

+
+
 generate_clipped_raster (year, bounds, dest, viirs_data_type='average',
+                          version='v21', product='annual',
+                          coverage='global',
+                          cache_dir='~/.cache/geowrangler/nightlights',
+                          process_suffix='c202205302300',
+                          vcmcfg='vcmslcfg')
+
+
+

source

+
+
+

generate_clipped_metadata

+
+
 generate_clipped_metadata (year, bounds, viirs_data_type, version,
+                            product, coverage, clip_cache_dir,
+                            process_suffix, vcmcfg)
+
+
+

source

+
+
+

get_clipped_raster

+
+
 get_clipped_raster (year, bounds, viirs_data_type='average',
+                     version='v21', product='annual', coverage='global',
+                     cache_dir='~/.cache/geowrangler/nightlights',
+                     process_suffix='c202205302300', vcmcfg='vcmslcfg')
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/datasets_ookla.html b/datasets_ookla.html new file mode 100644 index 0000000..be2e146 --- /dev/null +++ b/datasets_ookla.html @@ -0,0 +1,1169 @@ + + + + + + + + + + +geowrangler - Datasets Ookla + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Datasets Ookla

+
+ +
+
+ download Ookla data +
+
+ + +
+ + + + +
+ + + +
+ + + +

+
+

source

+
+

list_ookla_files

+
+
 list_ookla_files ()
+
+

Get list of ookla data

+
+
ookla_files = list_ookla_files()
+assert ookla_files.get(OoklaFile("fixed", "2021", "2"), None) is not None
+
+
+

source

+
+
+

download_ookla_file

+
+
 download_ookla_file (type_:str, year:str, quarter:str,
+                      directory:str='data/', overwrite:bool=False,
+                      show_progress=True, chunksize=8192, reporthook=None)
+
+

Download ookla file to path

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
type_strInternet connection type: ‘fixed’ or ‘mobile’
yearstrYear (e.g. ‘2020’)
quarterstrQuarter (valid values: ‘1’,‘2’,‘3’,‘4’)
directorystrdata/Download directory
overwriteboolFalseOverwrite if existing
show_progressboolTrueshow progres bar
chunksizeint8192Download chunksize
reporthookNoneTypeNoneUse custom progress bar
ReturnsOptional
+
+

source

+
+
+

parallel_download

+
+
 parallel_download (item)
+
+
+

source

+
+
+

download_ookla_parallel

+
+
 download_ookla_parallel (num_expected_ookla_files, type_, year,
+                          directory, overwrite, show_progress, chunksize,
+                          reporthook)
+
+
+

source

+
+
+

download_ookla_year_data

+
+
 download_ookla_year_data (type_, year, cache_dir, use_cache=True,
+                           show_progress=True, chunksize=8192,
+                           reporthook=None)
+
+

Download ookla data for a specifed type (fixed or mobile) and year. Data for all 4 quarters will be downloaded.

+
+

source

+
+
+

lookup_ookla_file

+
+
 lookup_ookla_file (filename)
+
+

Get OoklaFile for the given filename

+
+
assert lookup_ookla_file("2021-04-01_performance_fixed_tiles.parquet") == OoklaFile(
+    "fixed", "2021", "2"
+)
+
+
+

source

+
+
+

compute_datakey

+
+
 compute_datakey (aoi_bounds, type_, year, return_geometry)
+
+
+

source

+
+
+

write_ookla_metajson

+
+
 write_ookla_metajson (cache_dir, data_key, total_bounds, type_, year,
+                       return_geometry)
+
+
+

source

+
+
+

OoklaDataManager

+
+
 OoklaDataManager (cache_dir='~/.cache/geowrangler')
+
+

An instance of this class provides convenience functoins for loading and caching Ookla data

+
+

source

+
+
+

OoklaDataManager.reinitialize_processed_cache

+
+
 OoklaDataManager.reinitialize_processed_cache ()
+
+

Reinitialize processed_cache_dir to start over from scratch.

+
+

source

+
+
+

OoklaDataManager.reinitialize_aggregated_cache

+
+
 OoklaDataManager.reinitialize_aggregated_cache ()
+
+

Reinitialize aggregated_cache_dir to start over from scratch.

+
+

source

+
+
+

OoklaDataManager.load_type_year_data

+
+
 OoklaDataManager.load_type_year_data
+                                       (aoi:geopandas.geodataframe.GeoData
+                                       Frame, type_:str, year:str,
+                                       use_cache=True,
+                                       return_geometry=False,
+                                       show_progress=True, chunksize=8192,
+                                       reporthook=None)
+
+

Load Ookla data across all quarters for a specified aoi, type (fixed, mobile) and year

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
aoiGeoDataFramearea of interest
type_strookla data type: fixed or mobile
yearstryear
use_cacheboolTrueuse cache dir
return_geometryboolFalseinclude geometry in returned values
show_progressboolTruedisplay progress bar
chunksizeint8192download buffer size
reporthookNoneTypeNonecustom progress bar
+
+

source

+
+
+

OoklaDataManager.aggregate_ookla_features

+
+
 OoklaDataManager.aggregate_ookla_features
+                                            (aoi:geopandas.geodataframe.Ge
+                                            oDataFrame, type_:str,
+                                            year:str, use_cache=True,
+                                            return_geometry=False,
+                                            output_crs='epsg:4326', aggreg
+                                            ations:Dict[str,Any]={'mean_av
+                                            g_d_kbps': ('avg_d_kbps',
+                                            'mean'), 'mean_avg_u_kbps':
+                                            ('avg_u_kbps', 'mean'),
+                                            'mean_avg_lat_ms':
+                                            ('avg_lat_ms', 'mean'),
+                                            'mean_num_tests': ('tests',
+                                            'mean'), 'mean_num_devices':
+                                            ('devices', 'mean')},
+                                            show_progress=True,
+                                            chunksize=8192,
+                                            reporthook=None)
+
+

Generates yearly aggregate features for the AOI based on Ookla data for a given type (fixed, mobile) and year.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
aoiGeoDataFrameArea of interest
type_strOokla speed type: ’fixedormobile`
yearstrYear to aggregate (over 4 quarters)
use_cacheboolTrueUse cached data in cache dir as specified in ookla_data_manager
return_geometryboolFalseSave aggregated data as geojson
output_crsstrepsg:4326crs to use in creating aggregated geodataframe
aggregationsDict{‘mean_avg_d_kbps’: (‘avg_d_kbps’, ‘mean’), ‘mean_avg_u_kbps’: (‘avg_u_kbps’, ‘mean’), ‘mean_avg_lat_ms’: (‘avg_lat_ms’, ‘mean’), ‘mean_num_tests’: (‘tests’, ‘mean’), ‘mean_num_devices’: (‘devices’, ‘mean’)}Aggregation functions on ookla data (see https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.agg.html)
show_progressboolTruedisplay progress bar
chunksizeint8192download buffer size
reporthookNoneTypeNonecustom progress bar
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/datasets_utils.html b/datasets_utils.html new file mode 100644 index 0000000..1e02f56 --- /dev/null +++ b/datasets_utils.html @@ -0,0 +1,786 @@ + + + + + + + + + + +geowrangler - Datasets Utils + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Datasets Utils

+
+ +
+
+ utilities for caching and downloading datasets +
+
+ + +
+ + + + +
+ + + +
+ + + +

+
+

source

+
+

urlretrieve

+
+
 urlretrieve (url, filename, headers=None, reporthook=None, timeout=None,
+              chunksize=8192)
+
+

Same as urllib.request.urlretrieve but also works with Request objects

+
+

source

+
+
+

make_report_hook

+
+
 make_report_hook (show_progress)
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/dhs.html b/dhs.html new file mode 100644 index 0000000..d0a18f4 --- /dev/null +++ b/dhs.html @@ -0,0 +1,909 @@ + + + + + + + + + + +geowrangler - DHS Utilities + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

DHS Utilities

+
+ +
+
+ utility function for dealing with DHS data +
+
+ + +
+ + + + +
+ + + +
+ + + +

+
+

source

+
+

load_column_config

+
+
 load_column_config (country:str)
+
+

Get predined column mapping for some countries. The following countries area supported: - ph Philippines - tl East Timor - mm Myanmar - kh Cambodia

+ + + + + + + + + + + + + + + + + + + + +
TypeDetails
countrystr2 letter character representing the country
Returnsdict
+
+

source

+
+
+

load_dhs_file

+
+
 load_dhs_file (household_data:str)
+
+

Loads household data and renames columns based on variable labels of the file

+ + + + + + + + + + + + + + + + + + + + +
TypeDetails
household_datastrstr or pathlike object to the household data
ReturnsDataFrame
+
+

source

+
+
+

apply_threshold

+
+
 apply_threshold (df:pandas.core.frame.DataFrame, columns:List[str],
+                  config:dict)
+
+

Applies a threshold to a list of columns

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDetails
dfDataFrameDataframe
columnsListList of columns to apply the threshold
configdictConfig containing the min and max of each columns
ReturnsDataFrame
+
+

source

+
+
+

assign_wealth_index

+
+
 assign_wealth_index (asset_df:pandas.core.frame.DataFrame, use_pca=True)
+
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
asset_dfDataFrameDataframe containg only the features to apply wealth index
use_pcaboolTrueif calculating wealth index should be done via PCA or via Sigular Value Decomposition
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/distance_zonal_stats.html b/distance_zonal_stats.html new file mode 100644 index 0000000..4ab270c --- /dev/null +++ b/distance_zonal_stats.html @@ -0,0 +1,1215 @@ + + + + + + + + + + +geowrangler - Distance Zonal Stats + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Distance Zonal Stats

+
+ +
+
+ generate vector zonal stat features for distances to features +
+
+ + +
+ + + + +
+ + + +
+ + + +

+
+

source

+
+

create_distance_zonal_stats

+
+
 create_distance_zonal_stats (aoi:geopandas.geodataframe.GeoDataFrame,
+                              data:geopandas.geodataframe.GeoDataFrame,
+                              max_distance:float,
+                              aggregations:List[Dict[str,Any]]=[],
+                              distance_col:str='nearest')
+
+

Computes zonal stats based on nearest matching data geometry within max_distance. Note that setting a too high max_distance (or None) will incur a performance cost.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
aoiGeoDataFrameArea of interest for which zonal stats are to be computed for
dataGeoDataFrameSource gdf of region/areas containing data to compute zonal stats from
max_distancefloatmax distance to compute distance for (the larger the slower the join), set to None for no limit
aggregationsList[]aggregations
distance_colstrnearestcolumn name of the distance column, set to None if not wanted in results
+
+
+

Test data

+
+

Simple squares

+

Given an aoi (simple_aoi) and geodataframe containing sample data (simple_data)

+
+
simple_aoi
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + +
geometry
0POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...
1POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...
2POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...
+ +
+
+
+
+
+
simple_data
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
geometrypopulationinternet_speed
0POLYGON ((0.250 0.000, 0.250 1.000, 1.250 1.00...10020.0
1POLYGON ((1.250 0.000, 1.250 1.000, 2.250 1.00...20010.0
2POLYGON ((2.250 0.000, 2.250 1.000, 3.250 1.00...3005.0
+ +
+
+
+
+

We also have simple point data which do not intersect with our AOIs.

+
+
simple_point_data
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
geometrypopulationinternet_speed
0POINT (0.500 3.000)10020.0
1POINT (0.500 4.000)600120.0
2POINT (0.500 5.000)1100220.0
3POINT (0.500 6.000)1600320.0
4POINT (0.500 7.000)2100420.0
5POINT (1.500 3.000)20010.0
6POINT (1.500 4.000)700110.0
7POINT (1.500 5.000)1200210.0
8POINT (1.500 6.000)1700310.0
9POINT (1.500 7.000)2200410.0
10POINT (2.500 3.000)3005.0
11POINT (2.500 4.000)800105.0
12POINT (2.500 5.000)1300205.0
13POINT (2.500 6.000)1800305.0
14POINT (2.500 7.000)2300405.0
+ +
+
+
+
+
+
ax = plt.axes()
+ax = simple_data.plot(
+    ax=ax, color=["orange", "brown", "purple"], edgecolor="yellow", alpha=0.4
+)
+ax = simple_aoi.plot(ax=ax, facecolor="none", edgecolor=["r", "g", "b"])
+ax = simple_point_data.plot(ax=ax)
+
+
+
+

+
+
+
+
+

The red,green,blue outlines are the 3 regions of interest (aoi) while the orange,brown, purple areas are the data areas.The blue dots are data which do not intersect our AOIs.

+
+
results = create_distance_zonal_stats(
+    simple_aoi,
+    simple_point_data,
+    max_distance=7,
+    aggregations=[
+        dict(func="count"),
+        dict(func="sum", column="population"),
+        dict(func="mean", column="internet_speed"),
+    ],
+)
+
+
CPU times: user 32.1 ms, sys: 11 ms, total: 43.1 ms
+Wall time: 36 ms
+
+
+
+
results
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
geometryindex_countpopulation_suminternet_speed_meannearest
0POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...110020.02.0
1POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...120010.02.0
2POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...13005.02.0
+ +
+
+
+
+
+
results2 = create_distance_zonal_stats(
+    simple_aoi,
+    simple_data,
+    max_distance=1,
+    aggregations=[
+        dict(func="count"),
+        dict(func="sum", column="population"),
+        dict(func="mean", column="internet_speed"),
+    ],
+)
+
+
CPU times: user 38.5 ms, sys: 0 ns, total: 38.5 ms
+Wall time: 35 ms
+
+
+
+
results2
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
geometryindex_countpopulation_suminternet_speed_meannearest
0POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...110020.00.0
1POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...230015.00.0
2POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...25007.50.0
+ +
+
+
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/grids.html b/grids.html new file mode 100644 index 0000000..78a1561 --- /dev/null +++ b/grids.html @@ -0,0 +1,1016 @@ + + + + + + + + + + +geowrangler - Grids + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Grids

+
+ +
+
+ grid generators +
+
+ + +
+ + + + +
+ + + +
+ + + +

+
+

source

+
+

SquareGridBoundary

+
+
 SquareGridBoundary (x_min:float, y_min:float, x_max:float, y_max:float)
+
+

Reusing Boundary. x_min, y_min, x_max, and y_max are in the the target crs

+
+

source

+
+
+

SquareGridGenerator

+
+
 SquareGridGenerator (cell_size:float, grid_projection:str='EPSG:3857',
+                      boundary:Union[__main__.SquareGridBoundary,List[floa
+                      t]]=None)
+
+

Initialize self. See help(type(self)) for accurate signature.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
cell_sizefloatheight and width of a square cell in meters
grid_projectionstrEPSG:3857projection of grid output
boundaryUnionNoneoriginal boundary
+
+

source

+
+
+

SquareGridGenerator.create_cell

+
+
 SquareGridGenerator.create_cell (x:float, y:float)
+
+

Create a square cell based on the top left coordinates and cell_size

+ + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDetails
xfloatx coord of top left
yfloaty coord of top left
ReturnsPolygon
+
+

source

+
+
+

SquareGridGenerator.create_grid_for_polygon

+
+
 SquareGridGenerator.create_grid_for_polygon (boundary, geometry)
+
+
+

source

+
+
+

SquareGridGenerator.generate_grid

+
+
 SquareGridGenerator.generate_grid
+                                    (gdf:geopandas.geodataframe.GeoDataFra
+                                    me)
+
+
+

source

+
+
+

H3GridGenerator

+
+
 H3GridGenerator (resolution:int, return_geometry:bool=True)
+
+

Initialize self. See help(type(self)) for accurate signature.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
resolutionintResolution of hexagon. See: https://h3geo.org/docs/core-library/restable/ for more info
return_geometryboolTrueIf geometry should be returned. Setting this to false will only return hex_ids
+
+

source

+
+
+

H3GridGenerator.get_hexes_for_polygon

+
+
 H3GridGenerator.get_hexes_for_polygon
+                                        (poly:shapely.geometry.polygon.Pol
+                                        ygon)
+
+
+

source

+
+
+

H3GridGenerator.generate_grid

+
+
 H3GridGenerator.generate_grid (gdf:geopandas.geodataframe.GeoDataFrame)
+
+
+

source

+
+
+

BingTileGridGenerator

+
+
 BingTileGridGenerator (zoom_level:int, return_geometry:bool=True,
+                        add_xyz_cols:bool=False)
+
+

Initialize self. See help(type(self)) for accurate signature.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
zoom_levelintZoom level of tile. See: https://docs.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system for more info
return_geometryboolTrueIf geometry should be returned. Setting this to false will only return quadkeys
add_xyz_colsboolFalseIf quadkey should be converted to their xy values.
+
+

source

+
+
+

BingTileGridGenerator.get_all_tiles_for_polygon

+
+
 BingTileGridGenerator.get_all_tiles_for_polygon
+                                                  (polygon:shapely.geometr
+                                                  y.polygon.Polygon)
+
+

Get the interseting tiles with polygon for a zoom level. Polygon should be in EPSG:4326

+
+

source

+
+
+

BingTileGridGenerator.generate_grid

+
+
 BingTileGridGenerator.generate_grid
+                                      (gdf:geopandas.geodataframe.GeoDataF
+                                      rame)
+
+
+

source

+
+
+

BingTileGridGenerator.generate_grid_join

+
+
 BingTileGridGenerator.generate_grid_join
+                                           (gdf:geopandas.geodataframe.Geo
+                                           DataFrame, filter:bool=True,
+                                           n_workers=4, progress=True)
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/images/Geowrangler.svg b/images/Geowrangler.svg new file mode 100755 index 0000000..c83af60 --- /dev/null +++ b/images/Geowrangler.svg @@ -0,0 +1,26029 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/images/admin3-ncr.png 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+geowrangler - Geowrangler
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      Geowrangler
      
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+    Tools for wrangling with geospatial data
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      Geowrangler logo
      
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      Overview
      
+
      Geowrangler is a python package for geodata wrangling. It helps you build data transformation workflows that have no out-of-the-box solutions from other geospatial libraries.
      
+
      We surveyed our past geospatial projects to extract these solutions for our work and hope that these will be useful for others as well.
      
+
      Our audience are researchers, analysts and engineers delivering geospatial projects.
      
+
      We welcome your comments, suggestions, bug reports and code contributions to make Geowrangler better.
      
+
      
+
      
+
      Context
      
+
      Geowrangler was borne out of our efforts to reduce the amount of boilerplate code in wrangling geospatial data. It builds on top of existing geospatial libraries such as geopandas, rasterio, rasterstats, morecantile and others. Our goals are centered on the following tasks:
      
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+
      • Extracting area of interest zonal statistics from vector and raster data
        • 
+
      • Gridding areas of interest
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      • Validating geospatial datasets
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      • Downloading of publically available geospatial datasets (e.g. OSM, Ookla, Nightlights)
        • 
+
      • Other geospatial vector and raster data processing tasks
        • 
+
      
+
      To make it easy to document, maintain and extend the package, we opted to maintain the source code, tests and documentation on Jupyter notebooks. We use nbdev to generate the python package and documentation from the notebooks. See this document to learn more about our development workflow.
      
+
      By doing this, we hope to make it easy for geospatial analysts, scientists and engineers to learn, explore and extend this package for their geospatial processing needs.
      
+
      Aside from providing reference documentation for each module, we have included extensive tutorials and use case examples in order to make it easy to learn and use.
      
+
      
+
      
+
      Modules
      
+
        
+
      • Grid Tile Generation
        • 
+
      • Geometry Validation
        • 
+
      • Vector Zonal Stats
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      • Raster Zonal Stats
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      • Area Zonal Stats
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      • Distance Zonal Stats
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      • Demographic and Health Survey (DHS) Processing Utils
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      • Geofabrik (OSM) Data Download
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      • Ookla Data Download
        • 
+
      
+
      Check this page for more details about our Roadmap.
      
+
      
+
      
+
      Installation
      
+
      pip install geowrangler
      
+
      
+
      
+
      Exploring the Documentation
      
+
      We develop the package modules alongside their documentation. Each page comes with an Open in Colab button that will open the jupyter notebook in Colab for exploration (including this page).
      
+
      Click on the Open in Colab button below to open this page as a Google Colab notebook.
      
+
      
+
      
+
      # view the source of a grid component
+gdf = gpd.GeoDataFrame()
+grid = geowrangler.grids.SquareGridGenerator(gdf, 1)
+grid??
      
+
      
+
      
+
      Type:        SquareGridGenerator
+String form: <geowrangler.grids.SquareGridGenerator object at 0x7fc284380880>
+File:        ~/work/unicef-ai4d/geowrangler-1/geowrangler/grids.py
+Source:     
+class SquareGridGenerator:
+    def __init__(
+        self,
+        cell_size: float,  # height and width of a square cell in meters
+        grid_projection: str = "EPSG:3857",  # projection of grid output
+        boundary: Union[SquareGridBoundary, List[float]] = None,  # original boundary
+    ):
+        self.cell_size = cell_size
+        self.grid_projection = grid_projection
+        self.boundary = boundary
+
      
+
      
+
      
+
      
+
      
+
      Tutorials
      
+
+
      
+
      
+
      Reference
      
+
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      All the documentation pages (including the references) are executable Jupyter notebooks.
      
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+geowrangler - Explore Poverty Mapping Machine Learning Models
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+
      Explore Poverty Mapping Machine Learning Models
      
+
      
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+
      Building poverty mapping machine learning (ML) models with Geowrangler
      
+
      
+
      
+
      The aim of this notebook is to demonstrate how Geowrangler can be used to make the process of generating ML-ready or analytics-ready datasets from raw geospatial data easier.
      
+
      Concretely, this shows a sample workflow for a wealth estimation model trained on a dataset constructed using:
      
+
        
+
      • Ground Truth - DHS household clusters + wealth indices
        • 
+
      • Features - Derived from Night Time Lights, OSM POIs, and Ookla internet speeds.
        • 
+
      
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+
      
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+
      
+Note
+
      
+
      
+
      
+
      The goal here is to showcase Geowrangler’s functions for geospatial data wrangling and feature engineering. Hence, this notebook keeps the ML modelling portions as simple as possible.
      
+
      
+
      
+
      
+
      Set-up
      
+
      Install and import some libraries.
      
+
      
+
      from pathlib import Path
+
+import folium
+import geopandas as gpd
+import matplotlib.pyplot as plt
+import pandas as pd
+import sklearn
+from shapely import wkt
+from sklearn.ensemble import RandomForestRegressor
+
+import geowrangler.area_zonal_stats as azs
+import geowrangler.distance_zonal_stats as dzs
+import geowrangler.raster_zonal_stats as rzs
+import geowrangler.vector_zonal_stats as vzs
+from geowrangler import grids
+from geowrangler.datasets import geofabrik, ookla
      
+
      
+
      
+
      
+
      Pre-requisite: Manual Data Download
      
+
      
+
      # download data if not yet available
+![ ! -e ../data/phl_adm0.geojson ] && curl -s -o ../data/phl_adm0.geojson https://raw.githubusercontent.com/thinkingmachines/geowrangler/master/data/phl_adm0.geojson
+![ ! -e ../data/phl_dhs_cluster_level.csv ] && curl -s -o ../data/phl_dhs_cluster_level.csv https://raw.githubusercontent.com/thinkingmachines/geowrangler/master/data/phl_dhs_cluster_level.csv
+![ ! -e ../data/phl_ntl.tif ] && curl -s -o ../data/phl_ntl.tif https://raw.githubusercontent.com/thinkingmachines/geowrangler/master/data/phl_ntl.tif
      
+
      
+
      
+
      DATA_DIR = Path("../data/")
+# Auto-creates the folder if it does not exist
+DATA_DIR.mkdir(parents=True, exist_ok=True)
      
+
      
+
      Your data directory should look something like this.
      
+
      
+
      
+
      
+
      data_dir.PNG
      
+
      
+
      
+
      
+
      
+
      Data Preparation
      
+
      
+
      DHS Ground Truth
      
+
      The ground truth used in this demo notebook is from a DHS 2017 on-the-ground household survey conducted in the Philippines regarding the households’ socio-demographic information.
      
+
      The file we provide is already a pre-processed version of the data that is aggregated on a household-cluster level, meaning the information cannot be tied back to any individual household. Specifically, we only provide a list of household clusters with their corresponding (jittered) GPS coordinate and DHS-computed wealth index.
      
+
      Due to the sensitive nature of the data and the DHS program terms of use, we cannot provide the raw data. You can, however, request for access to raw data yourself on the DHS website. In that case, you can use GeoWrangler’s DHS processing utils help perform the said pre-processing.
      
+
      Our first step is to create a GeoDataFrame from the data.
      
+
      
+
      # Load ground truth data as a DataFrame first
+GROUND_TRUTH_CSV = DATA_DIR / "phl_dhs_cluster_level.csv"
+df = pd.read_csv(GROUND_TRUTH_CSV)
+
+# Some of the coordinates in the data are invalid. This filters them out.
+df = df[(df.longitude > 0) & (df.latitude > 0)]
+
+# Create a GeoDataFrame from the longitude, latitude columns.
+gdf = gpd.GeoDataFrame(
+    df, geometry=gpd.points_from_xy(df.longitude, df.latitude), crs="epsg:4326"
+)
+
+print(f"There are {len(gdf):,} clusters.")
+gdf.head()
      
+
      
+
      There are 1,213 clusters.
      
+
      
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      DHSCLUSTWealth IndexDHSIDlongitudelatitudegeometry
      01-31881.60870PH201700000001122.1098076.674652POINT (122.10981 6.67465)
      12-2855.37500PH201700000002122.1320276.662256POINT (122.13203 6.66226)
      23-57647.04762PH201700000003122.1794966.621822POINT (122.17950 6.62182)
      34-54952.66667PH201700000004122.1379656.485298POINT (122.13796 6.48530)
      56-80701.69565PH201700000006121.9160946.629457POINT (121.91609 6.62946)
      
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+
      Next, we want to create a buffer around each cluster centroid that represents the area’s neighborhood. This is so we can engineer some features and characterize these neighborhoods using open data.
      
+
      This is a design decision, but for this demo, we’ll create a circlular area with a 2km radius following the random displacement introduced by DHS to preserve household privacy.
      
+
      
+
      # Make sure to convert to PH crs first for the buffer in meters, and then back to WGS84
+gdf = gdf.to_crs("epsg:3123")
+gdf.geometry = gdf.geometry.buffer(2000)
+gdf = gdf.to_crs("epsg:4326")
+
+gdf.head(5)
      
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      DHSCLUSTWealth IndexDHSIDlongitudelatitudegeometry
      01-31881.60870PH201700000001122.1098076.674652POLYGON ((122.12789 6.67461, 122.12780 6.67284...
      12-2855.37500PH201700000002122.1320276.662256POLYGON ((122.15011 6.66221, 122.15002 6.66044...
      23-57647.04762PH201700000003122.1794966.621822POLYGON ((122.19758 6.62178, 122.19749 6.62001...
      34-54952.66667PH201700000004122.1379656.485298POLYGON ((122.15604 6.48526, 122.15595 6.48349...
      56-80701.69565PH201700000006121.9160946.629457POLYGON ((121.93418 6.62942, 121.93409 6.62765...
      
+
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+
      
+
      
+
      We can visualize what we’ve done so far on a map. DHS Clusters Philippines (2017)
      
+
      
+
      # Uncomment the next line to display an interactive map
+# gdf.explore()
      
+
      
+
      
+
      
+
      Download data and load them into memory
      
+
      Next, we’ll download some data from Ookla (internet speeds) and OSM (points of interest), which we’ll use to generate neighborhood characteristics to be used as ML features.
      
+
      
+
      
+
      Ookla
      
+
      Ookla has released global open data gathered from speedtests made on their platform. This gives us access to internet speed information across various geographies. In our context, this can give us a signal predictor.
      
+
      First, let’s download a local copy of data on fixed internet in the 1st quarter of 2019 (earliest data available).
      
+
      We can use GeoWrangler’s Ookla data utility to automatically download and cache the desired data on your machine given the type, year, and quarter.
      
+
      This is just a simplification for the demo. In practice, you might want to aggregate data across multiple time periods or incorporate wireless data.
      
+
      
+
      ookla_fixed_2019_q1_filepath = ookla.download_ookla_file(
+    type_="fixed", year="2019", quarter="1"
+)
+# This is where the downloaded file is located.
+# By default this downloads to your data/ folder, but you can customize this.
+ookla_fixed_2019_q1_filepath
      
+
      
+
      PosixPath('data/2019-01-01_performance_fixed_tiles.parquet')
      
+
      
+
      
+
      
+
      # This is a function to load and do some light pre-processing on the Ookla data.
+
+
+def load_ookla_data(filename, mask=None):
+
+    # Ookla's parquet file doesn't seem to have geo metadata so need to read through pandas first
+    ookla_df = pd.read_parquet(filename)
+    ookla_gdf = gpd.GeoDataFrame(
+        ookla_df,
+        geometry=ookla_df["tile"].apply(lambda x: wkt.loads(x)),
+        crs="epsg:4326",
+    )
+    ookla_gdf.drop(columns=["tile"], inplace=True)
+
+    # Ookla's data files contain data for the whole world.
+    # For our case, we're retaining only tiles that intersect with the given mask.
+    # This is to speed-up any processing we do later on.
+    if mask is not None:
+        keep_cols = ookla_gdf.columns
+        ookla_gdf = ookla_gdf.sjoin(mask, how="inner", predicate="intersects")
+        ookla_gdf = ookla_gdf[keep_cols]
+        ookla_gdf.head()
+
+    # Convert kbps to mbps for easier reading
+    ookla_gdf["avg_d_mbps"] = ookla_gdf["avg_d_kbps"] / 1000
+    ookla_gdf["avg_u_mbps"] = ookla_gdf["avg_u_kbps"] / 1000
+
+    return ookla_gdf
      
+
      
+
      The Ookla data is quite large, and takes around 5 minutes to load.
      
+
      
+
      ookla_gdf = load_ookla_data(ookla_fixed_2019_q1_filepath, mask=gdf)
      
+
      
+
      CPU times: user 1min 49s, sys: 10.8 s, total: 2min
+Wall time: 1min 58s
      
+
      
+
      
+
      
+
      print(
+    f"{len(ookla_gdf):,} Ookla data tiles retained that intersect with our DHS cluster neighborhoods."
+)
+ookla_gdf.head()
      
+
      
+
      17,245 Ookla data tiles retained that intersect with our DHS cluster neighborhoods.
      
+
      
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      quadkeyavg_d_kbpsavg_u_kbpsavg_lat_mstestsdevicesgeometryavg_d_mbpsavg_u_mbps
      43682401323011210311031102112611POLYGON ((121.96472 20.45790, 121.97021 20.457...0.0100.021
      4368241132301121031103359311063322832POLYGON ((121.96472 20.45275, 121.97021 20.452...5.93110.633
      4368242132301121031112213063926621852POLYGON ((121.97021 20.45275, 121.97571 20.452...13.0639.266
      43682431323011210311211205113011POLYGON ((121.96472 20.44760, 121.97021 20.447...0.0200.051
      4368245132301121031123019558911411POLYGON ((121.95923 20.43731, 121.96472 20.437...0.1950.589
      
+
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+
      OSM (Open Street Maps)
      
+
      One source of open data on points of interest is OSM. We’ll use the version of the data provided by Geofabrik vs accessing the OSM Overpass API. This allows us to not be bottlenecked by API calls.
      
+
      Similar to Ookla, we can also use GeoWrangler’s OSM data download utils to download and cache OSM data on your machine. All you have to do is specify the country you want.
      
+
      Geofabrik’s OSM data is a zip file containing different files for POIs, waterways, roads, etc. For this demo notebook, we’ll only use the POIs.
      
+
      
+
      # This download can take around 1-2 minutes.
+ph_osm_zip_path = geofabrik.download_geofabrik_region("philippines")
+
+# This line unzips the zip file if we haven't done so yet.
+!echo 'None' | unzip $ph_osm_zip_path -d data/ph_osm/
      
+
      
+
      Archive:  data/philippines-latest-free.shp.zip
+  inflating: data/ph_osm/README      
+ extracting: data/ph_osm/gis_osm_buildings_a_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_buildings_a_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_buildings_a_free_1.prj  
+  inflating: data/ph_osm/gis_osm_buildings_a_free_1.shp  
+  inflating: data/ph_osm/gis_osm_buildings_a_free_1.shx  
+ extracting: data/ph_osm/gis_osm_landuse_a_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_landuse_a_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_landuse_a_free_1.prj  
+  inflating: data/ph_osm/gis_osm_landuse_a_free_1.shp  
+  inflating: data/ph_osm/gis_osm_landuse_a_free_1.shx  
+ extracting: data/ph_osm/gis_osm_natural_a_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_natural_a_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_natural_a_free_1.prj  
+  inflating: data/ph_osm/gis_osm_natural_a_free_1.shp  
+  inflating: data/ph_osm/gis_osm_natural_a_free_1.shx  
+ extracting: data/ph_osm/gis_osm_natural_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_natural_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_natural_free_1.prj  
+  inflating: data/ph_osm/gis_osm_natural_free_1.shp  
+  inflating: data/ph_osm/gis_osm_natural_free_1.shx  
+ extracting: data/ph_osm/gis_osm_places_a_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_places_a_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_places_a_free_1.prj  
+  inflating: data/ph_osm/gis_osm_places_a_free_1.shp  
+  inflating: data/ph_osm/gis_osm_places_a_free_1.shx  
+ extracting: data/ph_osm/gis_osm_places_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_places_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_places_free_1.prj  
+  inflating: data/ph_osm/gis_osm_places_free_1.shp  
+  inflating: data/ph_osm/gis_osm_places_free_1.shx  
+ extracting: data/ph_osm/gis_osm_pofw_a_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_pofw_a_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_pofw_a_free_1.prj  
+  inflating: data/ph_osm/gis_osm_pofw_a_free_1.shp  
+  inflating: data/ph_osm/gis_osm_pofw_a_free_1.shx  
+ extracting: data/ph_osm/gis_osm_pofw_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_pofw_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_pofw_free_1.prj  
+  inflating: data/ph_osm/gis_osm_pofw_free_1.shp  
+  inflating: data/ph_osm/gis_osm_pofw_free_1.shx  
+ extracting: data/ph_osm/gis_osm_pois_a_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_pois_a_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_pois_a_free_1.prj  
+  inflating: data/ph_osm/gis_osm_pois_a_free_1.shp  
+  inflating: data/ph_osm/gis_osm_pois_a_free_1.shx  
+ extracting: data/ph_osm/gis_osm_pois_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_pois_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_pois_free_1.prj  
+  inflating: data/ph_osm/gis_osm_pois_free_1.shp  
+  inflating: data/ph_osm/gis_osm_pois_free_1.shx  
+ extracting: data/ph_osm/gis_osm_railways_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_railways_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_railways_free_1.prj  
+  inflating: data/ph_osm/gis_osm_railways_free_1.shp  
+  inflating: data/ph_osm/gis_osm_railways_free_1.shx  
+ extracting: data/ph_osm/gis_osm_roads_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_roads_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_roads_free_1.prj  
+  inflating: data/ph_osm/gis_osm_roads_free_1.shp  
+  inflating: data/ph_osm/gis_osm_roads_free_1.shx  
+ extracting: data/ph_osm/gis_osm_traffic_a_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_traffic_a_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_traffic_a_free_1.prj  
+  inflating: data/ph_osm/gis_osm_traffic_a_free_1.shp  
+  inflating: data/ph_osm/gis_osm_traffic_a_free_1.shx  
+ extracting: data/ph_osm/gis_osm_traffic_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_traffic_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_traffic_free_1.prj  
+  inflating: data/ph_osm/gis_osm_traffic_free_1.shp  
+  inflating: data/ph_osm/gis_osm_traffic_free_1.shx  
+ extracting: data/ph_osm/gis_osm_transport_a_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_transport_a_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_transport_a_free_1.prj  
+  inflating: data/ph_osm/gis_osm_transport_a_free_1.shp  
+  inflating: data/ph_osm/gis_osm_transport_a_free_1.shx  
+ extracting: data/ph_osm/gis_osm_transport_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_transport_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_transport_free_1.prj  
+  inflating: data/ph_osm/gis_osm_transport_free_1.shp  
+  inflating: data/ph_osm/gis_osm_transport_free_1.shx  
+ extracting: data/ph_osm/gis_osm_water_a_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_water_a_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_water_a_free_1.prj  
+  inflating: data/ph_osm/gis_osm_water_a_free_1.shp  
+  inflating: data/ph_osm/gis_osm_water_a_free_1.shx  
+ extracting: data/ph_osm/gis_osm_waterways_free_1.cpg  
+  inflating: data/ph_osm/gis_osm_waterways_free_1.dbf  
+  inflating: data/ph_osm/gis_osm_waterways_free_1.prj  
+  inflating: data/ph_osm/gis_osm_waterways_free_1.shp  
+  inflating: data/ph_osm/gis_osm_waterways_free_1.shx  
      
+
      
+
      
+
      
+
      # Uncomment the ff line to see all the available OSM files aside from POIs.
+# !ls data/ph_osm
      
+
      
+
      
+
      ph_osm_pois_filepath = "data/ph_osm/gis_osm_pois_free_1.shp"
+ph_osm = gpd.read_file(ph_osm_pois_filepath)
      
+
      
+
      
+
      print(f"There are {len(ph_osm)} OSM POIs.")
+ph_osm.head()
      
+
      
+
      There are 133219 OSM POIs.
      
+
      
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      osm_idcodefclassnamegeometry
      0217178202907camera_surveillanceNonePOINT (121.02120 14.57608)
      1217178722722museumAyala MuseumPOINT (121.02324 14.55358)
      2240783012402motelSogo Grand HotelPOINT (121.04515 14.56449)
      3240789592907camera_surveillanceNonePOINT (121.05945 14.60098)
      4247975112542bicycle_shopChristine Sports Cycle MarketingPOINT (120.99506 14.55224)
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Feature Engineering
      
+
      Now that we’ve prepared the DHS data and loaded Ookla and OSM data into memory, we’re ready to generate some neighborhood features for our ML model.
      
+
      For convenience, we’re wrapping all the feature engineering code in functions so we can re-use them later when we apply the model to the whole country.
      
+
      Note: if you are modifying the feature engineering portions, it is recommended to run the whole section end-to-end. This ensures you’re starting fresh from a new copy of the DHS clusters GeoDataFrame. This also ensures the function definitions are up-to-date, and can be re-used properly later for model rollout.
      
+
      
+
      # We'll make a copy of the GDF to avoid overwriting the original DHS GeoDataFrame.
+gdf_with_features = gdf.copy()
      
+
      
+
      
+
      OSM
      
+
      Our goal with OSM data is to generate neighborhood characteristics based on counts and distance to certain POIs, such as schools, hospitals, etc.
      
+
      To do this, we utilize GeoWrangler’s vector zonal stats and distance zonal stats features.
      
+
      
+
      # Uncomment the ff. line if you want to see the different POI classes available
+# ph_osm.fclass.sort_values().unique()
      
+
      
+
      
+
      def generate_osm_features(aoi, osm, metric_crs="epsg:3123"):
+
+    aoi = aoi.copy()
+
+    # GeoWrangler: Count number of all POIs per tile
+    aoi = vzs.create_zonal_stats(
+        aoi,
+        osm,
+        overlap_method="intersects",
+        aggregations=[{"func": "count", "output": "poi_count", "fillna": True}],
+    )
+
+    # Count specific aoi types
+    poi_types = ["restaurant", "school", "bank", "supermarket", "mall", "atm"]
+
+    for poi_type in poi_types:
+        # GeoWrangler: Count with vector zonal stats
+        aoi = vzs.create_zonal_stats(
+            aoi,
+            osm[osm["fclass"] == poi_type],
+            overlap_method="intersects",
+            aggregations=[
+                {"func": "count", "output": f"{poi_type}_count", "fillna": True}
+            ],
+        )
+
+        # GeoWrangler: Distance with distance zonal stats
+        col_name = f"{poi_type}_nearest"
+        aoi = dzs.create_distance_zonal_stats(
+            aoi.to_crs(metric_crs),
+            osm[osm["fclass"] == poi_type].to_crs(metric_crs),
+            max_distance=10_000,
+            aggregations=[],
+            distance_col=col_name,
+        ).to_crs("epsg:4326")
+
+        # If no POI was found within the distance limit, set the distance to a really high value
+        aoi[col_name] = aoi[col_name].fillna(value=999999)
+
+    return aoi
      
+
      
+
      
+
      gdf_with_features = generate_osm_features(gdf_with_features, ph_osm)
      
+
      
+
      CPU times: user 3.04 s, sys: 0 ns, total: 3.04 s
+Wall time: 3.04 s
      
+
      
+
      
+
      
+
      gdf_with_features.head()
      
+
      
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      DHSCLUSTWealth IndexDHSIDlongitudelatitudegeometrypoi_countrestaurant_countrestaurant_nearestschool_countschool_nearestbank_countbank_nearestsupermarket_countsupermarket_nearestmall_countmall_nearestatm_countatm_nearest
      01-31881.60870PH201700000001122.1098076.674652POLYGON ((122.12789 6.67461, 122.12780 6.67284...0.00.03152.9964300.01392.5036310.01764.7369500.0999999.00.0999999.00.0999999.000000
      12-2855.37500PH201700000002122.1320276.662256POLYGON ((122.15011 6.66221, 122.15002 6.66044...5.00.05788.1659180.03601.1325021.00.0000000.0999999.00.0999999.00.0999999.000000
      23-57647.04762PH201700000003122.1794966.621822POLYGON ((122.19758 6.62178, 122.19749 6.62001...0.00.0999999.0000000.0642.8710370.04026.3003270.0999999.00.0999999.00.0999999.000000
      34-54952.66667PH201700000004122.1379656.485298POLYGON ((122.15604 6.48526, 122.15595 6.48349...0.00.0999999.0000000.02287.8399030.0999999.0000000.0999999.00.0999999.00.0999999.000000
      56-80701.69565PH201700000006121.9160946.629457POLYGON ((121.93418 6.62942, 121.93409 6.62765...0.00.08408.9259950.03930.6062570.08332.3240840.0999999.00.0999999.00.08311.332736
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      Ookla
      
+
      Our goal with Ookla data is to generate neighborhood characteristics based on internet speeds (download, upload, latency).
      
+
      To do this, we utilize GeoWrangler’s area zonal stats feature.
      
+
      
+
      def generate_ookla_features(aoi, ookla_gdf, metric_crs="epsg:3123"):
+
+    aoi = aoi.copy()
+
+    orig_aoi_crs = aoi.crs
+    aoi = aoi.to_crs(metric_crs)
+    ookla_gdf = ookla_gdf.to_crs(metric_crs)
+
+    # For better formatting, rename some columns
+    ookla_gdf = ookla_gdf.rename(
+        columns={"avg_d_mbps": "d_mbps", "avg_u_mbps": "u_mbps", "avg_lat_ms": "lat_ms"}
+    )
+
+    # GeoWrangler: Compute stats on the various columns
+    aoi = azs.create_area_zonal_stats(
+        aoi,
+        ookla_gdf,
+        aggregations=[
+            # Count number of devices in the area
+            dict(column="devices", func=["raw_sum"], fillna=True),
+            # Get stats on the download speeds
+            dict(
+                column="d_mbps",
+                func=["imputed_mean", "max", "min", "std"],
+                fillna=[True, True, True, True],
+            ),
+            # Get stats on the upload speeds
+            dict(
+                column="u_mbps",
+                func=["imputed_mean", "max", "min", "std"],
+                fillna=[True, True, True, True],
+            ),
+        ],
+        # Don't include the land area that intersected as a column
+        include_intersect=False,
+        # Don't set minimum values to 0 if the neighborhood's area doesn't fully intersect with Ookla tiles.
+        fix_min=False,
+    )
+
+    aoi = aoi.fillna(value="0")
+    aoi = aoi.to_crs(orig_aoi_crs)
+
+    return aoi
      
+
      
+
      
+
      gdf_with_features = generate_ookla_features(gdf_with_features, ookla_gdf)
      
+
      
+
      CPU times: user 3 s, sys: 0 ns, total: 3 s
+Wall time: 3 s
      
+
      
+
      
+
      
+
      gdf_with_features.head()
      
+
      
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      DHSCLUSTWealth IndexDHSIDlongitudelatitudegeometrypoi_countrestaurant_countrestaurant_nearestschool_count...atm_nearestdevices_sumd_mbps_meand_mbps_maxd_mbps_mind_mbps_stdu_mbps_meanu_mbps_maxu_mbps_minu_mbps_std
      01-31881.60870PH201700000001122.1098076.674652POLYGON ((122.12789 6.67461, 122.12780 6.67284...0.00.03152.9964300.0...999999.0000000.00.0000000.0000.0000.0000000.0000000.0000.0000.000000
      12-2855.37500PH201700000002122.1320276.662256POLYGON ((122.15011 6.66221, 122.15002 6.66044...5.00.05788.1659180.0...999999.00000038.00.40185114.6282.1774.9820530.2550316.8671.8661.753667
      23-57647.04762PH201700000003122.1794966.621822POLYGON ((122.19758 6.62178, 122.19749 6.62001...0.00.0999999.0000000.0...999999.0000000.00.0000000.0000.0000.0000000.0000000.0000.0000.000000
      34-54952.66667PH201700000004122.1379656.485298POLYGON ((122.15604 6.48526, 122.15595 6.48349...0.00.0999999.0000000.0...999999.0000000.00.0000000.0000.0000.0000000.0000000.0000.0000.000000
      56-80701.69565PH201700000006121.9160946.629457POLYGON ((121.93418 6.62942, 121.93409 6.62765...0.00.08408.9259950.0...8311.3327360.00.0000000.0000.0000.0000000.0000000.0000.0000.000000
      
+
+
      5 rows × 28 columns
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Night Time Lights
      
+
      Lastly, we’ll generate features based on night time lights. GeoWrangler provides a raster zonal stats function that acts as a thin layer on top of rasterio’s rasterstats function. It provides a little convenience in terms of formatting and automatically joins back the features to your input GeoDataFrame.
      
+
      
+
      def generate_ntl_features(aoi, raster_path):
+    aoi = aoi.copy()
+
+    aoi = rzs.create_raster_zonal_stats(
+        aoi,
+        raster_path,
+        aggregation=dict(
+            func=["mean", "min", "max", "std"],
+            column="ntl",
+        ),
+        extra_args=dict(nodata=0),
+    )
+
+    aoi.fillna(0, inplace=True)
+
+    return aoi
      
+
      
+
      
+
      gdf_with_features = generate_ntl_features(gdf_with_features, DATA_DIR / "phl_ntl.tif")
      
+
      
+
      CPU times: user 3.27 s, sys: 438 ms, total: 3.7 s
+Wall time: 3.7 s
      
+
      
+
      
+
      
+
      
+
      Visualize the final dataset
      
+
      Finally, let’s visualize our cluster neighborhoods now with the generated features. Hover over each cluster to see its data.
      
+
      
+
      
+
      
+
      DHS Cluster Features (Zoomed)
      
+
      
+
      
+
      
+
      # Visualize the clusters with the generated features
+# gdf_with_features.explore()
      
+
      
+
      
+
      
+
      
+
      ML Training
      
+
      In this section, we will now train an ML model to predict the wealth index for an area given its features.
      
+
      
+
      Prepare data into X, y and generate train/test partitions
      
+
      
+
      # For convenience, store the list of features in a variable. Remove all extraneous columns.
+feature_cols = gdf_with_features.drop(
+    columns=["DHSCLUST", "DHSID", "Wealth Index", "longitude", "latitude", "geometry"]
+).columns
+
+# Separate data (X) and the target (y)
+X = gdf_with_features[feature_cols]
+y = gdf_with_features["Wealth Index"]
+
+# Bin y into 5 buckets for stratification when splitting the dataset
+cat_y = pd.cut(y, bins=5, labels=list(range(1, 6)))
+
+# Generate train and test partitions
+X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
+    X, y, test_size=0.2, random_state=2022, stratify=cat_y
+)
      
+
      
+
      
+
      X_train.head()
      
+
      
+
      
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      poi_countrestaurant_countrestaurant_nearestschool_countschool_nearestbank_countbank_nearestsupermarket_countsupermarket_nearestmall_count...d_mbps_mind_mbps_stdu_mbps_meanu_mbps_maxu_mbps_minu_mbps_stdntl_meanntl_minntl_maxntl_std
      9441.00.04332.4458081.00.0000000.0999999.0000000.05595.3577400.0...0.0000.0000000.0000000.0000.0000.0000000.2796360.2796360.2796360.000000
      3281110.0134.00.00000022.00.00000069.00.00000012.00.0000001.0...8.9486.5193990.03349630.5642.4965.9333729.8815454.58730017.1902982.768707
      6960.00.04396.5304820.07331.1816010.0999999.0000000.0999999.0000000.0...0.0000.0000000.0000000.0000.0000.0000000.0000000.0000000.0000000.000000
      1552.00.0352.5103110.0669.1417670.02606.4783810.02566.2278710.0...1.21531.3053030.863634151.3030.10635.9985490.2995910.1824550.5191100.125637
      614272.013.00.0000005.00.00000014.00.0000008.00.0000001.0...2.9866.7227380.17473832.7740.5277.0278010.3359810.2162800.5439960.084581
      
+
+
      5 rows × 26 columns
      
+
      
+
      
+
      
+
      
+
      
+
      y_train.head()
      
+
      
+
      944    -27114.20833
+328    175912.44440
+696    -29987.93750
+155     75826.10526
+614     54849.37500
+Name: Wealth Index, dtype: float64
      
+
      
+
      
+
      
+
      
+
      Evaluation Function
      
+
      We define an evaluation function that computes R^2 and makes a plot of the actual vs predicted values.
      
+
      
+
      def evaluate(model, X_train, X_test, y_train, y_test):
+
+    # R^2
+    train_predictions = model.predict(X_train)
+    print("Train R2 score:", sklearn.metrics.r2_score(y_train, train_predictions))
+
+    test_predictions = model.predict(X_test)
+    print("Test R2 score:", sklearn.metrics.r2_score(y_test, test_predictions))
+
+    # Plot
+
+    # Train and test predictions vs actual values
+    plt.scatter(train_predictions, y_train, label="Train samples", c="#d95f02")
+    plt.scatter(test_predictions, y_test, label="Test samples", c="#7570b3")
+
+    # Identity line
+    xpoints = ypoints = plt.xlim()
+    plt.plot(
+        xpoints, ypoints, linestyle="--", color="k", lw=3, scalex=False, scaley=False
+    )
+
+    # Labels and legends
+    plt.xlabel("Predicted value")
+    plt.ylabel("True value")
+    plt.legend()
+    plt.tight_layout()
+    plt.show()
      
+
      
+
      
+
      
+
      Fit and Evaluate Model
      
+
      For ML modelling, let’s fit a simple random forest regressor.
      
+
      As mentioned, we are keeping the ML modelling as simple as possible in this notebook. This is definitely not the best-performing wealth estimation model out there, but should be decent enough for demo purposes.
      
+
      
+
      model = RandomForestRegressor()
+model.fit(X_train, y_train)
+evaluate(model, X_train, X_test, y_train, y_test)
      
+
      
+
      Train R2 score: 0.9213900086093023
+Test R2 score: 0.5921958077621785
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      ML Rollout (Phl)
      
+
      Now that we have a wealth estimation model, we can apply it to the whole country.
      
+
      
+
      Generate country-wide tiles
      
+
      First, we need to split the country up into smaller areas.
      
+
      That usually means generating grid tiles or hex tiles for your area of interest. In this notebook, we’ll generate Bing tiles at zoom level 13 (which has the closest area to our 2km neighborhoods in training).
      
+
      GeoWrangler provides various grid generation utilities for these different scenarios.
      
+
      
+
      # Read in the admin boundary for PHL.
+# This file is a simplified version of the admin boundary to make the file size smaller and load times faster.
+phl_adm = gpd.read_file(f"{DATA_DIR}/phl_adm0.geojson")
+
+# Uncomment this line if you want to visualize it
+# phl_adm.explore()
      
+
      
+
      The generation can take around 1-2 minutes.
      
+
      
+
      phl_tiles = grids.BingTileGridGenerator(13).generate_grid(phl_adm)
+print(f"There are {len(phl_tiles):,} tiles.")
+phl_tiles.head()
      
+
      
+
      There are 16,257 tiles.
+CPU times: user 27.6 s, sys: 0 ns, total: 27.6 s
+Wall time: 27.6 s
      
+
      
+
      
+
      
+
      
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+
+
+
      quadkeygeometry
      01323230031332POLYGON ((119.44336 4.56547, 119.44336 4.60928...
      11323230031320POLYGON ((119.35547 4.60928, 119.35547 4.65308...
      21323230030300POLYGON ((119.00391 4.69688, 119.00391 4.74068...
      31323230030311POLYGON ((119.13574 4.69688, 119.13574 4.74068...
      41323230031201POLYGON ((119.22363 4.69688, 119.22363 4.74068...
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      Feature Engineering
      
+
      This part is simpler, as we’re just re-using the feature engineering functions we previously created. Note though that we are generating features for a bigger area, so the runtime is a little longer (in total should be within 2 minutes).
      
+
      
+
      phl_tiles_with_features = phl_tiles.copy()
      
+
      
+
      
+
      phl_tiles_with_features = generate_osm_features(phl_tiles_with_features, ph_osm)
      
+
      
+
      CPU times: user 4.24 s, sys: 10.7 ms, total: 4.25 s
+Wall time: 4.25 s
      
+
      
+
      
+
      
+
      phl_tiles_with_features = generate_ookla_features(phl_tiles_with_features, ookla_gdf)
      
+
      
+
      CPU times: user 1.93 s, sys: 20.8 ms, total: 1.95 s
+Wall time: 1.95 s
      
+
      
+
      
+
      
+
      phl_tiles_with_features = generate_ntl_features(
+    phl_tiles_with_features, f"{DATA_DIR}/phl_ntl.tif"
+)
      
+
      
+
      CPU times: user 37.5 s, sys: 1.16 s, total: 38.6 s
+Wall time: 38.7 s
      
+
      
+
      
+
      
+
      
+
      Make predictions and viz output
      
+
      Finally, we are ready to apply the model and make predictions throughout the whole country. We can also visualize it on a Cholorpleth map.
      
+
      
+
      phl_tiles_with_features["predicted_wealth_index"] = model.predict(
+    phl_tiles_with_features[feature_cols]
+)
      
+
      
+
      
+
      def viz_chloropleth(gdf, target_feature="predicted_wealth_index"):
+
+    map = folium.Map(location=[14.6091, 121.0223], width=1000, height=800, zoom_start=7)
+    subset = gdf.copy()
+    subset["id"] = list(range(len(subset)))
+
+    folium.Choropleth(
+        geo_data=subset,
+        data=subset,
+        name="Wealth Predictions",
+        columns=["id", target_feature],
+        key_on="feature.properties.id",
+        fill_color="Spectral",
+        legend_name=target_feature,
+    ).add_to(map)
+
+    return map
      
+
      
+
      Visualize the output
      
+
      
+
      
+
      
+
      Predicted Wealth Index Rollout (Philippines)
      
+
      
+
      
+
      
+
      # Uncomment the line below to visualize rollout
+# viz_chloropleth(phl_tiles_with_features)
      
+
      
+
+
+
      
+
      
+
+
       
+
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diff --git a/overview.usecase_ookla.html b/overview.usecase_ookla.html
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+geowrangler - Exploring Internet Speeds with Ookla
+
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+
      Exploring Internet Speeds with Ookla
      
+
      
+
+
+
+
      
+
+    
+  
+    
+  
      
+  
+
+
+
      
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+
      
+
      A simple use case demo for using geowrangler modules to find the Philippine provinces/towns with the slowest/fastest internet speeds.
      
+
      
+
      
+
      
+
      Summary
      
+
      This Use Case Demo shows how to use the geowrangler.datasets.ookla and the geowrangler.area_zonal_stats modules to find the slowest/fastest internet speeds within an area or region of interest.
      
+
      
+
      How geowrangler can make this process easier
      
+
      Geowrangler can:
      
+
        
+
      • Validate your geodataframes
        • 
+
      • Help you download Ookla data (internet speed)
        • 
+
      • Generate zonal stats for your province/town
        • 
+
      
+
      
+
      
+
      What you need to do
      
+
        
+
      • Get your AOIs (areas of interest) - get the boundaries of your administrative regions
        • 
+
      • Download ookla data
        • 
+
      • Validate AOIs and ookla data
        • 
+
      • Generate zonal Stats
        • 
+
      • Analyze and find fastest/slowest internet speeds
        • 
+
      
+
      
+
      
+
      
+
      Tutorial
      
+
      
+
      Import libraries
      
+
      Lets start by importing the required libraries
      
+
      
+
      import geopandas as gpd
+import matplotlib.pyplot as plt
+import pandas as pd
+
+import geowrangler.area_zonal_stats as azs
      
+
      
+
      
+
      
+
      Download Admin Areas
      
+
      Next, we get the administrative boundaries geodataset using data from Humanitarian Data Exchange
      
+
      
+
      Set Region Filter
      
+
      
+
      
+
      
+
+
      
+
      
+Important
+
      
+
      
+
      
+
      Setting the region filter to None will compute the zonal stats for the whole Philippines and can take a lot longer than for a region.
      
+
      
+
      
+
      Set the REGION_FILTER to your Region (or None for the whole Philippines).
      
+
      
+
      # REGION_FILTER = None  # All regions
+REGION_FILTER = "National Capital Region"  # limit to 1 to speed up processing
      
+
      
+
      Download the geodataset containing the admin areas of your country.
      
+
      Here we are using the data for the Philippines.
      
+
      
+
      phl_admin3_file = "phl_adminboundaries_candidate_adm3"
+phl_admin3_zip = f"{phl_admin3_file}.zip"
+# shapefiles
+phl_admin3_link = f"https://data.humdata.org/dataset/caf116df-f984-4deb-85ca-41b349d3f313/resource/12457689-6a86-4474-8032-5ca9464d38a8/download/{phl_admin3_zip}"
      
+
      
+
      Download the zipped file. Depending on your internet connection, it can take several minutes.
      
+
      
+
      ![ ! -e ../data/{phl_admin3_zip} ] && curl -L -o ../data/{phl_admin3_zip} {phl_admin3_link}
      
+
      
+
      CPU times: user 3.9 ms, sys: 12.6 ms, total: 16.5 ms
+Wall time: 123 ms
      
+
      
+
      
+
      
+
      !mkdir -p ../data/{phl_admin3_file}
      
+
      
+
      
+
      main_file = "phl_admbnda_adm3_psa_namria_20200529"
+phl_admin3_shp = f"../data/{phl_admin3_file}/{main_file}.shp"
      
+
      
+
      
+
      ![ ! -e {phl_admin3_shp} ] && unzip -d ../data/{phl_admin3_file} ../data/{phl_admin3_zip}
      
+
      
+
      CPU times: user 5.08 ms, sys: 3.09 ms, total: 8.17 ms
+Wall time: 112 ms
      
+
      
+
      
+
      Load the admin area geo dataset.
      
+
      In our example we are loading the .shp or shape file as a geopandas dataframe.
      
+
      
+
      print(f"loading {phl_admin3_shp}")
+admin3 = gpd.read_file(phl_admin3_shp)
      
+
      
+
      loading ../data/phl_adminboundaries_candidate_adm3/phl_admbnda_adm3_psa_namria_20200529.shp
+CPU times: user 4.4 s, sys: 4.54 s, total: 8.93 s
+Wall time: 9.1 s
      
+
      
+
      
+
      
+
      admin3.head()
      
+
      
+
      
+
      
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      Shape_LengShape_AreaADM3_ENADM3_PCODEADM3_REFADM3ALT1ENADM3ALT2ENADM2_ENADM2_PCODEADM1_ENADM1_PCODEADM0_ENADM0_PCODEdatevalidOnvalidTogeometry
      01.6012190.063496AborlanPH175301000NoneNoneNonePalawanPH175300000Region IV-BPH170000000Philippines (the)PH2016-06-302020-05-29NoneMULTIPOLYGON (((118.58350 9.37700, 118.58398 9...
      11.0787490.050232Abra de IlogPH175101000NoneNoneNoneOccidental MindoroPH175100000Region IV-BPH170000000Philippines (the)PH2016-06-302020-05-29NonePOLYGON ((120.58412 13.50198, 120.58420 13.501...
      20.4243010.006453AbucayPH030801000NoneNoneNoneBataanPH030800000Region IIIPH030000000Philippines (the)PH2016-06-302020-05-29NonePOLYGON ((120.49873 14.75614, 120.49891 14.755...
      30.5660530.011343AbulugPH021501000NoneNoneNoneCagayanPH021500000Region IIPH020000000Philippines (the)PH2016-06-302020-05-29NonePOLYGON ((121.43455 18.46651, 121.43502 18.466...
      41.0136490.026124AbuyogPH083701000NoneNoneNoneLeytePH083700000Region VIIIPH080000000Philippines (the)PH2016-06-302020-05-29NoneMULTIPOLYGON (((125.02684 10.73500, 125.02683 ...
      
+
+
      
+
      
+
      
+
      
+
      
+
      list(admin3.columns.values)
      
+
      
+
      ['Shape_Leng',
+ 'Shape_Area',
+ 'ADM3_EN',
+ 'ADM3_PCODE',
+ 'ADM3_REF',
+ 'ADM3ALT1EN',
+ 'ADM3ALT2EN',
+ 'ADM2_EN',
+ 'ADM2_PCODE',
+ 'ADM1_EN',
+ 'ADM1_PCODE',
+ 'ADM0_EN',
+ 'ADM0_PCODE',
+ 'date',
+ 'validOn',
+ 'validTo',
+ 'geometry']
      
+
      
+
      
+
      
+
      admin3.ADM1_EN.unique()
      
+
      
+
      array(['Region IV-B', 'Region III', 'Region II', 'Region VIII',
+       'Region I', 'Region IV-A', 'Cordillera Administrative Region',
+       'Region VI', 'Autonomous Region in Muslim Mindanao', 'Region XII',
+       'Region VII', 'Region XIII', 'Region IX', 'Region X', 'Region V',
+       'Region XI', 'National Capital Region'], dtype=object)
      
+
      
+
      
+
      Limit the admin regions to only 1 in order to make the process run faster.
      
+
      The REGION FILTER is set in the Set Region Filter Section
      
+
      
+
      if REGION_FILTER:
+    admin3 = admin3[admin3.ADM1_EN == REGION_FILTER]
      
+
      
+
      
+
      import matplotlib.pyplot as plt
      
+
      
+
      
+
      ax = plt.axes()
+ax = admin3.plot(ax=ax, facecolor="none", edgecolor="blue")
      
+
      
+
      
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+
      
+
      
+
      
+
      Admin 3 Levels for NCR
      
+
      
+
      
+
      
+
      ax = plt.axes()
+ax = admin3[admin3.ADM3_EN == "Pateros"].plot(ax=ax, facecolor="none", edgecolor="blue")
      
+
      
+
      CPU times: user 115 ms, sys: 92.5 ms, total: 207 ms
+Wall time: 105 ms
      
+
      
+
      
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+
      admin3.crs
      
+
      
+
      <Geographic 2D CRS: EPSG:4326>
+Name: WGS 84
+Axis Info [ellipsoidal]:
+- Lat[north]: Geodetic latitude (degree)
+- Lon[east]: Geodetic longitude (degree)
+Area of Use:
+- name: World.
+- bounds: (-180.0, -90.0, 180.0, 90.0)
+Datum: World Geodetic System 1984 ensemble
+- Ellipsoid: WGS 84
+- Prime Meridian: Greenwich
      
+
      
+
      
+
      
+
      admin3.total_bounds
      
+
      
+
      array([120.90639543,  14.35172957, 121.13503641,  14.78529173])
      
+
      
+
      
+
      
+
      len(admin3)
      
+
      
+
      30
      
+
      
+
      
+
      
+
      
+
      
+
      Download Ookla data
      
+
      Use the geowrangler.datasets.ookla module to explore and download ookla (internet speed) data
      
+
      
+
      from geowrangler.datasets import ookla
      
+
      
+
      List the publically available ookla datasets
      
+
      
+
      ookla_dsets = ookla.list_ookla_files()
+ookla_dsets
      
+
      
+
      CPU times: user 76.1 ms, sys: 14.5 ms, total: 90.5 ms
+Wall time: 1.13 s
      
+
      
+
      
+
      {OoklaQuarter(type='fixed', year='2019', quarter='1'): '2019-01-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2019', quarter='2'): '2019-04-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2019', quarter='3'): '2019-07-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2019', quarter='4'): '2019-10-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2020', quarter='1'): '2020-01-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2020', quarter='2'): '2020-04-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2020', quarter='3'): '2020-07-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2020', quarter='4'): '2020-10-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2021', quarter='1'): '2021-01-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2021', quarter='2'): '2021-04-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2021', quarter='3'): '2021-07-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2021', quarter='4'): '2021-10-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2022', quarter='1'): '2022-01-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2022', quarter='2'): '2022-04-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2019', quarter='1'): '2019-01-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2019', quarter='2'): '2019-04-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2019', quarter='3'): '2019-07-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2019', quarter='4'): '2019-10-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2020', quarter='1'): '2020-01-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2020', quarter='2'): '2020-04-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2020', quarter='3'): '2020-07-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2020', quarter='4'): '2020-10-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2021', quarter='1'): '2021-01-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2021', quarter='2'): '2021-04-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2021', quarter='3'): '2021-07-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2021', quarter='4'): '2021-10-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2022', quarter='1'): '2022-01-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2022', quarter='2'): '2022-04-01_performance_mobile_tiles.parquet'}
      
+
      
+
      
+
      Download the latest data (as of the time of writing this)
      
+
      
+
      ookla_params = dict(year="2022", quarter="2", directory="../data")
      
+
      
+
      
+
      ookla_fixed = ookla.download_ookla_file(type_="fixed", **ookla_params)
      
+
      
+
      CPU times: user 143 µs, sys: 118 µs, total: 261 µs
+Wall time: 182 µs
      
+
      
+
      
+
      
+
      ookla_fixed
      
+
      
+
      PosixPath('../data/2022-04-01_performance_fixed_tiles.parquet')
      
+
      
+
      
+
      
+
      
+
      Convert ookla data into a GeoDataFrame
      
+
      The downloaded file from ookla is not yet a geodataset, so we will h have to convert it into a GeoDataFrame suitable for use in geowrangler.
      
+
      
+
      import pandas as pd
      
+
      
+
      The downloaded ookla data contains the internet speed data for the whole world and can take a minute or two to load.
      
+
      Later, we will show how to filter the data to only include the data relevant to our AOI.
      
+
      
+
      fixed = pd.read_parquet(ookla_fixed)
      
+
      
+
      CPU times: user 7.72 s, sys: 14.2 s, total: 21.9 s
+Wall time: 19.2 s
      
+
      
+
      
+
      
+
      len(fixed)
      
+
      
+
      6581735
      
+
      
+
      
+
      
+
      fixed.head()
      
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      quadkeytileavg_d_kbpsavg_u_kbpsavg_lat_mstestsdevices
      00000122200000000POLYGON((-168.75 84.1249731939109, -168.744506...97075424586571
      10022133222312322POLYGON((-160.02685546875 70.6435894914449, -1...832437989121
      20022133222330100POLYGON((-160.02685546875 70.6417687358462, -1...14972446194292
      30022133222330201POLYGON((-160.043334960938 70.6344840663086, -...625328189111
      40022202203233313POLYGON((-179.478149414062 68.9169336615194, -...700126109011
      
+
+
      
+
      
+
      
+
      
+
      
+
      fixed.dtypes
      
+
      
+
      quadkey       object
+tile          object
+avg_d_kbps     int64
+avg_u_kbps     int64
+avg_lat_ms     int64
+tests          int64
+devices        int64
+dtype: object
      
+
      
+
      
+
      The data is now a Pandas DataFrame but this needs to be converted to a GeoDataFrame by converting the tile column into a geometry with a crs (Coordinate Reference System).
      
+
      See EPSG:4326 for more details about the CRS.
      
+
      Converting the data can also take a minute or two.
      
+
      
+
      fixed["geometry"] = gpd.GeoSeries.from_wkt(fixed.tile, crs="EPSG:4326")
      
+
      
+
      CPU times: user 30.1 s, sys: 1.55 s, total: 31.6 s
+Wall time: 31.3 s
      
+
      
+
      
+
      
+
      fixed.head()
      
+
      
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      quadkeytileavg_d_kbpsavg_u_kbpsavg_lat_mstestsdevicesgeometry
      00000122200000000POLYGON((-168.75 84.1249731939109, -168.744506...97075424586571POLYGON ((-168.75000 84.12497, -168.74451 84.1...
      10022133222312322POLYGON((-160.02685546875 70.6435894914449, -1...832437989121POLYGON ((-160.02686 70.64359, -160.02136 70.6...
      20022133222330100POLYGON((-160.02685546875 70.6417687358462, -1...14972446194292POLYGON ((-160.02686 70.64177, -160.02136 70.6...
      30022133222330201POLYGON((-160.043334960938 70.6344840663086, -...625328189111POLYGON ((-160.04333 70.63448, -160.03784 70.6...
      40022202203233313POLYGON((-179.478149414062 68.9169336615194, -...700126109011POLYGON ((-179.47815 68.91693, -179.47266 68.9...
      
+
+
      
+
      
+
      
+
      
+
      
+
      fixed.drop(columns=["tile"], inplace=True)
      
+
      
+
      CPU times: user 515 ms, sys: 95.8 ms, total: 610 ms
+Wall time: 606 ms
      
+
      
+
      
+
      
+
      fixed = gpd.GeoDataFrame(fixed, geometry="geometry", crs="EPSG:4326")
      
+
      
+
      CPU times: user 71.8 ms, sys: 61.1 ms, total: 133 ms
+Wall time: 131 ms
      
+
      
+
      
+
      
+
      
+
      Validate AOI and Data Geometries
      
+
      In order to prevent more headaches as we process and analyze geospatial datasets down the line, it is prudent to check that our datasets have valid geometries.
      
+
      We can use the geowrangler.validation module to check as well fix these problems.
      
+
      
+
      from geowrangler.validation import GeometryValidation
      
+
      
+
      
+
      Validate AOI
      
+
      
+
      admin3_gvm = GeometryValidation(admin3)
      
+
      
+
      
+
      valid_admin3 = admin3_gvm.validate_all()
      
+
      
+
      CPU times: user 306 ms, sys: 1.56 ms, total: 308 ms
+Wall time: 306 ms
      
+
      
+
      
+
      
+
      valid_admin3.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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+
+
+
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+
+
+
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+
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+
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+
+
+
+
+
+
+
      Shape_LengShape_AreaADM3_ENADM3_PCODEADM3_REFADM3ALT1ENADM3ALT2ENADM2_ENADM2_PCODEADM1_ENADM1_PCODEADM0_ENADM0_PCODEdatevalidOnvalidTogeometryis_not_nullis_not_self_intersectingis_oriented_properlyis_within_crs_boundsarea_is_not_zero
      2110.0378030.000056BinondoPH133902000NoneNoneNoneNCR, City of Manila, First DistrictPH133900000National Capital RegionPH130000000Philippines (the)PH2016-06-302020-05-29NonePOLYGON ((120.97738 14.60226, 120.97765 14.602...TrueTrueFalseTrueTrue
      3080.6927580.004468Caloocan CityPH137501000NoneNoneNoneNCR, Third DistrictPH137500000National Capital RegionPH130000000Philippines (the)PH2016-06-302020-05-29NoneMULTIPOLYGON (((121.01856 14.69176, 121.01846 ...TrueTrueFalseTrueTrue
      3950.3623060.002745City of Las PiñasPH137601000City of Las PinasNoneNoneNCR, Fourth DistrictPH137600000National Capital RegionPH130000000Philippines (the)PH2016-06-302020-05-29NoneMULTIPOLYGON (((120.98122 14.48720, 120.98077 ...TrueTrueFalseTrueTrue
      3980.2603170.002094City of MakatiPH137602000NoneNoneNoneNCR, Fourth DistrictPH137600000National Capital RegionPH130000000Philippines (the)PH2016-06-302020-05-29NonePOLYGON ((121.03468 14.56725, 121.03416 14.567...TrueTrueFalseTrueTrue
      3990.2620340.001323City of MalabonPH137502000NoneNoneNoneNCR, Third DistrictPH137500000National Capital RegionPH130000000Philippines (the)PH2016-06-302020-05-29NonePOLYGON ((120.92733 14.70265, 120.92690 14.702...TrueTrueFalseTrueTrue
      
+
+
      
+
      
+
      
+
      
+
      
+
      valid_cols = [
+    "is_oriented_properly",
+    "is_not_null",
+    "is_not_self_intersecting",
+    "is_within_crs_bounds",
+    "area_is_not_zero",
+]
      
+
      
+
      
+
      [valid_admin3[col].value_counts() for col in valid_cols]
      
+
      
+
      [False    30
+ Name: is_oriented_properly, dtype: int64,
+ True    30
+ Name: is_not_null, dtype: int64,
+ True    30
+ Name: is_not_self_intersecting, dtype: int64,
+ True    30
+ Name: is_within_crs_bounds, dtype: int64,
+ True    30
+ Name: area_is_not_zero, dtype: int64]
      
+
      
+
      
+
      So the admin areas have their geometry improperly oriented (i.e. layed out in a counter clockwise direction instead of clockwise direction) and this has been fixed, but passes all the other default validations.
      
+
      
+
      valid_admin3.drop(
+    columns=valid_cols,
+    inplace=True,
+)
      
+
      
+
      
+
      
+
      Validate Data Geometries
      
+
      Before validating, let’s filter the data to only those intersecting our AOI because we don’t need to check all the data from around the world when we’re only interested in our AOI.
      
+
      Before we validate, we need to create the spatial indexes for both the AOI and data geometries. Generating the spatial indexes of the data geometries can take a minute or two due to the size of the data and their geometries.
      
+
      
+
      valid_admin3.geometry.sindex
      
+
      
+
      CPU times: user 1.12 ms, sys: 541 µs, total: 1.66 ms
+Wall time: 1.61 ms
      
+
      
+
      
+
      <geopandas.sindex.PyGEOSSTRTreeIndex at 0x7f6e434b17f0>
      
+
      
+
      
+
      
+
      fixed.geometry.sindex
      
+
      
+
      CPU times: user 6.12 s, sys: 4.01 s, total: 10.1 s
+Wall time: 10.1 s
      
+
      
+
      
+
      <geopandas.sindex.PyGEOSSTRTreeIndex at 0x7f6e434b1880>
      
+
      
+
      
+
      Lets now filter the data (filtered_fixed).
      
+
      Filtering the data using a spatial join can also take a minute or two.
      
+
      
+
      filtered_fixed = fixed.sjoin(
+    valid_admin3[["geometry"]], how="inner", predicate="intersects"
+)
      
+
      
+
      CPU times: user 1.97 s, sys: 688 ms, total: 2.66 s
+Wall time: 2.67 s
      
+
      
+
      
+
      
+
      filtered_fixed.drop(columns=["index_right"], inplace=True)
      
+
      
+
      
+
      fixed_gvm = GeometryValidation(filtered_fixed)
      
+
      
+
      
+
      valid_fixed = fixed_gvm.validate_all()
      
+
      
+
      CPU times: user 676 ms, sys: 0 ns, total: 676 ms
+Wall time: 673 ms
      
+
      
+
      
+
      
+
      [valid_fixed[col].value_counts() for col in valid_cols]
      
+
      
+
      CPU times: user 1.96 ms, sys: 961 µs, total: 2.92 ms
+Wall time: 2.28 ms
      
+
      
+
      
+
      [False    2300
+ Name: is_oriented_properly, dtype: int64,
+ True    2300
+ Name: is_not_null, dtype: int64,
+ True    2300
+ Name: is_not_self_intersecting, dtype: int64,
+ True    2300
+ Name: is_within_crs_bounds, dtype: int64,
+ True    2300
+ Name: area_is_not_zero, dtype: int64]
      
+
      
+
      
+
      Again the data geometries have an improperly oriented geometry and have been fixed by the validator
      
+
      
+
      valid_fixed.drop(columns=valid_cols, inplace=True)
+filtered_fixed = valid_fixed
      
+
      
+
      
+
      
+
      
+
      filtered fixed ookla data
      
+
      
+
      
+
      
+
      
+
      
+
      Generate Zonal Stats
      
+
      Lets now generate the zonal stats – the statistics of the data we are interested in.
      
+
      
+
      filtered_fixed.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
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+
+
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+
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+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
      quadkeyavg_d_kbpsavg_u_kbpsavg_lat_mstestsdevicesgeometry
      56460061323030313311323435654788989323POLYGON ((120.92102 14.73770, 120.92102 14.732...
      5646008132303031331133272579917171114936POLYGON ((120.92651 14.73770, 120.92651 14.732...
      564600913230303133113335257267337311POLYGON ((120.93201 14.73770, 120.93201 14.732...
      5646018132303031331311063548775381460169POLYGON ((120.92651 14.73239, 120.92651 14.727...
      564601913230303133131111059031038741311522POLYGON ((120.93201 14.73239, 120.93201 14.727...
      
+
+
      
+
      
+
      
+
      
+
      
+
      list(filtered_fixed.columns.values)
      
+
      
+
      ['quadkey',
+ 'avg_d_kbps',
+ 'avg_u_kbps',
+ 'avg_lat_ms',
+ 'tests',
+ 'devices',
+ 'geometry']
      
+
      
+
      
+
      
+
      len(filtered_fixed)
      
+
      
+
      2300
      
+
      
+
      
+
      Convert to planar CRS (since we are computing areas)
      
+
      
+
      valid_admin3 = valid_admin3.to_crs("EPSG:3857")
      
+
      
+
      CPU times: user 38.6 ms, sys: 0 ns, total: 38.6 ms
+Wall time: 37.2 ms
      
+
      
+
      
+
      
+
      filtered_fixed = filtered_fixed.to_crs("EPSG:3857")
      
+
      
+
      CPU times: user 23.3 ms, sys: 1.52 ms, total: 24.8 ms
+Wall time: 23.4 ms
      
+
      
+
      
+
      Lets save the files so we can retrieve them as necessary without having to reprocess them again.
      
+
      
+
      valid_admin3.to_file("../data/valid_admin3.geojson", driver="GeoJSON")
      
+
      
+
      CPU times: user 539 ms, sys: 37.6 ms, total: 577 ms
+Wall time: 574 ms
      
+
      
+
      
+
      
+
      filtered_fixed.to_file("../data/filtered_fixed.geojson", driver="GeoJSON")
      
+
      
+
      CPU times: user 583 ms, sys: 38.6 ms, total: 622 ms
+Wall time: 620 ms
      
+
      
+
      
+
      
+
      # %%time
+# valid_admin3 = gpd.read_file("../data/valid_admin3.geojson")
      
+
      
+
      
+
      # %%time
+# filtered_fixed = gpd.read_file("../data/filtered_fixed.geojson")
      
+
      
+
      We want the following statistics - mean (aka average), min (minimum), max (maximum) and the standard deviation (std) for the 3 data columns that ookla provides - average download speed (avg_d_kbps), average upload speed (avg_u_kbps) and average latency in milliseconds (avg_lat_ms)
      
+
      
+
      funcs = ["mean", "min", "max", "std"]
+columns = ["avg_d_kbps", "avg_u_kbps", "avg_lat_ms"]
      
+
      
+
      
+
      aggregations = [dict(func=funcs, column=c) for c in columns]
      
+
      
+
      These are the aggregations to be performed for each data column
      
+
      
+
      aggregations
      
+
      
+
      [{'func': ['mean', 'min', 'max', 'std'], 'column': 'avg_d_kbps'},
+ {'func': ['mean', 'min', 'max', 'std'], 'column': 'avg_u_kbps'},
+ {'func': ['mean', 'min', 'max', 'std'], 'column': 'avg_lat_ms'}]
      
+
      
+
      
+
      The output columns use the default {column}_{func} format if not explicitly specified.
      
+
      
+
      aoi = azs.create_area_zonal_stats(
+    valid_admin3, filtered_fixed, aggregations=aggregations
+)
      
+
      
+
      CPU times: user 3.74 s, sys: 0 ns, total: 3.74 s
+Wall time: 3.73 s
      
+
      
+
      
+
      
+
      
+
      Analyze Zonal Stats
      
+
      These are the results - the same dataframe as the original, with the addition of zonal statistics we specified as the aggregations.
      
+
      
+
      aoi.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
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+
+
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+
+
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+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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+
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+
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+
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+
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+
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+
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+
+
+
+
+
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+
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+
+
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+
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+
+
      Shape_LengShape_AreaADM3_ENADM3_PCODEADM3_REFADM3ALT1ENADM3ALT2ENADM2_ENADM2_PCODEADM1_ENADM1_PCODEADM0_ENADM0_PCODEdatevalidOnvalidTogeometryintersect_area_sumavg_d_kbps_meanavg_d_kbps_minavg_d_kbps_maxavg_d_kbps_stdavg_u_kbps_meanavg_u_kbps_minavg_u_kbps_maxavg_u_kbps_stdavg_lat_ms_meanavg_lat_ms_minavg_lat_ms_maxavg_lat_ms_std
      2110.0378030.000056BinondoPH133902000NoneNoneNoneNCR, City of Manila, First DistrictPH133900000National Capital RegionPH130000000Philippines (the)PH2016-06-302020-05-29NonePOLYGON ((13467140.279 1643403.808, 13467170.0...2.330890e+0615125.3771990.0978274141.08177412152.9162470.0809235625.7910891.2288320.080.923381
      3080.6927580.004468Caloocan CityPH137501000NoneNoneNoneNCR, Third DistrictPH137500000National Capital RegionPH130000000Philippines (the)PH2016-06-302020-05-29NoneMULTIPOLYGON (((13471724.654 1653701.565, 1347...7.425998e+07309.9565210.012323615346.775252307.7169500.011788515118.5760340.0352430.0596.147227
      3950.3623060.002745City of Las PiñasPH137601000City of Las PinasNoneNoneNCR, Fourth DistrictPH137600000National Capital RegionPH130000000Philippines (the)PH2016-06-302020-05-29NoneMULTIPOLYGON (((13467568.063 1630171.435, 1346...4.080667e+07649.6035010.017581022328.120899555.3248170.013616719944.2269710.0452980.014611.110505
      3980.2603170.002094City of MakatiPH137602000NoneNoneNoneNCR, Fourth DistrictPH137600000National Capital RegionPH130000000Philippines (the)PH2016-06-302020-05-29NonePOLYGON ((13473518.917 1639376.906, 13473461.1...3.840895e+07765.4623830.015949616771.721664663.2009950.014493917818.3613740.0914360.0255.288884
      3990.2620340.001323City of MalabonPH137502000NoneNoneNoneNCR, Third DistrictPH137500000National Capital RegionPH130000000Philippines (the)PH2016-06-302020-05-29NonePOLYGON ((13461569.031 1654955.161, 13461520.7...2.583556e+071006.8387810.011962213444.542392933.3450490.013000214309.5855890.0967790.0172.893162
      
+
+
      
+
      
+
      
+
      
+
      Lets save the results
      
+
      
+
      aoi.to_file("../data/admin3_internet_aoi.geojson", driver="GeoJSON")
      
+
      
+
      CPU times: user 415 ms, sys: 12.3 ms, total: 428 ms
+Wall time: 424 ms
      
+
      
+
      
+
      
+
      list(aoi.columns.values)
      
+
      
+
      ['Shape_Leng',
+ 'Shape_Area',
+ 'ADM3_EN',
+ 'ADM3_PCODE',
+ 'ADM3_REF',
+ 'ADM3ALT1EN',
+ 'ADM3ALT2EN',
+ 'ADM2_EN',
+ 'ADM2_PCODE',
+ 'ADM1_EN',
+ 'ADM1_PCODE',
+ 'ADM0_EN',
+ 'ADM0_PCODE',
+ 'date',
+ 'validOn',
+ 'validTo',
+ 'geometry',
+ 'intersect_area_sum',
+ 'avg_d_kbps_mean',
+ 'avg_d_kbps_min',
+ 'avg_d_kbps_max',
+ 'avg_d_kbps_std',
+ 'avg_u_kbps_mean',
+ 'avg_u_kbps_min',
+ 'avg_u_kbps_max',
+ 'avg_u_kbps_std',
+ 'avg_lat_ms_mean',
+ 'avg_lat_ms_min',
+ 'avg_lat_ms_max',
+ 'avg_lat_ms_std']
      
+
      
+
      
+
      Let’s sort the different admin level 3 areas (cities/municipalities/district) by average download speed (avg_d_kbps_mean) in descending order
      
+
      
+
      fastest_mean_download = aoi.sort_values("avg_d_kbps_mean", ascending=False)
      
+
      
+
      CPU times: user 3.72 ms, sys: 0 ns, total: 3.72 ms
+Wall time: 3.16 ms
      
+
      
+
      
+
      
+
      len(fastest_mean_download)
      
+
      
+
      30
      
+
      
+
      
+
      So according the latest ookla data (as of this writing) the district/town/province within the NCR Region with the fastest average download speed is Binondo!! :)
      
+
      
+
      fastest_mean_download.iloc[0]
      
+
      
+
      Shape_Leng                                                     0.037803
+Shape_Area                                                     0.000056
+ADM3_EN                                                         Binondo
+ADM3_PCODE                                                  PH133902000
+ADM3_REF                                                           None
+ADM3ALT1EN                                                         None
+ADM3ALT2EN                                                         None
+ADM2_EN                             NCR, City of Manila, First District
+ADM2_PCODE                                                  PH133900000
+ADM1_EN                                         National Capital Region
+ADM1_PCODE                                                  PH130000000
+ADM0_EN                                               Philippines (the)
+ADM0_PCODE                                                           PH
+date                                                         2016-06-30
+validOn                                                      2020-05-29
+validTo                                                            None
+geometry              POLYGON ((13467140.2793258 1643403.80801774, 1...
+intersect_area_sum                                       2330890.174196
+avg_d_kbps_mean                                            15125.377199
+avg_d_kbps_min                                                      0.0
+avg_d_kbps_max                                                    97827
+avg_d_kbps_std                                              4141.081774
+avg_u_kbps_mean                                            12152.916247
+avg_u_kbps_min                                                      0.0
+avg_u_kbps_max                                                    80923
+avg_u_kbps_std                                              5625.791089
+avg_lat_ms_mean                                                1.228832
+avg_lat_ms_min                                                      0.0
+avg_lat_ms_max                                                        8
+avg_lat_ms_std                                                 0.923381
+Name: 211, dtype: object
      
+
      
+
      
+
      While the district/city/municipality within the NCR Regions with the slowest average download speed is Quezon City!!!
      
+
      
+
      fastest_mean_download.iloc[-1]
      
+
      
+
      Shape_Leng                                                     0.773902
+Shape_Area                                                      0.01363
+ADM3_EN                                                     Quezon City
+ADM3_PCODE                                                  PH137404000
+ADM3_REF                                                           None
+ADM3ALT1EN                                                         None
+ADM3ALT2EN                                                         None
+ADM2_EN                                            NCR, Second District
+ADM2_PCODE                                                  PH137400000
+ADM1_EN                                         National Capital Region
+ADM1_PCODE                                                  PH130000000
+ADM0_EN                                               Philippines (the)
+ADM0_PCODE                                                           PH
+date                                                         2016-06-30
+validOn                                                      2020-05-29
+validTo                                                            None
+geometry              POLYGON ((13484683.37714301 1663482.482347114,...
+intersect_area_sum                                      174544789.29521
+avg_d_kbps_mean                                              145.111131
+avg_d_kbps_min                                                      0.0
+avg_d_kbps_max                                                   269544
+avg_d_kbps_std                                             20232.269854
+avg_u_kbps_mean                                              133.033955
+avg_u_kbps_min                                                      0.0
+avg_u_kbps_max                                                   260425
+avg_u_kbps_std                                             20141.871761
+avg_lat_ms_mean                                                0.011798
+avg_lat_ms_min                                                      0.0
+avg_lat_ms_max                                                       29
+avg_lat_ms_std                                                  2.90016
+Name: 1191, dtype: object
      
+
      
+
      
+
      The top 5 fastest areas are
      
+
      
+
      fastest_mean_download[["ADM3_EN", "ADM2_EN", "avg_d_kbps_mean"]].head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      ADM3_ENADM2_ENavg_d_kbps_mean
      211BinondoNCR, City of Manila, First District15125.377199
      1343San NicolasNCR, City of Manila, First District10897.783018
      1192QuiapoNCR, City of Manila, First District9799.121085
      1336San MiguelNCR, City of Manila, First District8708.791412
      1112PaterosNCR, Fourth District7581.975774
      
+
+
      
+
      
+
      
+
      
+
      The top 5 slowest areas are
      
+
      
+
      fastest_mean_download[["ADM3_EN", "ADM2_EN", "avg_d_kbps_mean"]].tail()
      
+
      
+
      
+
      
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      ADM3_ENADM2_ENavg_d_kbps_mean
      1521Taguig CityNCR, Fourth District531.559943
      411City of ParañaqueNCR, Fourth District470.491966
      436City of ValenzuelaNCR, Third District420.083766
      308Caloocan CityNCR, Third District309.956521
      1191Quezon CityNCR, Second District145.111131
      
+
+
      
+
      
+
      
+
      
+
      
+
      ax = plt.axes()
+ax = fastest_mean_download.plot(ax=ax, facecolor="none", edgecolor="blue")
+ax = fastest_mean_download.iloc[:1].plot(ax=ax, facecolor="red", edgecolor="red")
+ax = fastest_mean_download.iloc[29:].plot(ax=ax, facecolor="green", edgecolor="green")
+ax = fastest_mean_download.plot(ax=ax, column="avg_d_kbps_mean", alpha=0.8)
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
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+
      fastest fixed ookla by adm3
      
+
      
+
      
+
      So now, we’ve answered the question of where the fastest/slowest internet speeds are for the National Capital Region (NCR)
      
+
      
+
      
+
      Additional Exercises
      
+
      You can further experiment and try out other exercises to explore ookla data, or even try it out using a different country using a different geodataset from the Humanitarian Data Exchange site.
      
+
      The following are more exercises you can try:
      
+
        
+
      • Repeat the same process to find the fastest and slowest mobile internet speeds
        • 
+
      • Repeat the same process for a different REGION_FILTER
        • 
+
      • Aggregate by adm2 level instead of adm3
        • 
+
      
+
+
+
      
+
      
+
+
       
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+
       
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+
\ No newline at end of file
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literal 0
HcmV?d00001

diff --git a/raster_process.html b/raster_process.html
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+geowrangler - Raster Processing
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+
      Raster Processing
      
+
      
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+
      
+  
      
+    utilities for processing raster data
+  
      
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+    
+  
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+  
      
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+
      
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+
      
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      source
      
+
      
+
      query_window_by_polygon
      
+
      
+
       query_window_by_polygon
+                          (input_raster:Union[str,rasterio.io.DatasetReade
+                          r,pathlib.PosixPath], output_path:str,
+                          geometry:shapely.geometry.polygon.Polygon,
+                          mask=False)
      
+
      
+
      *Query a cloud-optimized geotiff based on polygon and save to disk Returns a subset of a rasterio dataset based on a window defined by (left,right,bottom,top) coordinates. This function assumes that the CRS of the bounds is based on the input dataset
      
+
      Args: input_raster (str, DatasetReader, PosixPath): Input rasterio dataset, which can be specified by a local filename or an open rasterio dataset output_path (str): Path to save output file geometry (polygon): polygon to crop Returns: None
      
+
      Cloud optimized geotiffs are also supported, which allows you to query a geotiff hosted in an HTTP server.*
      
+
      
+
      source
      
+
      
+
      
+
      query_window_by_gdf
      
+
      
+
       query_window_by_gdf
+                      (input_raster:Union[str,rasterio.io.DatasetReader,pa
+                      thlib.PosixPath], output_folder:str, gdf,
+                      name_col=None, mask=False)
      
+
      
+
      *Query a subset of a geotiff based on a window and save to disk Returns a subset of a rasterio dataset based on a window defined by (left,right,bottom,top) coordinates. This function assumes that the CRS of the bounds is based on the input dataset
      
+
      Cloud optimized geotiffs are also supported, which allows you to query a geotiff hosted in an HTTP server.
      
+
      Args: input_raster (str, DatasetReader, PosixPath): Input rasterio dataset, which can be specified by a local filename or an open rasterio dataset output_folder (str): Path to save output file gdf, name_col (str): column name to base output filepath on. If none, outputs will be named sequentially as output_0.tif Returns: None*
      
+
      
+
      
+
      Download raster
      
+
      
+
      input_image = "../data/phl_ppp_2020_constrained.tif"
      
+
      
+
      
+
      raster = rio.open(input_image)
+raster.meta
      
+
      
+
      {'driver': 'GTiff',
+ 'dtype': 'float32',
+ 'nodata': -99999.0,
+ 'width': 11613,
+ 'height': 19781,
+ 'count': 1,
+ 'crs': CRS.from_epsg(4326),
+ 'transform': Affine(0.0008333333299750276, 0.0, 116.927916214,
+        0.0, -0.0008333333300136493, 21.070416784)}
      
+
      
+
      
+
      
+
      show(raster.read(1), cmap="plasma", transform=raster.transform)
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Define bounding box
      
+
      
+
      bbox = (120.888062, 14.394778, 121.199112, 14.705822)
      
+
      
+
      
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      circle_gdf
      
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      latlongeometry
      014.599512120.984222POLYGON ((121.98422 14.59951, 121.97941 14.501...
      
+
+
      
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+
      
+
      
+
      print(circle_gdf.crs)
      
+
      
+
      epsg:4326
      
+
      
+
      
+
      
+
      fig, ax = plt.subplots(1, 1, figsize=(4, 8))
+show(raster.read(1), cmap="viridis", ax=ax, transform=raster.transform)
+circle_gdf.plot(ax=ax, facecolor="none", edgecolor="yellow")
+
+ax
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Crop a raster without masking
      
+
      
+
      output_folder = Path("../data")
      
+
      
+
      
+
      query_window_by_gdf(input_image, output_folder, circle_gdf, mask=False)
      
+
      
+
      ../data/output_0.tif
      
+
      
+
      
+
      
+
      with rio.open(output_folder / "output_0.tif") as dst:
+    fig, ax = plt.subplots(1, 1, figsize=(4, 4))
+
+    show(dst.read(1), cmap="viridis", ax=ax, transform=dst.transform)
+    circle_gdf.plot(facecolor="none", edgecolor="yellow", ax=ax)
+    print(dst.read(1))
+ax
      
+
      
+
      [[-99999. -99999. -99999. ... -99999. -99999. -99999.]
+ [-99999. -99999. -99999. ... -99999. -99999. -99999.]
+ [-99999. -99999. -99999. ... -99999. -99999. -99999.]
+ ...
+ [-99999. -99999. -99999. ... -99999. -99999. -99999.]
+ [-99999. -99999. -99999. ... -99999. -99999. -99999.]
+ [-99999. -99999. -99999. ... -99999. -99999. -99999.]]
      
+
      
+
      
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+
      
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+
      
+
      
+
      
+
      Crop a raster with masking
      
+
      
+
      
+
      Crop on multiple geometries at once
      
+
      
+
      grid_generator = grids.SquareGridGenerator(100_000)
      
+
      
+
      
+
      grid_gdf = grid_generator.generate_grid(circle_gdf)
+grid_gdf["name"] = (
+    "gridxy-" + grid_gdf["x"].astype(str) + "-" + grid_gdf["y"].astype(str)
+)
      
+
      
+
      
+
      grid_gdf
      
+
      
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      xygeometryname
      000POLYGON ((119.98422 13.59951, 120.88254 13.599...gridxy-0-0
      101POLYGON ((119.98422 14.47100, 120.88254 14.471...gridxy-0-1
      202POLYGON ((119.98422 15.33908, 120.88254 15.339...gridxy-0-2
      310POLYGON ((120.88254 13.59951, 121.78085 13.599...gridxy-1-0
      411POLYGON ((120.88254 14.47100, 121.78085 14.471...gridxy-1-1
      512POLYGON ((120.88254 15.33908, 121.78085 15.339...gridxy-1-2
      620POLYGON ((121.78085 13.59951, 122.67917 13.599...gridxy-2-0
      721POLYGON ((121.78085 14.47100, 122.67917 14.471...gridxy-2-1
      
+
+
      
+
      
+
      
+
      
+
      
+
      fig, ax = plt.subplots(1, 1, figsize=(4, 8))
+show(raster.read(1), cmap="viridis", ax=ax, transform=raster.transform)
+grid_gdf.plot(ax=ax, facecolor="none", edgecolor="yellow")
+
+ax
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      query_window_by_gdf(input_image, output_folder, grid_gdf, name_col="name", mask=False)
      
+
      
+
      ../data/gridxy-0-0.tif
+../data/gridxy-0-1.tif
+../data/gridxy-0-2.tif
+../data/gridxy-1-0.tif
+../data/gridxy-1-1.tif
+../data/gridxy-1-2.tif
+../data/gridxy-2-0.tif
+../data/gridxy-2-1.tif
      
+
      
+
      
+
      
+
      for name in grid_gdf["name"]:
+    image_path = output_folder / (name + ".tif")
+    with rio.open(image_path) as dst:
+        fig, ax = plt.subplots(1, 1, figsize=(4, 4))
+        ax.set_title(image_path)
+        show(dst.read(1), cmap="viridis", ax=ax, transform=dst.transform)
      
+
      
+
      
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\ No newline at end of file
diff --git a/raster_to_dataframe.html b/raster_to_dataframe.html
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index 0000000..9a018b9
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+++ b/raster_to_dataframe.html
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+geowrangler - Raster to Dataframe
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+
      
+  
      
+    
+  
+
      
+
+
      
+
+  
+
      
+
+    
+
+
      
+
+
      
+
      
+
      Raster to Dataframe
      
+
      
+
+
      
+  
      
+    load raster file as a Dataframe
+  
      
+
      
+
+
+
      
+
+    
+  
+    
+  
      
+  
+
+
+
      
+
+
+
+
      
+
      
+
      from rasterio import features
+from rasterio.plot import show
+from rasterio.windows import Window, transform
      
+
      
+
      
+
      Vector to Raster mask
      
+
      
+
      convert vectors into raster masks where each pixel is a numerical representation of a label/category
      
+
      
+
      
+
      source
      
+
      
+
      
+
      generate_mask
      
+
      
+
       generate_mask (tiff_file, shape_file, output_file, labels_column,
+                labels_dict:Dict[str,int], plot=False)
      
+
      
+
      Generates a segmentation mask for one TIFF image. Returns: image (np.array): A binary mask as a numpy array
      
+
++++++
+
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+
      





      TypeDefaultDetails
      tiff_filePath to reference TIFF file
      shape_filePath to shapefile
      output_filePath to output file
      labels_columnFeature in the shapefile that contains labels/categories
      labels_dictDictDictionary of desired labels and assigned values for the mask
      plotboolFalsePlots vector, reference TIF image, and raster mask output
      
+
      
+
      
+
      Raster to Dataframe
      
+
      
+
      Reads the bands for each image in the list and returns a dataframe where each band is one column with the image name as a suffix for column name.
      
+
      
+
      
+
      source
      
+
      
+
      
+
      read_bands
      
+
      
+
       read_bands (image_list:List[str], mask:str)
      
+
      
+
      
+
      
+
      Test data
      
+
      
+
      Generating a raster mask
      
+
      
+
      # Get filepaths
+tiff_file = "../data/vector_to_raster_mask_sample/cabanglasan.tif"
+shape_file = "../data/vector_to_raster_mask_sample/labels_20220816.gpkg"
+target_file = shape_file.replace("gpkg", "tiff")
      
+
      
+
      Given a raster image of a certain area that will be masked to use as a reference and a shape file that contains that area. Note that the shape file must include a column that contains labels/categories.
      
+
      
+
      gpd.read_file(shape_file).head(3)
      
+
      
+
      
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+
      yearlabeluidADM3_ENADM3_PCODEADM2_ENADM2_PCODEADM1_ENADM1_PCODEgeometry
      02017.0mining72_2017_miningSofronio EspañolaPH175324000PalawanPH175300000Region IV-BPH170000000MULTIPOLYGON Z (((117.95961 9.03303 0.00000, 1...
      12017.0mining71_2017_miningSofronio EspañolaPH175324000PalawanPH175300000Region IV-BPH170000000MULTIPOLYGON Z (((117.95507 9.03809 0.00000, 1...
      22017.0mining70_2017_miningSofronio EspañolaPH175324000PalawanPH175300000Region IV-BPH170000000MULTIPOLYGON Z (((117.95663 9.03869 0.00000, 1...
      
+
+
      
+
      
+
      
+
      
+
      And a dictionary with your desired labels and assigned values in creating a mask
      
+
      
+
      labels = {
+    "mining": 1,
+    "neg": 2,
+    "agriculture": 3,
+    "product_extraction": 4,
+    "kaingin": 5,
+    "biophysical": 6,
+}
      
+
      
+
      Input them in the generate_mask function to create a raster mask of the same dimension as the reference raster image
      
+
      
+
      # Generate masks for a file
+masks, grids, values = generate_mask(
+    tiff_file=tiff_file,
+    shape_file=shape_file,
+    output_file=target_file,
+    labels_column="label",
+    labels_dict=labels,
+    plot=True,
+)
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      masks
      
+
      
+
      array([[0, 0, 0, ..., 0, 0, 0],
+       [0, 0, 0, ..., 0, 0, 0],
+       [0, 0, 0, ..., 0, 0, 0],
+       ...,
+       [0, 0, 0, ..., 0, 0, 0],
+       [0, 0, 0, ..., 0, 0, 0],
+       [0, 0, 0, ..., 0, 0, 0]], dtype=uint16)
      
+
      
+
      
+
      
+
      grids
      
+
      
+
      array([[0, 0, 0, ..., 0, 0, 0],
+       [0, 0, 0, ..., 0, 0, 0],
+       [0, 0, 0, ..., 0, 0, 0],
+       ...,
+       [0, 0, 0, ..., 0, 0, 0],
+       [0, 0, 0, ..., 0, 0, 0],
+       [0, 0, 0, ..., 0, 0, 0]], dtype=uint16)
      
+
      
+
      
+
      
+
      values
      
+
      
+
      {'mining': 1,
+ 'neg': 2,
+ 'agriculture': 3,
+ 'product_extraction': 4,
+ 'kaingin': 5,
+ 'biophysical': 6}
      
+
      
+
      
+
      Choose raster images to convert into a dataframe and use the generated raster mask to add the labels the converted dataframe.
      
+
      
+
      tiff_files = ["../data/vector_to_raster_mask_sample/cabanglasan.tif"]
+mask_file = "../data/vector_to_raster_mask_sample/labels_20220816.tiff"
      
+
      
+
      
+
      data = read_bands(tiff_files, mask_file)
      
+
      
+
      
+
      data
      
+
      
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+
      B1_0B2_0B3_0B4_0B5_0B6_0B7_0B8_0B9_0B10_0B11_0B12_0label
      00.11980.096350.093300.06980.106650.202500.24900.235250.281250.03770.199250.10020
      10.11980.095800.092450.07080.106650.202500.24900.239250.281250.03770.199250.10020
      20.11480.094200.094600.07070.103800.203950.24780.231500.271650.03850.182400.09020
      30.11480.091900.088500.06310.103800.203950.24780.233000.271650.03850.182400.09020
      40.11480.093500.090800.06430.105650.208300.24660.242050.269900.03850.180500.08940
      ..........................................
      7758240.00000.000000.000000.00000.000000.000000.00000.000000.000000.00000.000000.00000
      7758250.00000.000000.000000.00000.000000.000000.00000.000000.000000.00000.000000.00000
      7758260.00000.000000.000000.00000.000000.000000.00000.000000.000000.00000.000000.00000
      7758270.00000.000000.000000.00000.000000.000000.00000.000000.000000.00000.000000.00000
      7758280.00000.000000.000000.00000.000000.000000.00000.000000.000000.00000.000000.00000
      
+
+
      775829 rows × 13 columns
      
+
      
+
      
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+
      
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+
       
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+
       
+
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+
\ No newline at end of file
diff --git a/raster_zonal_stats.html b/raster_zonal_stats.html
new file mode 100644
index 0000000..1f4bd86
--- /dev/null
+++ b/raster_zonal_stats.html
@@ -0,0 +1,1135 @@
+
+
+
+
+
+
+
+
+
+
+geowrangler - Raster Zonal Stats
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
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+
      
+  
      
+    
+  
+
      
+
+
      
+
+  
+
      
+
+    
+
+
      
+
+
      
+
      
+
      Raster Zonal Stats
      
+
      
+
+
      
+  
      
+    generating zonal stats from raster data
+  
      
+
      
+
+
+
      
+
+    
+  
+    
+  
      
+  
+
+
+
      
+
+
+
+
      
+
      Note: This module is a thin layer on top of the rasterstats package to make its interface more compatible with the other geowrangler modules (e.g. vector zonal stats)
      
+
      
+
      source
      
+
      
+
      create_raster_zonal_stats
      
+
      
+
       create_raster_zonal_stats
+                            (aoi:Union[str,geopandas.geodataframe.GeoDataF
+                            rame], data:Union[str,pathlib.Path],
+                            aggregation:Dict[str,Any],
+                            extra_args:Dict[str,Any]={'layer': 0,
+                            'band_num': 1, 'nodata': None, 'affine': None,
+                            'all_touched': False})
      
+
      
+
      Compute zonal stats with a vector areas of interest (aoi) from raster data sources. This is a thin layer over the zonal_stats method from the rasterstats python package for compatibility with other geowrangler modules. This method currently only supports 1 band for each call, so if you want to create zonal stats for multiple bands with the same raster data, you can call this method for each band (make sure to specify the correct band_num in the extra_args parameter). See https://pythonhosted.org/rasterstats/manual.html#zonal-statistics for more details
      
+
+
+
+
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+
+
+
      TypeDefaultDetails
      aoiUnionThe area of interest geodataframe, or path to the vector file
      dataUnionThe path to the raster data file
      aggregationDictA dict specifying the aggregation. See create_zonal_stats from the geowrangler.vector_zonal_stats module for more details
      extra_argsDict{‘layer’: 0, ‘band_num’: 1, ‘nodata’: None, ‘affine’: None, ‘all_touched’: False}Extra arguments passed to rasterstats.zonal_stats method
      ReturnsGeoDataFrame
      
+
      
+
      def make_simple_aoi():
+    df = pd.DataFrame(
+        data={
+            "col1": [1, 2, 3],
+            "lat0": [0.0, 1.0, 2.0],
+            "lon0": [0.0, 0.0, 0.0],
+            "lat1": [0.0, 1.0, 2.0],
+            "lon1": [1.0, 1.0, 1.0],
+            "lat2": [1.0, 2.0, 3.0],
+            "lon2": [1.0, 1.0, 1.0],
+            "lat3": [1.0, 2.0, 3.0],
+            "lon3": [0.0, 0.0, 0.0],
+        }
+    )
+
+    def square(row):
+        return Polygon(
+            (
+                [
+                    (row.lat0, row.lon0),
+                    (row.lat1, row.lon1),
+                    (row.lat2, row.lon2),
+                    (row.lat3, row.lon3),
+                ]
+            )
+        )
+
+    return gpd.GeoDataFrame(df, geometry=df.apply(square, axis=1), crs="EPSG:4326")
+
+
+simple_aoi = make_simple_aoi()
+terrain_file = "../data/sample_terrain.tif"
      
+
      
+
      
+
      import matplotlib.pyplot as plt
+import numpy as np
+import rasterio
      
+
      
+
      
+
      with rasterio.open(terrain_file) as src:
+    data = src.read(1)
+    data_crs = src.crs
+    data_bounds = src.bounds
+print(data.shape, data_crs, data_bounds)
      
+
      
+
      (707, 707) EPSG:3857 BoundingBox(left=-20.0, bottom=-19.994, right=19.994, top=20.0)
      
+
      
+
      
+
      
+
      simple_aoi.total_bounds
      
+
      
+
      array([0., 0., 3., 1.])
      
+
      
+
      
+
      
+
      ax = plt.imshow(data, cmap="pink")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      ax = simple_aoi.plot(facecolor="none", edgecolor="blue")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      results = create_raster_zonal_stats(
+    simple_aoi,
+    terrain_file,
+    aggregation=dict(func=["mean", "max", "min", "std"], column="elevation"),
+    extra_args=dict(nodata=np.nan),
+)
      
+
      
+
      CPU times: user 18.5 ms, sys: 39.4 ms, total: 58 ms
+Wall time: 19 ms
      
+
      
+
      
+
      
+
      results
      
+
      
+
      
+
      
+
+
+
+
+
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+
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+
      col1lat0lon0lat1lon1lat2lon2lat3lon3geometryelevation_minelevation_maxelevation_meanelevation_std
      010.00.00.01.01.01.01.00.0POLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....1238.7341611444.7222131339.12672664.362218
      121.00.01.01.02.01.02.00.0POLYGON ((1.00000 0.00000, 1.00000 1.00000, 2....1222.4091021425.9208521311.90399751.286449
      232.00.02.01.03.01.03.00.0POLYGON ((2.00000 0.00000, 2.00000 1.00000, 3....1231.5697711402.8596281319.62486845.298326
      
+
+
      
+
      
+
      
+
      
+
      Check that create_raster_zonal_stats uses the nodata attribute set in the tif file if extra_args.nodata is set to None
      
+
      
+
      # Load in admin bounds
+phl_adm = gpd.read_file("../data/region3_admin.geojson")
      
+
      
+
      If the nodata parameter is explicitly set to -999999, the Population count should be > 0.
      
+
      
+
      grid_aoi_results = create_raster_zonal_stats(
+    phl_adm,
+    "../data/phl_ppp_2020_constrained.tif",
+    aggregation=dict(
+        func=["sum"],
+        column="population",
+        output=["population_count"],
+        fillna=[True],
+    ),
+    extra_args=dict(nodata=-99999),
+)
      
+
      
+
      
+
      grid_aoi_results
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      Reg_CodeReg_NameReg_Alt_Namegeometrypopulation_count
      0030000000Region IIICentral LuzonMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...10983338.0
      
+
+
      
+
      
+
      
+
      
+
      If the nodata parameter is not set, create_raster_zonal_stats should use the nodata attribute set by the geotiff file so that population count should still be > 0.
      
+
      
+
      grid_aoi_results = create_raster_zonal_stats(
+    phl_adm,
+    "../data/phl_ppp_2020_constrained.tif",
+    aggregation=dict(
+        func=["sum"],
+        column="population",
+        output=["population_count"],
+        fillna=[True],
+    ),
+)
      
+
      
+
      
+
      grid_aoi_results
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      Reg_CodeReg_NameReg_Alt_Namegeometrypopulation_count
      0030000000Region IIICentral LuzonMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...10983338.0
      
+
+
      
+
      
+
      
+
      
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+
      
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+
       
+
+
       
+
+
+
+
+
\ No newline at end of file
diff --git a/readme.html b/readme.html
new file mode 100644
index 0000000..ca97a1e
--- /dev/null
+++ b/readme.html
@@ -0,0 +1,916 @@
+
+
+
+
+
+
+
+
+
+
+geowrangler - Geowrangler
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
      
+  
      
+    
+  
+
      
+
+
      
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+  
+
      
+
+    
+
+
      
+
+
      
+
      
+
      Geowrangler
      
+
      
+
+
      
+  
      
+    Tools for wrangling with geospatial data
+  
      
+
      
+
+
+
      
+
+    
+  
+    
+  
      
+  
+
+
+
      
+
+
+
+
      Geowrangler logo
      
+
      
+
      Overview
      
+
      Geowrangler is a python package for geodata wrangling. It helps you build data transformation workflows that have no out-of-the-box solutions from other geospatial libraries.
      
+
      We surveyed our past geospatial projects to extract these solutions for our work and hope that these will be useful for others as well.
      
+
      Our audience are researchers, analysts and engineers delivering geospatial projects.
      
+
      We welcome your comments, suggestions, bug reports and code contributions to make Geowrangler better.
      
+
      
+
      
+
      Context
      
+
      Geowrangler was borne out of our efforts to reduce the amount of boilerplate code in wrangling geospatial data. It builds on top of existing geospatial libraries such as geopandas, rasterio, rasterstats, morecantile and others. Our goals are centered on the following tasks:
      
+
        
+
      • Extracting area of interest zonal statistics from vector and raster data
        • 
+
      • Gridding areas of interest
        • 
+
      • Validating geospatial datasets
        • 
+
      • Downloading of publically available geospatial datasets (e.g. OSM, Ookla, Nightlights)
        • 
+
      • Other geospatial vector and raster data processing tasks
        • 
+
      
+
      To make it easy to document, maintain and extend the package, we opted to maintain the source code, tests and documentation on Jupyter notebooks. We use nbdev to generate the python package and documentation from the notebooks. See this document to learn more about our development workflow.
      
+
      By doing this, we hope to make it easy for geospatial analysts, scientists and engineers to learn, explore and extend this package for their geospatial processing needs.
      
+
      Aside from providing reference documentation for each module, we have included extensive tutorials and use case examples in order to make it easy to learn and use.
      
+
      
+
      
+
      Modules
      
+
        
+
      • Grid Tile Generation
        • 
+
      • Geometry Validation
        • 
+
      • Vector Zonal Stats
        • 
+
      • Raster Zonal Stats
        • 
+
      • Area Zonal Stats
        • 
+
      • Distance Zonal Stats
        • 
+
      • Demographic and Health Survey (DHS) Processing Utils
        • 
+
      • Geofabrik (OSM) Data Download
        • 
+
      • Ookla Data Download
        • 
+
      
+
      Check this page for more details about our Roadmap.
      
+
      
+
      
+
      Installation
      
+
      pip install geowrangler
      
+
      
+
      
+
      Exploring the Documentation
      
+
      We develop the package modules alongside their documentation. Each page comes with an Open in Colab button that will open the jupyter notebook in Colab for exploration (including this page).
      
+
      Click on the Open in Colab button below to open this page as a Google Colab notebook.
      
+
      
+
      
+
      # view the source of a grid component
+gdf = gpd.GeoDataFrame()
+grid = geowrangler.grids.SquareGridGenerator(gdf, 1)
+grid??
      
+
      
+
      
+
      Type:        SquareGridGenerator
+String form: <geowrangler.grids.SquareGridGenerator object at 0x7fc284380880>
+File:        ~/work/unicef-ai4d/geowrangler-1/geowrangler/grids.py
+Source:     
+class SquareGridGenerator:
+    def __init__(
+        self,
+        cell_size: float,  # height and width of a square cell in meters
+        grid_projection: str = "EPSG:3857",  # projection of grid output
+        boundary: Union[SquareGridBoundary, List[float]] = None,  # original boundary
+    ):
+        self.cell_size = cell_size
+        self.grid_projection = grid_projection
+        self.boundary = boundary
+
      
+
      
+
      
+
      
+
      
+
      Tutorials
      
+
+
      
+
      
+
      Reference
      
+
+
      
+
      
+
      
+
+
      
+
      
+Note
+
      
+
      
+
      
+
      All the documentation pages (including the references) are executable Jupyter notebooks.
      
+
      
+
      
+
+
+
      
+
      
+
+
       
+
+
       
+
+
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+
\ No newline at end of file
diff --git a/robots.txt b/robots.txt
new file mode 100644
index 0000000..6bba148
--- /dev/null
+++ b/robots.txt
@@ -0,0 +1 @@
+Sitemap: https://geowrangler.thinkingmachin.es/sitemap.xml
diff --git a/search.json b/search.json
new file mode 100644
index 0000000..13bc9ac
--- /dev/null
+++ b/search.json
@@ -0,0 +1,862 @@
+[
+  {
+    "objectID": "datasets_nightlights.html",
+    "href": "datasets_nightlights.html",
+    "title": "Night Lights",
+    "section": "",
+    "text": "source\n\nget_eog_access_token\n\n get_eog_access_token (username, password, save_token=False,\n                       save_path='~/.eog_creds/eog_access_token',\n                       set_env=True, env_token_var='EOG_ACCESS_TOKEN')\n\n\nsource\n\n\nclear_eog_access_token\n\n clear_eog_access_token (save_file='~/.eog_creds/eog_access_token',\n                         env_var='EOG_ACCESS_TOKEN', clear_file=True,\n                         clear_env=True)\n\n\nsource\n\n\nsetup_eog_auth_headers\n\n setup_eog_auth_headers (headers, access_token, env_var, creds_file)\n\n\nsource\n\n\ndownload_url\n\n download_url (url, dest=None, access_token=None, headers=None,\n               timeout=None, show_progress=True, chunksize=1048576,\n               env_var='EOG_ACCESS_TOKEN',\n               creds_file='~/.eog_creds/eog_access_token')\n\nDownload url to dest and show progress\n\nsource\n\n\nunzip_eog_gzip\n\n unzip_eog_gzip (gz_file, dest=None, delete_src=False)\n\n\nsource\n\n\nclip_raster\n\n clip_raster (input_raster_file, dest, bounds, buffer=None)\n\n\nsource\n\n\nmake_url\n\n make_url (year, viirs_data_type='average',\n           ntlights_base_url='https://eogdata.mines.edu/nighttime_light',\n           version='v21', product='annual', coverage='global',\n           process_suffix='c202205302300', vcmcfg='vcmslcfg')\n\n\nsource\n\n\nmake_clip_hash\n\n make_clip_hash (year, bounds, viirs_data_type='average', version='v21',\n                 product='annual', coverage='global',\n                 process_suffix='c202205302300', vcmcfg='vcmslcfg')\n\n\nsource\n\n\ngenerate_clipped_raster\n\n generate_clipped_raster (year, bounds, dest, viirs_data_type='average',\n                          version='v21', product='annual',\n                          coverage='global',\n                          cache_dir='~/.cache/geowrangler/nightlights',\n                          process_suffix='c202205302300',\n                          vcmcfg='vcmslcfg')\n\n\nsource\n\n\ngenerate_clipped_metadata\n\n generate_clipped_metadata (year, bounds, viirs_data_type, version,\n                            product, coverage, clip_cache_dir,\n                            process_suffix, vcmcfg)\n\n\nsource\n\n\nget_clipped_raster\n\n get_clipped_raster (year, bounds, viirs_data_type='average',\n                     version='v21', product='annual', coverage='global',\n                     cache_dir='~/.cache/geowrangler/nightlights',\n                     process_suffix='c202205302300', vcmcfg='vcmslcfg')",
+    "crumbs": [
+      "Night Lights"
+    ]
+  },
+  {
+    "objectID": "raster_zonal_stats.html",
+    "href": "raster_zonal_stats.html",
+    "title": "Raster Zonal Stats",
+    "section": "",
+    "text": "Note: This module is a thin layer on top of the rasterstats package to make its interface more compatible with the other geowrangler modules (e.g. vector zonal stats)\n\nsource\n\ncreate_raster_zonal_stats\n\n create_raster_zonal_stats\n                            (aoi:Union[str,geopandas.geodataframe.GeoDataF\n                            rame], data:Union[str,pathlib.Path],\n                            aggregation:Dict[str,Any],\n                            extra_args:Dict[str,Any]={'layer': 0,\n                            'band_num': 1, 'nodata': None, 'affine': None,\n                            'all_touched': False})\n\nCompute zonal stats with a vector areas of interest (aoi) from raster data sources. This is a thin layer over the zonal_stats method from the rasterstats python package for compatibility with other geowrangler modules. This method currently only supports 1 band for each call, so if you want to create zonal stats for multiple bands with the same raster data, you can call this method for each band (make sure to specify the correct band_num in the extra_args parameter). See https://pythonhosted.org/rasterstats/manual.html#zonal-statistics for more details\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\naoi\nUnion\n\nThe area of interest geodataframe, or path to the vector file\n\n\ndata\nUnion\n\nThe path to the raster data file\n\n\naggregation\nDict\n\nA dict specifying the aggregation. See create_zonal_stats from the geowrangler.vector_zonal_stats module for more details\n\n\nextra_args\nDict\n{‘layer’: 0, ‘band_num’: 1, ‘nodata’: None, ‘affine’: None, ‘all_touched’: False}\nExtra arguments passed to rasterstats.zonal_stats method\n\n\nReturns\nGeoDataFrame\n\n\n\n\n\n\ndef make_simple_aoi():\n    df = pd.DataFrame(\n        data={\n            \"col1\": [1, 2, 3],\n            \"lat0\": [0.0, 1.0, 2.0],\n            \"lon0\": [0.0, 0.0, 0.0],\n            \"lat1\": [0.0, 1.0, 2.0],\n            \"lon1\": [1.0, 1.0, 1.0],\n            \"lat2\": [1.0, 2.0, 3.0],\n            \"lon2\": [1.0, 1.0, 1.0],\n            \"lat3\": [1.0, 2.0, 3.0],\n            \"lon3\": [0.0, 0.0, 0.0],\n        }\n    )\n\n    def square(row):\n        return Polygon(\n            (\n                [\n                    (row.lat0, row.lon0),\n                    (row.lat1, row.lon1),\n                    (row.lat2, row.lon2),\n                    (row.lat3, row.lon3),\n                ]\n            )\n        )\n\n    return gpd.GeoDataFrame(df, geometry=df.apply(square, axis=1), crs=\"EPSG:4326\")\n\n\nsimple_aoi = make_simple_aoi()\nterrain_file = \"../data/sample_terrain.tif\"\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport rasterio\n\n\nwith rasterio.open(terrain_file) as src:\n    data = src.read(1)\n    data_crs = src.crs\n    data_bounds = src.bounds\nprint(data.shape, data_crs, data_bounds)\n\n(707, 707) EPSG:3857 BoundingBox(left=-20.0, bottom=-19.994, right=19.994, top=20.0)\n\n\n\nsimple_aoi.total_bounds\n\narray([0., 0., 3., 1.])\n\n\n\nax = plt.imshow(data, cmap=\"pink\")\n\n\n\n\n\n\n\n\n\nax = simple_aoi.plot(facecolor=\"none\", edgecolor=\"blue\")\n\n\n\n\n\n\n\n\n\nresults = create_raster_zonal_stats(\n    simple_aoi,\n    terrain_file,\n    aggregation=dict(func=[\"mean\", \"max\", \"min\", \"std\"], column=\"elevation\"),\n    extra_args=dict(nodata=np.nan),\n)\n\nCPU times: user 18.5 ms, sys: 39.4 ms, total: 58 ms\nWall time: 19 ms\n\n\n\nresults\n\n\n\n\n\n\n\n\n\ncol1\nlat0\nlon0\nlat1\nlon1\nlat2\nlon2\nlat3\nlon3\ngeometry\nelevation_min\nelevation_max\nelevation_mean\nelevation_std\n\n\n\n\n0\n1\n0.0\n0.0\n0.0\n1.0\n1.0\n1.0\n1.0\n0.0\nPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....\n1238.734161\n1444.722213\n1339.126726\n64.362218\n\n\n1\n2\n1.0\n0.0\n1.0\n1.0\n2.0\n1.0\n2.0\n0.0\nPOLYGON ((1.00000 0.00000, 1.00000 1.00000, 2....\n1222.409102\n1425.920852\n1311.903997\n51.286449\n\n\n2\n3\n2.0\n0.0\n2.0\n1.0\n3.0\n1.0\n3.0\n0.0\nPOLYGON ((2.00000 0.00000, 2.00000 1.00000, 3....\n1231.569771\n1402.859628\n1319.624868\n45.298326\n\n\n\n\n\n\n\n\nCheck that create_raster_zonal_stats uses the nodata attribute set in the tif file if extra_args.nodata is set to None\n\n# Load in admin bounds\nphl_adm = gpd.read_file(\"../data/region3_admin.geojson\")\n\nIf the nodata parameter is explicitly set to -999999, the Population count should be > 0.\n\ngrid_aoi_results = create_raster_zonal_stats(\n    phl_adm,\n    \"../data/phl_ppp_2020_constrained.tif\",\n    aggregation=dict(\n        func=[\"sum\"],\n        column=\"population\",\n        output=[\"population_count\"],\n        fillna=[True],\n    ),\n    extra_args=dict(nodata=-99999),\n)\n\n\ngrid_aoi_results\n\n\n\n\n\n\n\n\n\nReg_Code\nReg_Name\nReg_Alt_Name\ngeometry\npopulation_count\n\n\n\n\n0\n030000000\nRegion III\nCentral Luzon\nMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...\n10983338.0\n\n\n\n\n\n\n\n\nIf the nodata parameter is not set, create_raster_zonal_stats should use the nodata attribute set by the geotiff file so that population count should still be > 0.\n\ngrid_aoi_results = create_raster_zonal_stats(\n    phl_adm,\n    \"../data/phl_ppp_2020_constrained.tif\",\n    aggregation=dict(\n        func=[\"sum\"],\n        column=\"population\",\n        output=[\"population_count\"],\n        fillna=[True],\n    ),\n)\n\n\ngrid_aoi_results\n\n\n\n\n\n\n\n\n\nReg_Code\nReg_Name\nReg_Alt_Name\ngeometry\npopulation_count\n\n\n\n\n0\n030000000\nRegion III\nCentral Luzon\nMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...\n10983338.0",
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+      "Raster Zonal Stats"
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+  },
+  {
+    "objectID": "readme.html",
+    "href": "readme.html",
+    "title": "Geowrangler",
+    "section": "",
+    "text": "Overview\nGeowrangler is a python package for geodata wrangling. It helps you build data transformation workflows that have no out-of-the-box solutions from other geospatial libraries.\nWe surveyed our past geospatial projects to extract these solutions for our work and hope that these will be useful for others as well.\nOur audience are researchers, analysts and engineers delivering geospatial projects.\nWe welcome your comments, suggestions, bug reports and code contributions to make Geowrangler better.\n\n\nContext\nGeowrangler was borne out of our efforts to reduce the amount of boilerplate code in wrangling geospatial data. It builds on top of existing geospatial libraries such as geopandas, rasterio, rasterstats, morecantile and others. Our goals are centered on the following tasks:\n\nExtracting area of interest zonal statistics from vector and raster data\nGridding areas of interest\nValidating geospatial datasets\nDownloading of publically available geospatial datasets (e.g. OSM, Ookla, Nightlights)\nOther geospatial vector and raster data processing tasks\n\nTo make it easy to document, maintain and extend the package, we opted to maintain the source code, tests and documentation on Jupyter notebooks. We use nbdev to generate the python package and documentation from the notebooks. See this document to learn more about our development workflow.\nBy doing this, we hope to make it easy for geospatial analysts, scientists and engineers to learn, explore and extend this package for their geospatial processing needs.\nAside from providing reference documentation for each module, we have included extensive tutorials and use case examples in order to make it easy to learn and use.\n\n\nModules\n\nGrid Tile Generation\nGeometry Validation\nVector Zonal Stats\nRaster Zonal Stats\nArea Zonal Stats\nDistance Zonal Stats\nDemographic and Health Survey (DHS) Processing Utils\nGeofabrik (OSM) Data Download\nOokla Data Download\n\nCheck this page for more details about our Roadmap.\n\n\nInstallation\npip install geowrangler\n\n\nExploring the Documentation\nWe develop the package modules alongside their documentation. Each page comes with an Open in Colab button that will open the jupyter notebook in Colab for exploration (including this page).\nClick on the Open in Colab button below to open this page as a Google Colab notebook.\n\n\n# view the source of a grid component\ngdf = gpd.GeoDataFrame()\ngrid = geowrangler.grids.SquareGridGenerator(gdf, 1)\ngrid??\n\n\nType:        SquareGridGenerator\nString form: <geowrangler.grids.SquareGridGenerator object at 0x7fc284380880>\nFile:        ~/work/unicef-ai4d/geowrangler-1/geowrangler/grids.py\nSource:     \nclass SquareGridGenerator:\n    def __init__(\n        self,\n        cell_size: float,  # height and width of a square cell in meters\n        grid_projection: str = \"EPSG:3857\",  # projection of grid output\n        boundary: Union[SquareGridBoundary, List[float]] = None,  # original boundary\n    ):\n        self.cell_size = cell_size\n        self.grid_projection = grid_projection\n        self.boundary = boundary\n\n\n\n\n\nTutorials\n\nGrids Generation\nGeometry Validation\nVector Zonal Stats\nRaster Zonal Stats\nArea Zonal Stats\nDistance Zonal Stats\nDHS Processing Utils\nDataset Downloads\n\n\n\nReference\n\nGrids Generation\nGeometry Validation\nVector Zonal Stats\nRaster Zonal Stats\nArea Zonal Stats\nDistance Zonal Stats\nDHS Processing Utils\nDataset Geofabrik (OSM)\nDataset Ookla\n\n\n\n\n\n\n\nNote\n\n\n\nAll the documentation pages (including the references) are executable Jupyter notebooks.",
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+      "Geowrangler"
+    ]
+  },
+  {
+    "objectID": "overview.poverty_mapping_demo.html",
+    "href": "overview.poverty_mapping_demo.html",
+    "title": "Explore Poverty Mapping Machine Learning Models",
+    "section": "",
+    "text": "Building poverty mapping machine learning (ML) models with Geowrangler\nThe aim of this notebook is to demonstrate how Geowrangler can be used to make the process of generating ML-ready or analytics-ready datasets from raw geospatial data easier.\nConcretely, this shows a sample workflow for a wealth estimation model trained on a dataset constructed using:",
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+      "Explore Poverty Mapping Machine Learning Models"
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+    "objectID": "overview.poverty_mapping_demo.html#set-up",
+    "href": "overview.poverty_mapping_demo.html#set-up",
+    "title": "Explore Poverty Mapping Machine Learning Models",
+    "section": "Set-up",
+    "text": "Set-up\nInstall and import some libraries.\n\nfrom pathlib import Path\n\nimport folium\nimport geopandas as gpd\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport sklearn\nfrom shapely import wkt\nfrom sklearn.ensemble import RandomForestRegressor\n\nimport geowrangler.area_zonal_stats as azs\nimport geowrangler.distance_zonal_stats as dzs\nimport geowrangler.raster_zonal_stats as rzs\nimport geowrangler.vector_zonal_stats as vzs\nfrom geowrangler import grids\nfrom geowrangler.datasets import geofabrik, ookla",
+    "crumbs": [
+      "Explore Poverty Mapping Machine Learning Models"
+    ]
+  },
+  {
+    "objectID": "overview.poverty_mapping_demo.html#pre-requisite-manual-data-download",
+    "href": "overview.poverty_mapping_demo.html#pre-requisite-manual-data-download",
+    "title": "Explore Poverty Mapping Machine Learning Models",
+    "section": "Pre-requisite: Manual Data Download",
+    "text": "Pre-requisite: Manual Data Download\n\n# download data if not yet available\n![ ! -e ../data/phl_adm0.geojson ] && curl -s -o ../data/phl_adm0.geojson https://raw.githubusercontent.com/thinkingmachines/geowrangler/master/data/phl_adm0.geojson\n![ ! -e ../data/phl_dhs_cluster_level.csv ] && curl -s -o ../data/phl_dhs_cluster_level.csv https://raw.githubusercontent.com/thinkingmachines/geowrangler/master/data/phl_dhs_cluster_level.csv\n![ ! -e ../data/phl_ntl.tif ] && curl -s -o ../data/phl_ntl.tif https://raw.githubusercontent.com/thinkingmachines/geowrangler/master/data/phl_ntl.tif\n\n\nDATA_DIR = Path(\"../data/\")\n# Auto-creates the folder if it does not exist\nDATA_DIR.mkdir(parents=True, exist_ok=True)\n\nYour data directory should look something like this.\n\n\n\ndata_dir.PNG",
+    "crumbs": [
+      "Explore Poverty Mapping Machine Learning Models"
+    ]
+  },
+  {
+    "objectID": "overview.poverty_mapping_demo.html#data-preparation",
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+    "title": "Explore Poverty Mapping Machine Learning Models",
+    "section": "Data Preparation",
+    "text": "Data Preparation\n\nDHS Ground Truth\nThe ground truth used in this demo notebook is from a DHS 2017 on-the-ground household survey conducted in the Philippines regarding the households’ socio-demographic information.\nThe file we provide is already a pre-processed version of the data that is aggregated on a household-cluster level, meaning the information cannot be tied back to any individual household. Specifically, we only provide a list of household clusters with their corresponding (jittered) GPS coordinate and DHS-computed wealth index.\nDue to the sensitive nature of the data and the DHS program terms of use, we cannot provide the raw data. You can, however, request for access to raw data yourself on the DHS website. In that case, you can use GeoWrangler’s DHS processing utils help perform the said pre-processing.\nOur first step is to create a GeoDataFrame from the data.\n\n# Load ground truth data as a DataFrame first\nGROUND_TRUTH_CSV = DATA_DIR / \"phl_dhs_cluster_level.csv\"\ndf = pd.read_csv(GROUND_TRUTH_CSV)\n\n# Some of the coordinates in the data are invalid. This filters them out.\ndf = df[(df.longitude > 0) & (df.latitude > 0)]\n\n# Create a GeoDataFrame from the longitude, latitude columns.\ngdf = gpd.GeoDataFrame(\n    df, geometry=gpd.points_from_xy(df.longitude, df.latitude), crs=\"epsg:4326\"\n)\n\nprint(f\"There are {len(gdf):,} clusters.\")\ngdf.head()\n\nThere are 1,213 clusters.\n\n\n\n\n\n\n\n\n\n\nDHSCLUST\nWealth Index\nDHSID\nlongitude\nlatitude\ngeometry\n\n\n\n\n0\n1\n-31881.60870\nPH201700000001\n122.109807\n6.674652\nPOINT (122.10981 6.67465)\n\n\n1\n2\n-2855.37500\nPH201700000002\n122.132027\n6.662256\nPOINT (122.13203 6.66226)\n\n\n2\n3\n-57647.04762\nPH201700000003\n122.179496\n6.621822\nPOINT (122.17950 6.62182)\n\n\n3\n4\n-54952.66667\nPH201700000004\n122.137965\n6.485298\nPOINT (122.13796 6.48530)\n\n\n5\n6\n-80701.69565\nPH201700000006\n121.916094\n6.629457\nPOINT (121.91609 6.62946)\n\n\n\n\n\n\n\n\nNext, we want to create a buffer around each cluster centroid that represents the area’s neighborhood. This is so we can engineer some features and characterize these neighborhoods using open data.\nThis is a design decision, but for this demo, we’ll create a circlular area with a 2km radius following the random displacement introduced by DHS to preserve household privacy.\n\n# Make sure to convert to PH crs first for the buffer in meters, and then back to WGS84\ngdf = gdf.to_crs(\"epsg:3123\")\ngdf.geometry = gdf.geometry.buffer(2000)\ngdf = gdf.to_crs(\"epsg:4326\")\n\ngdf.head(5)\n\n\n\n\n\n\n\n\n\nDHSCLUST\nWealth Index\nDHSID\nlongitude\nlatitude\ngeometry\n\n\n\n\n0\n1\n-31881.60870\nPH201700000001\n122.109807\n6.674652\nPOLYGON ((122.12789 6.67461, 122.12780 6.67284...\n\n\n1\n2\n-2855.37500\nPH201700000002\n122.132027\n6.662256\nPOLYGON ((122.15011 6.66221, 122.15002 6.66044...\n\n\n2\n3\n-57647.04762\nPH201700000003\n122.179496\n6.621822\nPOLYGON ((122.19758 6.62178, 122.19749 6.62001...\n\n\n3\n4\n-54952.66667\nPH201700000004\n122.137965\n6.485298\nPOLYGON ((122.15604 6.48526, 122.15595 6.48349...\n\n\n5\n6\n-80701.69565\nPH201700000006\n121.916094\n6.629457\nPOLYGON ((121.93418 6.62942, 121.93409 6.62765...\n\n\n\n\n\n\n\n\nWe can visualize what we’ve done so far on a map. \n\n# Uncomment the next line to display an interactive map\n# gdf.explore()\n\n\n\nDownload data and load them into memory\nNext, we’ll download some data from Ookla (internet speeds) and OSM (points of interest), which we’ll use to generate neighborhood characteristics to be used as ML features.\n\n\nOokla\nOokla has released global open data gathered from speedtests made on their platform. This gives us access to internet speed information across various geographies. In our context, this can give us a signal predictor.\nFirst, let’s download a local copy of data on fixed internet in the 1st quarter of 2019 (earliest data available).\nWe can use GeoWrangler’s Ookla data utility to automatically download and cache the desired data on your machine given the type, year, and quarter.\nThis is just a simplification for the demo. In practice, you might want to aggregate data across multiple time periods or incorporate wireless data.\n\nookla_fixed_2019_q1_filepath = ookla.download_ookla_file(\n    type_=\"fixed\", year=\"2019\", quarter=\"1\"\n)\n# This is where the downloaded file is located.\n# By default this downloads to your data/ folder, but you can customize this.\nookla_fixed_2019_q1_filepath\n\nPosixPath('data/2019-01-01_performance_fixed_tiles.parquet')\n\n\n\n# This is a function to load and do some light pre-processing on the Ookla data.\n\n\ndef load_ookla_data(filename, mask=None):\n\n    # Ookla's parquet file doesn't seem to have geo metadata so need to read through pandas first\n    ookla_df = pd.read_parquet(filename)\n    ookla_gdf = gpd.GeoDataFrame(\n        ookla_df,\n        geometry=ookla_df[\"tile\"].apply(lambda x: wkt.loads(x)),\n        crs=\"epsg:4326\",\n    )\n    ookla_gdf.drop(columns=[\"tile\"], inplace=True)\n\n    # Ookla's data files contain data for the whole world.\n    # For our case, we're retaining only tiles that intersect with the given mask.\n    # This is to speed-up any processing we do later on.\n    if mask is not None:\n        keep_cols = ookla_gdf.columns\n        ookla_gdf = ookla_gdf.sjoin(mask, how=\"inner\", predicate=\"intersects\")\n        ookla_gdf = ookla_gdf[keep_cols]\n        ookla_gdf.head()\n\n    # Convert kbps to mbps for easier reading\n    ookla_gdf[\"avg_d_mbps\"] = ookla_gdf[\"avg_d_kbps\"] / 1000\n    ookla_gdf[\"avg_u_mbps\"] = ookla_gdf[\"avg_u_kbps\"] / 1000\n\n    return ookla_gdf\n\nThe Ookla data is quite large, and takes around 5 minutes to load.\n\nookla_gdf = load_ookla_data(ookla_fixed_2019_q1_filepath, mask=gdf)\n\nCPU times: user 1min 49s, sys: 10.8 s, total: 2min\nWall time: 1min 58s\n\n\n\nprint(\n    f\"{len(ookla_gdf):,} Ookla data tiles retained that intersect with our DHS cluster neighborhoods.\"\n)\nookla_gdf.head()\n\n17,245 Ookla data tiles retained that intersect with our DHS cluster neighborhoods.\n\n\n\n\n\n\n\n\n\n\nquadkey\navg_d_kbps\navg_u_kbps\navg_lat_ms\ntests\ndevices\ngeometry\navg_d_mbps\navg_u_mbps\n\n\n\n\n4368240\n1323011210311031\n10\n21\n126\n1\n1\nPOLYGON ((121.96472 20.45790, 121.97021 20.457...\n0.010\n0.021\n\n\n4368241\n1323011210311033\n5931\n10633\n228\n3\n2\nPOLYGON ((121.96472 20.45275, 121.97021 20.452...\n5.931\n10.633\n\n\n4368242\n1323011210311122\n13063\n9266\n218\n5\n2\nPOLYGON ((121.97021 20.45275, 121.97571 20.452...\n13.063\n9.266\n\n\n4368243\n1323011210311211\n20\n51\n130\n1\n1\nPOLYGON ((121.96472 20.44760, 121.97021 20.447...\n0.020\n0.051\n\n\n4368245\n1323011210311230\n195\n589\n114\n1\n1\nPOLYGON ((121.95923 20.43731, 121.96472 20.437...\n0.195\n0.589\n\n\n\n\n\n\n\n\n\n\nOSM (Open Street Maps)\nOne source of open data on points of interest is OSM. We’ll use the version of the data provided by Geofabrik vs accessing the OSM Overpass API. This allows us to not be bottlenecked by API calls.\nSimilar to Ookla, we can also use GeoWrangler’s OSM data download utils to download and cache OSM data on your machine. All you have to do is specify the country you want.\nGeofabrik’s OSM data is a zip file containing different files for POIs, waterways, roads, etc. For this demo notebook, we’ll only use the POIs.\n\n# This download can take around 1-2 minutes.\nph_osm_zip_path = geofabrik.download_geofabrik_region(\"philippines\")\n\n# This line unzips the zip file if we haven't done so yet.\n!echo 'None' | unzip $ph_osm_zip_path -d data/ph_osm/\n\nArchive:  data/philippines-latest-free.shp.zip\n  inflating: data/ph_osm/README      \n extracting: data/ph_osm/gis_osm_buildings_a_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_buildings_a_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_buildings_a_free_1.prj  \n  inflating: data/ph_osm/gis_osm_buildings_a_free_1.shp  \n  inflating: data/ph_osm/gis_osm_buildings_a_free_1.shx  \n extracting: data/ph_osm/gis_osm_landuse_a_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_landuse_a_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_landuse_a_free_1.prj  \n  inflating: data/ph_osm/gis_osm_landuse_a_free_1.shp  \n  inflating: data/ph_osm/gis_osm_landuse_a_free_1.shx  \n extracting: data/ph_osm/gis_osm_natural_a_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_natural_a_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_natural_a_free_1.prj  \n  inflating: data/ph_osm/gis_osm_natural_a_free_1.shp  \n  inflating: data/ph_osm/gis_osm_natural_a_free_1.shx  \n extracting: data/ph_osm/gis_osm_natural_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_natural_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_natural_free_1.prj  \n  inflating: data/ph_osm/gis_osm_natural_free_1.shp  \n  inflating: data/ph_osm/gis_osm_natural_free_1.shx  \n extracting: data/ph_osm/gis_osm_places_a_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_places_a_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_places_a_free_1.prj  \n  inflating: data/ph_osm/gis_osm_places_a_free_1.shp  \n  inflating: data/ph_osm/gis_osm_places_a_free_1.shx  \n extracting: data/ph_osm/gis_osm_places_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_places_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_places_free_1.prj  \n  inflating: data/ph_osm/gis_osm_places_free_1.shp  \n  inflating: data/ph_osm/gis_osm_places_free_1.shx  \n extracting: data/ph_osm/gis_osm_pofw_a_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_pofw_a_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_pofw_a_free_1.prj  \n  inflating: data/ph_osm/gis_osm_pofw_a_free_1.shp  \n  inflating: data/ph_osm/gis_osm_pofw_a_free_1.shx  \n extracting: data/ph_osm/gis_osm_pofw_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_pofw_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_pofw_free_1.prj  \n  inflating: data/ph_osm/gis_osm_pofw_free_1.shp  \n  inflating: data/ph_osm/gis_osm_pofw_free_1.shx  \n extracting: data/ph_osm/gis_osm_pois_a_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_pois_a_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_pois_a_free_1.prj  \n  inflating: data/ph_osm/gis_osm_pois_a_free_1.shp  \n  inflating: data/ph_osm/gis_osm_pois_a_free_1.shx  \n extracting: data/ph_osm/gis_osm_pois_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_pois_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_pois_free_1.prj  \n  inflating: data/ph_osm/gis_osm_pois_free_1.shp  \n  inflating: data/ph_osm/gis_osm_pois_free_1.shx  \n extracting: data/ph_osm/gis_osm_railways_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_railways_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_railways_free_1.prj  \n  inflating: data/ph_osm/gis_osm_railways_free_1.shp  \n  inflating: data/ph_osm/gis_osm_railways_free_1.shx  \n extracting: data/ph_osm/gis_osm_roads_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_roads_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_roads_free_1.prj  \n  inflating: data/ph_osm/gis_osm_roads_free_1.shp  \n  inflating: data/ph_osm/gis_osm_roads_free_1.shx  \n extracting: data/ph_osm/gis_osm_traffic_a_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_traffic_a_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_traffic_a_free_1.prj  \n  inflating: data/ph_osm/gis_osm_traffic_a_free_1.shp  \n  inflating: data/ph_osm/gis_osm_traffic_a_free_1.shx  \n extracting: data/ph_osm/gis_osm_traffic_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_traffic_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_traffic_free_1.prj  \n  inflating: data/ph_osm/gis_osm_traffic_free_1.shp  \n  inflating: data/ph_osm/gis_osm_traffic_free_1.shx  \n extracting: data/ph_osm/gis_osm_transport_a_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_transport_a_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_transport_a_free_1.prj  \n  inflating: data/ph_osm/gis_osm_transport_a_free_1.shp  \n  inflating: data/ph_osm/gis_osm_transport_a_free_1.shx  \n extracting: data/ph_osm/gis_osm_transport_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_transport_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_transport_free_1.prj  \n  inflating: data/ph_osm/gis_osm_transport_free_1.shp  \n  inflating: data/ph_osm/gis_osm_transport_free_1.shx  \n extracting: data/ph_osm/gis_osm_water_a_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_water_a_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_water_a_free_1.prj  \n  inflating: data/ph_osm/gis_osm_water_a_free_1.shp  \n  inflating: data/ph_osm/gis_osm_water_a_free_1.shx  \n extracting: data/ph_osm/gis_osm_waterways_free_1.cpg  \n  inflating: data/ph_osm/gis_osm_waterways_free_1.dbf  \n  inflating: data/ph_osm/gis_osm_waterways_free_1.prj  \n  inflating: data/ph_osm/gis_osm_waterways_free_1.shp  \n  inflating: data/ph_osm/gis_osm_waterways_free_1.shx  \n\n\n\n# Uncomment the ff line to see all the available OSM files aside from POIs.\n# !ls data/ph_osm\n\n\nph_osm_pois_filepath = \"data/ph_osm/gis_osm_pois_free_1.shp\"\nph_osm = gpd.read_file(ph_osm_pois_filepath)\n\n\nprint(f\"There are {len(ph_osm)} OSM POIs.\")\nph_osm.head()\n\nThere are 133219 OSM POIs.\n\n\n\n\n\n\n\n\n\n\nosm_id\ncode\nfclass\nname\ngeometry\n\n\n\n\n0\n21717820\n2907\ncamera_surveillance\nNone\nPOINT (121.02120 14.57608)\n\n\n1\n21717872\n2722\nmuseum\nAyala Museum\nPOINT (121.02324 14.55358)\n\n\n2\n24078301\n2402\nmotel\nSogo Grand Hotel\nPOINT (121.04515 14.56449)\n\n\n3\n24078959\n2907\ncamera_surveillance\nNone\nPOINT (121.05945 14.60098)\n\n\n4\n24797511\n2542\nbicycle_shop\nChristine Sports Cycle Marketing\nPOINT (120.99506 14.55224)",
+    "crumbs": [
+      "Explore Poverty Mapping Machine Learning Models"
+    ]
+  },
+  {
+    "objectID": "overview.poverty_mapping_demo.html#feature-engineering",
+    "href": "overview.poverty_mapping_demo.html#feature-engineering",
+    "title": "Explore Poverty Mapping Machine Learning Models",
+    "section": "Feature Engineering",
+    "text": "Feature Engineering\nNow that we’ve prepared the DHS data and loaded Ookla and OSM data into memory, we’re ready to generate some neighborhood features for our ML model.\nFor convenience, we’re wrapping all the feature engineering code in functions so we can re-use them later when we apply the model to the whole country.\nNote: if you are modifying the feature engineering portions, it is recommended to run the whole section end-to-end. This ensures you’re starting fresh from a new copy of the DHS clusters GeoDataFrame. This also ensures the function definitions are up-to-date, and can be re-used properly later for model rollout.\n\n# We'll make a copy of the GDF to avoid overwriting the original DHS GeoDataFrame.\ngdf_with_features = gdf.copy()\n\n\nOSM\nOur goal with OSM data is to generate neighborhood characteristics based on counts and distance to certain POIs, such as schools, hospitals, etc.\nTo do this, we utilize GeoWrangler’s vector zonal stats and distance zonal stats features.\n\n# Uncomment the ff. line if you want to see the different POI classes available\n# ph_osm.fclass.sort_values().unique()\n\n\ndef generate_osm_features(aoi, osm, metric_crs=\"epsg:3123\"):\n\n    aoi = aoi.copy()\n\n    # GeoWrangler: Count number of all POIs per tile\n    aoi = vzs.create_zonal_stats(\n        aoi,\n        osm,\n        overlap_method=\"intersects\",\n        aggregations=[{\"func\": \"count\", \"output\": \"poi_count\", \"fillna\": True}],\n    )\n\n    # Count specific aoi types\n    poi_types = [\"restaurant\", \"school\", \"bank\", \"supermarket\", \"mall\", \"atm\"]\n\n    for poi_type in poi_types:\n        # GeoWrangler: Count with vector zonal stats\n        aoi = vzs.create_zonal_stats(\n            aoi,\n            osm[osm[\"fclass\"] == poi_type],\n            overlap_method=\"intersects\",\n            aggregations=[\n                {\"func\": \"count\", \"output\": f\"{poi_type}_count\", \"fillna\": True}\n            ],\n        )\n\n        # GeoWrangler: Distance with distance zonal stats\n        col_name = f\"{poi_type}_nearest\"\n        aoi = dzs.create_distance_zonal_stats(\n            aoi.to_crs(metric_crs),\n            osm[osm[\"fclass\"] == poi_type].to_crs(metric_crs),\n            max_distance=10_000,\n            aggregations=[],\n            distance_col=col_name,\n        ).to_crs(\"epsg:4326\")\n\n        # If no POI was found within the distance limit, set the distance to a really high value\n        aoi[col_name] = aoi[col_name].fillna(value=999999)\n\n    return aoi\n\n\ngdf_with_features = generate_osm_features(gdf_with_features, ph_osm)\n\nCPU times: user 3.04 s, sys: 0 ns, total: 3.04 s\nWall time: 3.04 s\n\n\n\ngdf_with_features.head()\n\n\n\n\n\n\n\n\n\nDHSCLUST\nWealth Index\nDHSID\nlongitude\nlatitude\ngeometry\npoi_count\nrestaurant_count\nrestaurant_nearest\nschool_count\nschool_nearest\nbank_count\nbank_nearest\nsupermarket_count\nsupermarket_nearest\nmall_count\nmall_nearest\natm_count\natm_nearest\n\n\n\n\n0\n1\n-31881.60870\nPH201700000001\n122.109807\n6.674652\nPOLYGON ((122.12789 6.67461, 122.12780 6.67284...\n0.0\n0.0\n3152.996430\n0.0\n1392.503631\n0.0\n1764.736950\n0.0\n999999.0\n0.0\n999999.0\n0.0\n999999.000000\n\n\n1\n2\n-2855.37500\nPH201700000002\n122.132027\n6.662256\nPOLYGON ((122.15011 6.66221, 122.15002 6.66044...\n5.0\n0.0\n5788.165918\n0.0\n3601.132502\n1.0\n0.000000\n0.0\n999999.0\n0.0\n999999.0\n0.0\n999999.000000\n\n\n2\n3\n-57647.04762\nPH201700000003\n122.179496\n6.621822\nPOLYGON ((122.19758 6.62178, 122.19749 6.62001...\n0.0\n0.0\n999999.000000\n0.0\n642.871037\n0.0\n4026.300327\n0.0\n999999.0\n0.0\n999999.0\n0.0\n999999.000000\n\n\n3\n4\n-54952.66667\nPH201700000004\n122.137965\n6.485298\nPOLYGON ((122.15604 6.48526, 122.15595 6.48349...\n0.0\n0.0\n999999.000000\n0.0\n2287.839903\n0.0\n999999.000000\n0.0\n999999.0\n0.0\n999999.0\n0.0\n999999.000000\n\n\n5\n6\n-80701.69565\nPH201700000006\n121.916094\n6.629457\nPOLYGON ((121.93418 6.62942, 121.93409 6.62765...\n0.0\n0.0\n8408.925995\n0.0\n3930.606257\n0.0\n8332.324084\n0.0\n999999.0\n0.0\n999999.0\n0.0\n8311.332736\n\n\n\n\n\n\n\n\n\n\nOokla\nOur goal with Ookla data is to generate neighborhood characteristics based on internet speeds (download, upload, latency).\nTo do this, we utilize GeoWrangler’s area zonal stats feature.\n\ndef generate_ookla_features(aoi, ookla_gdf, metric_crs=\"epsg:3123\"):\n\n    aoi = aoi.copy()\n\n    orig_aoi_crs = aoi.crs\n    aoi = aoi.to_crs(metric_crs)\n    ookla_gdf = ookla_gdf.to_crs(metric_crs)\n\n    # For better formatting, rename some columns\n    ookla_gdf = ookla_gdf.rename(\n        columns={\"avg_d_mbps\": \"d_mbps\", \"avg_u_mbps\": \"u_mbps\", \"avg_lat_ms\": \"lat_ms\"}\n    )\n\n    # GeoWrangler: Compute stats on the various columns\n    aoi = azs.create_area_zonal_stats(\n        aoi,\n        ookla_gdf,\n        aggregations=[\n            # Count number of devices in the area\n            dict(column=\"devices\", func=[\"raw_sum\"], fillna=True),\n            # Get stats on the download speeds\n            dict(\n                column=\"d_mbps\",\n                func=[\"imputed_mean\", \"max\", \"min\", \"std\"],\n                fillna=[True, True, True, True],\n            ),\n            # Get stats on the upload speeds\n            dict(\n                column=\"u_mbps\",\n                func=[\"imputed_mean\", \"max\", \"min\", \"std\"],\n                fillna=[True, True, True, True],\n            ),\n        ],\n        # Don't include the land area that intersected as a column\n        include_intersect=False,\n        # Don't set minimum values to 0 if the neighborhood's area doesn't fully intersect with Ookla tiles.\n        fix_min=False,\n    )\n\n    aoi = aoi.fillna(value=\"0\")\n    aoi = aoi.to_crs(orig_aoi_crs)\n\n    return aoi\n\n\ngdf_with_features = generate_ookla_features(gdf_with_features, ookla_gdf)\n\nCPU times: user 3 s, sys: 0 ns, total: 3 s\nWall time: 3 s\n\n\n\ngdf_with_features.head()\n\n\n\n\n\n\n\n\n\nDHSCLUST\nWealth Index\nDHSID\nlongitude\nlatitude\ngeometry\npoi_count\nrestaurant_count\nrestaurant_nearest\nschool_count\n...\natm_nearest\ndevices_sum\nd_mbps_mean\nd_mbps_max\nd_mbps_min\nd_mbps_std\nu_mbps_mean\nu_mbps_max\nu_mbps_min\nu_mbps_std\n\n\n\n\n0\n1\n-31881.60870\nPH201700000001\n122.109807\n6.674652\nPOLYGON ((122.12789 6.67461, 122.12780 6.67284...\n0.0\n0.0\n3152.996430\n0.0\n...\n999999.000000\n0.0\n0.000000\n0.000\n0.000\n0.000000\n0.000000\n0.000\n0.000\n0.000000\n\n\n1\n2\n-2855.37500\nPH201700000002\n122.132027\n6.662256\nPOLYGON ((122.15011 6.66221, 122.15002 6.66044...\n5.0\n0.0\n5788.165918\n0.0\n...\n999999.000000\n38.0\n0.401851\n14.628\n2.177\n4.982053\n0.255031\n6.867\n1.866\n1.753667\n\n\n2\n3\n-57647.04762\nPH201700000003\n122.179496\n6.621822\nPOLYGON ((122.19758 6.62178, 122.19749 6.62001...\n0.0\n0.0\n999999.000000\n0.0\n...\n999999.000000\n0.0\n0.000000\n0.000\n0.000\n0.000000\n0.000000\n0.000\n0.000\n0.000000\n\n\n3\n4\n-54952.66667\nPH201700000004\n122.137965\n6.485298\nPOLYGON ((122.15604 6.48526, 122.15595 6.48349...\n0.0\n0.0\n999999.000000\n0.0\n...\n999999.000000\n0.0\n0.000000\n0.000\n0.000\n0.000000\n0.000000\n0.000\n0.000\n0.000000\n\n\n5\n6\n-80701.69565\nPH201700000006\n121.916094\n6.629457\nPOLYGON ((121.93418 6.62942, 121.93409 6.62765...\n0.0\n0.0\n8408.925995\n0.0\n...\n8311.332736\n0.0\n0.000000\n0.000\n0.000\n0.000000\n0.000000\n0.000\n0.000\n0.000000\n\n\n\n\n5 rows × 28 columns\n\n\n\n\n\n\nNight Time Lights\nLastly, we’ll generate features based on night time lights. GeoWrangler provides a raster zonal stats function that acts as a thin layer on top of rasterio’s rasterstats function. It provides a little convenience in terms of formatting and automatically joins back the features to your input GeoDataFrame.\n\ndef generate_ntl_features(aoi, raster_path):\n    aoi = aoi.copy()\n\n    aoi = rzs.create_raster_zonal_stats(\n        aoi,\n        raster_path,\n        aggregation=dict(\n            func=[\"mean\", \"min\", \"max\", \"std\"],\n            column=\"ntl\",\n        ),\n        extra_args=dict(nodata=0),\n    )\n\n    aoi.fillna(0, inplace=True)\n\n    return aoi\n\n\ngdf_with_features = generate_ntl_features(gdf_with_features, DATA_DIR / \"phl_ntl.tif\")\n\nCPU times: user 3.27 s, sys: 438 ms, total: 3.7 s\nWall time: 3.7 s\n\n\n\n\nVisualize the final dataset\nFinally, let’s visualize our cluster neighborhoods now with the generated features. Hover over each cluster to see its data.\n\n\n\nDHS Cluster Features (Zoomed)\n\n\n\n# Visualize the clusters with the generated features\n# gdf_with_features.explore()",
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+    "section": "Prepare data into X, y and generate train/test partitions",
+    "text": "Prepare data into X, y and generate train/test partitions\n\n# For convenience, store the list of features in a variable. Remove all extraneous columns.\nfeature_cols = gdf_with_features.drop(\n    columns=[\"DHSCLUST\", \"DHSID\", \"Wealth Index\", \"longitude\", \"latitude\", \"geometry\"]\n).columns\n\n# Separate data (X) and the target (y)\nX = gdf_with_features[feature_cols]\ny = gdf_with_features[\"Wealth Index\"]\n\n# Bin y into 5 buckets for stratification when splitting the dataset\ncat_y = pd.cut(y, bins=5, labels=list(range(1, 6)))\n\n# Generate train and test partitions\nX_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n    X, y, test_size=0.2, random_state=2022, stratify=cat_y\n)\n\n\nX_train.head()\n\n\n\n\n\n\n\n\n\npoi_count\nrestaurant_count\nrestaurant_nearest\nschool_count\nschool_nearest\nbank_count\nbank_nearest\nsupermarket_count\nsupermarket_nearest\nmall_count\n...\nd_mbps_min\nd_mbps_std\nu_mbps_mean\nu_mbps_max\nu_mbps_min\nu_mbps_std\nntl_mean\nntl_min\nntl_max\nntl_std\n\n\n\n\n944\n1.0\n0.0\n4332.445808\n1.0\n0.000000\n0.0\n999999.000000\n0.0\n5595.357740\n0.0\n...\n0.000\n0.000000\n0.000000\n0.000\n0.000\n0.000000\n0.279636\n0.279636\n0.279636\n0.000000\n\n\n328\n1110.0\n134.0\n0.000000\n22.0\n0.000000\n69.0\n0.000000\n12.0\n0.000000\n1.0\n...\n8.948\n6.519399\n0.033496\n30.564\n2.496\n5.933372\n9.881545\n4.587300\n17.190298\n2.768707\n\n\n696\n0.0\n0.0\n4396.530482\n0.0\n7331.181601\n0.0\n999999.000000\n0.0\n999999.000000\n0.0\n...\n0.000\n0.000000\n0.000000\n0.000\n0.000\n0.000000\n0.000000\n0.000000\n0.000000\n0.000000\n\n\n155\n2.0\n0.0\n352.510311\n0.0\n669.141767\n0.0\n2606.478381\n0.0\n2566.227871\n0.0\n...\n1.215\n31.305303\n0.863634\n151.303\n0.106\n35.998549\n0.299591\n0.182455\n0.519110\n0.125637\n\n\n614\n272.0\n13.0\n0.000000\n5.0\n0.000000\n14.0\n0.000000\n8.0\n0.000000\n1.0\n...\n2.986\n6.722738\n0.174738\n32.774\n0.527\n7.027801\n0.335981\n0.216280\n0.543996\n0.084581\n\n\n\n\n5 rows × 26 columns\n\n\n\n\n\ny_train.head()\n\n944    -27114.20833\n328    175912.44440\n696    -29987.93750\n155     75826.10526\n614     54849.37500\nName: Wealth Index, dtype: float64",
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+    "text": "Evaluation Function\nWe define an evaluation function that computes R^2 and makes a plot of the actual vs predicted values.\n\ndef evaluate(model, X_train, X_test, y_train, y_test):\n\n    # R^2\n    train_predictions = model.predict(X_train)\n    print(\"Train R2 score:\", sklearn.metrics.r2_score(y_train, train_predictions))\n\n    test_predictions = model.predict(X_test)\n    print(\"Test R2 score:\", sklearn.metrics.r2_score(y_test, test_predictions))\n\n    # Plot\n\n    # Train and test predictions vs actual values\n    plt.scatter(train_predictions, y_train, label=\"Train samples\", c=\"#d95f02\")\n    plt.scatter(test_predictions, y_test, label=\"Test samples\", c=\"#7570b3\")\n\n    # Identity line\n    xpoints = ypoints = plt.xlim()\n    plt.plot(\n        xpoints, ypoints, linestyle=\"--\", color=\"k\", lw=3, scalex=False, scaley=False\n    )\n\n    # Labels and legends\n    plt.xlabel(\"Predicted value\")\n    plt.ylabel(\"True value\")\n    plt.legend()\n    plt.tight_layout()\n    plt.show()",
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+    "text": "Fit and Evaluate Model\nFor ML modelling, let’s fit a simple random forest regressor.\nAs mentioned, we are keeping the ML modelling as simple as possible in this notebook. This is definitely not the best-performing wealth estimation model out there, but should be decent enough for demo purposes.\n\nmodel = RandomForestRegressor()\nmodel.fit(X_train, y_train)\nevaluate(model, X_train, X_test, y_train, y_test)\n\nTrain R2 score: 0.9213900086093023\nTest R2 score: 0.5921958077621785",
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+    "section": "Generate country-wide tiles",
+    "text": "Generate country-wide tiles\nFirst, we need to split the country up into smaller areas.\nThat usually means generating grid tiles or hex tiles for your area of interest. In this notebook, we’ll generate Bing tiles at zoom level 13 (which has the closest area to our 2km neighborhoods in training).\nGeoWrangler provides various grid generation utilities for these different scenarios.\n\n# Read in the admin boundary for PHL.\n# This file is a simplified version of the admin boundary to make the file size smaller and load times faster.\nphl_adm = gpd.read_file(f\"{DATA_DIR}/phl_adm0.geojson\")\n\n# Uncomment this line if you want to visualize it\n# phl_adm.explore()\n\nThe generation can take around 1-2 minutes.\n\nphl_tiles = grids.BingTileGridGenerator(13).generate_grid(phl_adm)\nprint(f\"There are {len(phl_tiles):,} tiles.\")\nphl_tiles.head()\n\nThere are 16,257 tiles.\nCPU times: user 27.6 s, sys: 0 ns, total: 27.6 s\nWall time: 27.6 s\n\n\n\n\n\n\n\n\n\n\nquadkey\ngeometry\n\n\n\n\n0\n1323230031332\nPOLYGON ((119.44336 4.56547, 119.44336 4.60928...\n\n\n1\n1323230031320\nPOLYGON ((119.35547 4.60928, 119.35547 4.65308...\n\n\n2\n1323230030300\nPOLYGON ((119.00391 4.69688, 119.00391 4.74068...\n\n\n3\n1323230030311\nPOLYGON ((119.13574 4.69688, 119.13574 4.74068...\n\n\n4\n1323230031201\nPOLYGON ((119.22363 4.69688, 119.22363 4.74068...",
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+    "text": "Feature Engineering\nThis part is simpler, as we’re just re-using the feature engineering functions we previously created. Note though that we are generating features for a bigger area, so the runtime is a little longer (in total should be within 2 minutes).\n\nphl_tiles_with_features = phl_tiles.copy()\n\n\nphl_tiles_with_features = generate_osm_features(phl_tiles_with_features, ph_osm)\n\nCPU times: user 4.24 s, sys: 10.7 ms, total: 4.25 s\nWall time: 4.25 s\n\n\n\nphl_tiles_with_features = generate_ookla_features(phl_tiles_with_features, ookla_gdf)\n\nCPU times: user 1.93 s, sys: 20.8 ms, total: 1.95 s\nWall time: 1.95 s\n\n\n\nphl_tiles_with_features = generate_ntl_features(\n    phl_tiles_with_features, f\"{DATA_DIR}/phl_ntl.tif\"\n)\n\nCPU times: user 37.5 s, sys: 1.16 s, total: 38.6 s\nWall time: 38.7 s",
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+    "section": "Make predictions and viz output",
+    "text": "Make predictions and viz output\nFinally, we are ready to apply the model and make predictions throughout the whole country. We can also visualize it on a Cholorpleth map.\n\nphl_tiles_with_features[\"predicted_wealth_index\"] = model.predict(\n    phl_tiles_with_features[feature_cols]\n)\n\n\ndef viz_chloropleth(gdf, target_feature=\"predicted_wealth_index\"):\n\n    map = folium.Map(location=[14.6091, 121.0223], width=1000, height=800, zoom_start=7)\n    subset = gdf.copy()\n    subset[\"id\"] = list(range(len(subset)))\n\n    folium.Choropleth(\n        geo_data=subset,\n        data=subset,\n        name=\"Wealth Predictions\",\n        columns=[\"id\", target_feature],\n        key_on=\"feature.properties.id\",\n        fill_color=\"Spectral\",\n        legend_name=target_feature,\n    ).add_to(map)\n\n    return map\n\nVisualize the output\n\n\n\nPredicted Wealth Index Rollout (Philippines)\n\n\n\n# Uncomment the line below to visualize rollout\n# viz_chloropleth(phl_tiles_with_features)",
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+    "text": "from rasterio import features\nfrom rasterio.plot import show\nfrom rasterio.windows import Window, transform\nsource",
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+    "text": "Test data\n\nGenerating a raster mask\n\n# Get filepaths\ntiff_file = \"../data/vector_to_raster_mask_sample/cabanglasan.tif\"\nshape_file = \"../data/vector_to_raster_mask_sample/labels_20220816.gpkg\"\ntarget_file = shape_file.replace(\"gpkg\", \"tiff\")\n\nGiven a raster image of a certain area that will be masked to use as a reference and a shape file that contains that area. Note that the shape file must include a column that contains labels/categories.\n\ngpd.read_file(shape_file).head(3)\n\n\n\n\n\n\n\n\n\nyear\nlabel\nuid\nADM3_EN\nADM3_PCODE\nADM2_EN\nADM2_PCODE\nADM1_EN\nADM1_PCODE\ngeometry\n\n\n\n\n0\n2017.0\nmining\n72_2017_mining\nSofronio Española\nPH175324000\nPalawan\nPH175300000\nRegion IV-B\nPH170000000\nMULTIPOLYGON Z (((117.95961 9.03303 0.00000, 1...\n\n\n1\n2017.0\nmining\n71_2017_mining\nSofronio Española\nPH175324000\nPalawan\nPH175300000\nRegion IV-B\nPH170000000\nMULTIPOLYGON Z (((117.95507 9.03809 0.00000, 1...\n\n\n2\n2017.0\nmining\n70_2017_mining\nSofronio Española\nPH175324000\nPalawan\nPH175300000\nRegion IV-B\nPH170000000\nMULTIPOLYGON Z (((117.95663 9.03869 0.00000, 1...\n\n\n\n\n\n\n\n\nAnd a dictionary with the labels and assigned values to be used in creating a mask\n\nlabels = {\n    \"mining\": 1,\n    \"neg\": 2,\n    \"agriculture\": 3,\n    \"product_extraction\": 4,\n    \"kaingin\": 5,\n    \"biophysical\": 6,\n}\n\nInput them in the generate_mask function to create a raster mask of the same dimension as the reference raster image\n\n# Generate masks for a file\nmasks, grids, values = generate_mask(\n    tiff_file=tiff_file,\n    shape_file=shape_file,\n    output_file=target_file,\n    labels_column=\"label\",\n    labels_dict=labels,\n    plot=True,\n)\n\n\n\n\n\n\n\n\n\nmasks\n\narray([[0, 0, 0, ..., 0, 0, 0],\n       [0, 0, 0, ..., 0, 0, 0],\n       [0, 0, 0, ..., 0, 0, 0],\n       ...,\n       [0, 0, 0, ..., 0, 0, 0],\n       [0, 0, 0, ..., 0, 0, 0],\n       [0, 0, 0, ..., 0, 0, 0]], dtype=uint16)\n\n\n\ngrids\n\narray([[0, 0, 0, ..., 0, 0, 0],\n       [0, 0, 0, ..., 0, 0, 0],\n       [0, 0, 0, ..., 0, 0, 0],\n       ...,\n       [0, 0, 0, ..., 0, 0, 0],\n       [0, 0, 0, ..., 0, 0, 0],\n       [0, 0, 0, ..., 0, 0, 0]], dtype=uint16)\n\n\n\nvalues\n\n{'mining': 1,\n 'neg': 2,\n 'agriculture': 3,\n 'product_extraction': 4,\n 'kaingin': 5,\n 'biophysical': 6}",
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+    "title": "Distance Zonal Stats",
+    "section": "Test data",
+    "text": "Test data\n\nSimple squares\nGiven an aoi (simple_aoi) and geodataframe containing sample data (simple_data)\n\nsimple_aoi\n\n\n\n\n\n\n\n\n\ngeometry\n\n\n\n\n0\nPOLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...\n\n\n1\nPOLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...\n\n\n2\nPOLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...\n\n\n\n\n\n\n\n\n\nsimple_data\n\n\n\n\n\n\n\n\n\ngeometry\npopulation\ninternet_speed\n\n\n\n\n0\nPOLYGON ((0.250 0.000, 0.250 1.000, 1.250 1.00...\n100\n20.0\n\n\n1\nPOLYGON ((1.250 0.000, 1.250 1.000, 2.250 1.00...\n200\n10.0\n\n\n2\nPOLYGON ((2.250 0.000, 2.250 1.000, 3.250 1.00...\n300\n5.0\n\n\n\n\n\n\n\n\nWe also have simple point data which do not intersect with our AOIs.\n\nsimple_point_data\n\n\n\n\n\n\n\n\n\ngeometry\npopulation\ninternet_speed\n\n\n\n\n0\nPOINT (0.500 3.000)\n100\n20.0\n\n\n1\nPOINT (0.500 4.000)\n600\n120.0\n\n\n2\nPOINT (0.500 5.000)\n1100\n220.0\n\n\n3\nPOINT (0.500 6.000)\n1600\n320.0\n\n\n4\nPOINT (0.500 7.000)\n2100\n420.0\n\n\n5\nPOINT (1.500 3.000)\n200\n10.0\n\n\n6\nPOINT (1.500 4.000)\n700\n110.0\n\n\n7\nPOINT (1.500 5.000)\n1200\n210.0\n\n\n8\nPOINT (1.500 6.000)\n1700\n310.0\n\n\n9\nPOINT (1.500 7.000)\n2200\n410.0\n\n\n10\nPOINT (2.500 3.000)\n300\n5.0\n\n\n11\nPOINT (2.500 4.000)\n800\n105.0\n\n\n12\nPOINT (2.500 5.000)\n1300\n205.0\n\n\n13\nPOINT (2.500 6.000)\n1800\n305.0\n\n\n14\nPOINT (2.500 7.000)\n2300\n405.0\n\n\n\n\n\n\n\n\n\nax = plt.axes()\nax = simple_data.plot(\n    ax=ax, color=[\"orange\", \"brown\", \"purple\"], edgecolor=\"yellow\", alpha=0.4\n)\nax = simple_aoi.plot(ax=ax, facecolor=\"none\", edgecolor=[\"r\", \"g\", \"b\"])\nax = simple_point_data.plot(ax=ax)\n\n\n\n\n\n\n\n\nThe red,green,blue outlines are the 3 regions of interest (aoi) while the orange,brown, purple areas are the data areas.The blue dots are data which do not intersect our AOIs.\n\nresults = create_distance_zonal_stats(\n    simple_aoi,\n    simple_point_data,\n    max_distance=7,\n    aggregations=[\n        dict(func=\"count\"),\n        dict(func=\"sum\", column=\"population\"),\n        dict(func=\"mean\", column=\"internet_speed\"),\n    ],\n)\n\nCPU times: user 32.1 ms, sys: 11 ms, total: 43.1 ms\nWall time: 36 ms\n\n\n\nresults\n\n\n\n\n\n\n\n\n\ngeometry\nindex_count\npopulation_sum\ninternet_speed_mean\nnearest\n\n\n\n\n0\nPOLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...\n1\n100\n20.0\n2.0\n\n\n1\nPOLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...\n1\n200\n10.0\n2.0\n\n\n2\nPOLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...\n1\n300\n5.0\n2.0\n\n\n\n\n\n\n\n\n\nresults2 = create_distance_zonal_stats(\n    simple_aoi,\n    simple_data,\n    max_distance=1,\n    aggregations=[\n        dict(func=\"count\"),\n        dict(func=\"sum\", column=\"population\"),\n        dict(func=\"mean\", column=\"internet_speed\"),\n    ],\n)\n\nCPU times: user 38.5 ms, sys: 0 ns, total: 38.5 ms\nWall time: 35 ms\n\n\n\nresults2\n\n\n\n\n\n\n\n\n\ngeometry\nindex_count\npopulation_sum\ninternet_speed_mean\nnearest\n\n\n\n\n0\nPOLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...\n1\n100\n20.0\n0.0\n\n\n1\nPOLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...\n2\n300\n15.0\n0.0\n\n\n2\nPOLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...\n2\n500\n7.5\n0.0",
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+    "title": "Spatial Join Highest Intersection",
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+    "text": "Import\n\n\nGenerate Test Data\n\n\nLoad a sample admin boundary file using geopandas\n\nadmin_bounds_gdf = gpd.read_file(\"../data/geoboundary.geojson\")\n\n\nadmin_bounds_gdf.head(3)\n\n\n\n\n\n\n\n\n\nshapeName\nshapeISO\nshapeID\nshapeGroup\nshapeType\ngeometry\n\n\n\n\n0\nAbra\nNone\nPHL-ADM2-3_0_0-B1\nPHL\nADM2\nMULTIPOLYGON (((120.96795 17.95706, 120.97803 ...\n\n\n1\nAgusan del Norte\nNone\nPHL-ADM2-3_0_0-B2\nPHL\nADM2\nMULTIPOLYGON (((125.57724 9.45679, 125.59687 9...\n\n\n2\nAgusan del Sur\nNone\nPHL-ADM2-3_0_0-B3\nPHL\nADM2\nMULTIPOLYGON (((125.91087 8.85625, 125.91461 8...\n\n\n\n\n\n\n\n\n\nadmin_bounds_gdf.dtypes\n\nshapeName       object\nshapeISO        object\nshapeID         object\nshapeGroup      object\nshapeType       object\ngeometry      geometry\ndtype: object\n\n\n\n# admin_bounds_gdf.explore()\n\n\n\nGenerate a sample grid\n\ngrid_generator5k = grids.SquareGridGenerator(50_000)  # 5 km x 5 km square cells\n\n\ngrid_gdf = grid_generator5k.generate_grid(admin_bounds_gdf)\n\nCPU times: user 5.08 s, sys: 14.4 ms, total: 5.09 s\nWall time: 5.11 s\n\n\n\ngrid_gdf.plot();\n\n\n\n\n\n\n\n\n\nax = admin_bounds_gdf.plot(facecolor=\"grey\", edgecolor=\"grey\", alpha=0.2)\nax = grid_gdf.plot(ax=ax, facecolor=\"none\", edgecolor=\"green\")\n\n\n\n\n\n\n\n\n\ngrid_gdf.describe()\n\n\n\n\n\n\n\n\n\nx\ny\n\n\n\n\ncount\n323.000000\n323.000000\n\n\nmean\n11.773994\n15.018576\n\n\nstd\n4.861594\n8.791210\n\n\nmin\n0.000000\n0.000000\n\n\n25%\n8.000000\n8.000000\n\n\n50%\n11.000000\n14.000000\n\n\n75%\n16.000000\n21.000000\n\n\nmax\n21.000000\n37.000000\n\n\n\n\n\n\n\n\n\ngrid_gdf.head(3)\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\n\n\n\n\n0\n3\n3\nPOLYGON ((118.27581 5.92871, 118.72497 5.92871...\n\n\n1\n2\n3\nPOLYGON ((117.82665 5.92871, 118.27581 5.92871...\n\n\n2\n3\n4\nPOLYGON ((118.27581 6.37528, 118.72497 6.37528...\n\n\n\n\n\n\n\n\n\ngrid_gdf.dtypes\n\nx              int64\ny              int64\ngeometry    geometry\ndtype: object\n\n\n\n\nSpatial join with highest intersection\n\nsource\n\nget_highest_intersection\n\n get_highest_intersection (gdf1:geopandas.geodataframe.GeoDataFrame,\n                           gdf2:geopandas.geodataframe.GeoDataFrame,\n                           proj_crs:str)\n\nGets the intersection based on the largest area joined\n\n\n\n\n\n\n\n\n\nType\nDetails\n\n\n\n\ngdf1\nGeoDataFrame\ngdf1 will be the basis of output geometry\n\n\ngdf2\nGeoDataFrame\ngdf2 data will all be included during intersection\n\n\nproj_crs\nstr\nmetric CRS (e.g., Philippines uses EPSG:32651)\n\n\nReturns\nGeoDataFrame\n\n\n\n\n\nget_highest_intersection(grid_gdf, admin_bounds_gdf, \"EPSG:32651\")\n\n\n\n\n\n\n\n\n\ngeometry\nx\ny\nshapeName\nshapeISO\nshapeID\nshapeGroup\nshapeType\n\n\n\n\n0\nPOLYGON ((118.27581 5.92871, 118.72497 5.92871...\n3\n3\nTawi-Tawi\nNone\nPHL-ADM2-3_0_0-B77\nPHL\nADM2\n\n\n1\nPOLYGON ((117.82665 5.92871, 118.27581 5.92871...\n2\n3\nTawi-Tawi\nNone\nPHL-ADM2-3_0_0-B77\nPHL\nADM2\n\n\n2\nPOLYGON ((118.27581 6.37528, 118.72497 6.37528...\n3\n4\nTawi-Tawi\nNone\nPHL-ADM2-3_0_0-B77\nPHL\nADM2\n\n\n3\nPOLYGON ((118.27581 6.82146, 118.72497 6.82146...\n3\n5\nTawi-Tawi\nNone\nPHL-ADM2-3_0_0-B77\nPHL\nADM2\n\n\n4\nPOLYGON ((116.92834 7.26723, 117.37749 7.26723...\n0\n6\nPalawan\nNone\nPHL-ADM2-3_0_0-B59\nPHL\nADM2\n\n\n...\n...\n...\n...\n...\n...\n...\n...\n...\n\n\n318\nPOLYGON ((125.46233 5.03451, 125.91149 5.03451...\n19\n1\nDavao del Sur\nNone\nPHL-ADM2-3_0_0-B28\nPHL\nADM2\n\n\n319\nPOLYGON ((125.46233 9.93163, 125.91149 9.93163...\n19\n12\nDinagat Islands\nNone\nPHL-ADM2-3_0_0-B30\nPHL\nADM2\n\n\n320\nPOLYGON ((125.46233 10.37375, 125.91149 10.373...\n19\n13\nEastern Samar\nNone\nPHL-ADM2-3_0_0-B31\nPHL\nADM2\n\n\n321\nPOLYGON ((125.91149 9.93163, 126.36065 9.93163...\n20\n12\nSurigao del Norte\nNone\nPHL-ADM2-3_0_0-B74\nPHL\nADM2\n\n\n322\nPOLYGON ((125.91149 10.37375, 126.36065 10.373...\n20\n13\nEastern Samar\nNone\nPHL-ADM2-3_0_0-B31\nPHL\nADM2\n\n\n\n\n323 rows × 8 columns\n\n\n\n\n\n\n\nCheck plot of output\n\noutput = get_highest_intersection(grid_gdf, admin_bounds_gdf, \"EPSG:32651\")\n\n\noutput.plot()\n\n\n\n\n\n\n\n\n\n# output.explore()",
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+    "title": "Vector Zonal Stats",
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+    "text": "Aggregations\nIn order to generate zonal stats for an area (or areas) of interest (aoi) , we have come up with the concept of an aggregation specification or agg spec, which is a way to specify what aggregation functions (such as count,sum, mean,std etc.) are to be applied to columns in the source dataframe (data).\nThe method create_zonal_stats can then take in a list of these agg specs and apply them to create zonal stats from the data for the aoi.\nEach agg spec consists of a dict with the following keys:\n\nfunc: (Required) a str or a list [str] of aggregation functions. See the pandas documentation for agg\ncolumn: (Optional) an existing column in the data to generate the zonal statistic from. If not specified, the grouping key based on the index of the aoi applied to the data is used as default.\noutput: (Optional) a str or a list [str] of the name(s) of the output zonal statistic column. If not specified it is concatenated from the column and func i.e. {column}_{func} (e.g. 'func':'mean' on 'column':'population' has a default value 'output':'population_mean')\nfillna: (Optional) a bool or a list [bool] of the flag(s) that indicates whether to to a fillna(0) step for the new zonal column, True meaning it will set any NA values in the resulting zonal stat to 0, and False will retain any NA values. The default value of the flag(s) is False.\n\nExamples\n\nThe simplest aggregation spec. This will result in an output column named index_count as it will use the aoi index as the default column.\n\n{\"func\":\"count\"}\n\nThe sum function is applied to the data column population which will create an output column named total_population.\n\n{\n \"func:\"sum\",\n \"column\": \"population\",\n \"output\": \"total_population\"\n}\n\nCompute the zonal stats mean,sum,max on the population column and rename the output columns (by default) to population_mean, population_sum and population_max.\n\n{\n \"func\": [\"mean\",\"sum\",\"max\"],\n \"column\": \"population\",\n}\n\nA full aggregation spec with fillna. fillna == False for std means it will remain an NA if there is no data for the column in the group. The default value for fillna is True which means that 0 is used to replace any NA in the output column.\n\n{\n \"func\": [\"mean\", \"sum\", \"std\"],\n \"column\":\"population\",\n \"output\": [\"avg_pop\", \"total_pop\", \"std_dev\"],\n \"fillna\": [True,True,False],\n}\nThe agg spec in the list of aggregations can contain the same columns, but the output columns must be unique since they will added as columns in the results.\n\n\nRegular and Grid Zonal Stats\n\nVector zonal stats for user defined areas and grids (e.g. Admin areas)\n\n\nsource\n\n\ncreate_zonal_stats\n\n create_zonal_stats (aoi:geopandas.geodataframe.GeoDataFrame,\n                     data:geopandas.geodataframe.GeoDataFrame,\n                     aggregations:List[Dict[str,Any]],\n                     overlap_method:str='intersects')\n\nCreate zonal stats for area of interest from data using aggregration operations on data columns. Returns the same aoi with additional columns containing the computed zonal features.\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\naoi\nGeoDataFrame\n\nArea of interest for which zonal stats are to be computed for\n\n\ndata\nGeoDataFrame\n\nSource gdf containing data to compute zonal stats from\n\n\naggregations\nList\n\nList of agg specs, with each agg spec applied to a data column\n\n\noverlap_method\nstr\nintersects\nspatial predicate to used in spatial join of aoi and data geopandas.sjoin for more details\n\n\nReturns\nGeoDataFrame\n\ncategorical_column_options: str = None,\n\n\n\n\nsimple_aoi  # sample aoi\n\n\n\n\n\n\n\n\n\ncol1\nlat0\nlon0\nlat1\nlon1\nlat2\nlon2\nlat3\nlon3\ngeometry\n\n\n\n\n0\n1\n0.0\n0.0\n0.0\n1.0\n1.0\n1.0\n1.0\n0.0\nPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....\n\n\n1\n2\n1.0\n0.0\n1.0\n1.0\n2.0\n1.0\n2.0\n0.0\nPOLYGON ((1.00000 0.00000, 1.00000 1.00000, 2....\n\n\n2\n3\n2.0\n0.0\n2.0\n1.0\n3.0\n1.0\n3.0\n0.0\nPOLYGON ((2.00000 0.00000, 2.00000 1.00000, 3....\n\n\n\n\n\n\n\n\n\nsimple_data  # sample data\n\n\n\n\n\n\n\n\n\ncol1\nlat\nlon\ngeometry\n\n\n\n\n0\n1\n0.50\n0.50\nPOINT (0.50000 0.50000)\n\n\n1\n2\n1.50\n0.50\nPOINT (1.50000 0.50000)\n\n\n2\n3\n2.50\n0.50\nPOINT (2.50000 0.50000)\n\n\n3\n4\n0.45\n0.50\nPOINT (0.45000 0.50000)\n\n\n4\n5\n1.45\n0.50\nPOINT (1.45000 0.50000)\n\n\n5\n6\n2.45\n0.50\nPOINT (2.45000 0.50000)\n\n\n6\n7\n0.45\n0.45\nPOINT (0.45000 0.45000)\n\n\n7\n8\n1.45\n0.45\nPOINT (1.45000 0.45000)\n\n\n8\n9\n2.45\n0.45\nPOINT (2.45000 0.45000)\n\n\n9\n10\n0.45\n1.45\nPOINT (0.45000 1.45000)\n\n\n10\n11\n1.45\n1.45\nPOINT (1.45000 1.45000)\n\n\n11\n12\n2.45\n1.45\nPOINT (2.45000 1.45000)\n\n\n\n\n\n\n\n\n\nax = simple_aoi.plot(\n    ax=plt.axes(), facecolor=\"none\", edgecolor=[\"red\", \"blue\", \"green\"]\n)\nax = simple_data.plot(ax=ax, color=\"purple\")\n\n\n\n\n\n\n\n\n\nresults = create_zonal_stats(simple_aoi, simple_data, aggregations=[{\"func\": \"count\"}])\nresults\n\n\n\n\n\n\n\n\n\ncol1\nlat0\nlon0\nlat1\nlon1\nlat2\nlon2\nlat3\nlon3\ngeometry\nindex_count\n\n\n\n\n0\n1\n0.0\n0.0\n0.0\n1.0\n1.0\n1.0\n1.0\n0.0\nPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....\n3\n\n\n1\n2\n1.0\n0.0\n1.0\n1.0\n2.0\n1.0\n2.0\n0.0\nPOLYGON ((1.00000 0.00000, 1.00000 1.00000, 2....\n3\n\n\n2\n3\n2.0\n0.0\n2.0\n1.0\n3.0\n1.0\n3.0\n0.0\nPOLYGON ((2.00000 0.00000, 2.00000 1.00000, 3....\n3\n\n\n\n\n\n\n\n\nIndex name is not none\n\nnamed_index_aoi = simple_aoi.copy()\nnamed_index_aoi.index.name = \"myindex\"\n\n\nnamed_index_aoi\n\n\n\n\n\n\n\n\n\ncol1\nlat0\nlon0\nlat1\nlon1\nlat2\nlon2\nlat3\nlon3\ngeometry\n\n\nmyindex\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n0\n1\n0.0\n0.0\n0.0\n1.0\n1.0\n1.0\n1.0\n0.0\nPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....\n\n\n1\n2\n1.0\n0.0\n1.0\n1.0\n2.0\n1.0\n2.0\n0.0\nPOLYGON ((1.00000 0.00000, 1.00000 1.00000, 2....\n\n\n2\n3\n2.0\n0.0\n2.0\n1.0\n3.0\n1.0\n3.0\n0.0\nPOLYGON ((2.00000 0.00000, 2.00000 1.00000, 3....\n\n\n\n\n\n\n\n\n\nnamed_index_results = create_zonal_stats(\n    named_index_aoi, simple_data, aggregations=[{\"func\": \"count\"}]\n)\n\n\nnamed_index_results.head()\n\n\n\n\n\n\n\n\n\ncol1\nlat0\nlon0\nlat1\nlon1\nlat2\nlon2\nlat3\nlon3\ngeometry\nindex_count\n\n\nmyindex\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n0\n1\n0.0\n0.0\n0.0\n1.0\n1.0\n1.0\n1.0\n0.0\nPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....\n3\n\n\n1\n2\n1.0\n0.0\n1.0\n1.0\n2.0\n1.0\n2.0\n0.0\nPOLYGON ((1.00000 0.00000, 1.00000 1.00000, 2....\n3\n\n\n2\n3\n2.0\n0.0\n2.0\n1.0\n3.0\n1.0\n3.0\n0.0\nPOLYGON ((2.00000 0.00000, 2.00000 1.00000, 3....\n3\n\n\n\n\n\n\n\n\n\n\nBing Map Tile Grid Zonal Stats\n\nGenerating zonal stats for Bing tile grid AOIs\n\n\nsource\n\n\ncompute_quadkey\n\n compute_quadkey (data:geopandas.geodataframe.GeoDataFrame,\n                  zoom_level:int, inplace:bool=False,\n                  quadkey_column:str='quadkey')\n\nComputes the quadkeys for the geometries of the data. If geometries are not points, the quadkeys are computed from the centroids of the geometries.\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\ndata\nGeoDataFrame\n\nThe geodataframe\n\n\nzoom_level\nint\n\nThe quadkey zoom level (1-23)\n\n\ninplace\nbool\nFalse\nWhether to change data inplace or not\n\n\nquadkey_column\nstr\nquadkey\nThe name of the quadkey output column\n\n\nReturns\nGeoDataFrame\n\n\n\n\n\nIf our existing data geodataframe doesn’t have quadkeys, we can use the compute_quadkey to generate the quadkeys for the centroid of the data’s geometries.\n\nDATA_ZOOM_LEVEL = 19\nAOI_ZOOM_LEVEL = 9\nsimple_data_quadkey = compute_quadkey(simple_data, DATA_ZOOM_LEVEL)\n\nCPU times: user 34.6 ms, sys: 0 ns, total: 34.6 ms\nWall time: 34.4 ms\n\n\n\nsimple_data_quadkey.head()\n\n\n\n\n\n\n\n\n\ncol1\nlat\nlon\ngeometry\nquadkey\n\n\n\n\n0\n1\n0.50\n0.5\nPOINT (0.50000 0.50000)\n1222222221211211222\n\n\n1\n2\n1.50\n0.5\nPOINT (1.50000 0.50000)\n1222222320210201222\n\n\n2\n3\n2.50\n0.5\nPOINT (2.50000 0.50000)\n1222222331200311222\n\n\n3\n4\n0.45\n0.5\nPOINT (0.45000 0.50000)\n1222222221210201333\n\n\n4\n5\n1.45\n0.5\nPOINT (1.45000 0.50000)\n1222222320200311333\n\n\n\n\n\n\n\n\n\nsource\n\n\ncreate_bingtile_zonal_stats\n\n create_bingtile_zonal_stats (aoi:pandas.core.frame.DataFrame,\n                              data:pandas.core.frame.DataFrame,\n                              aggregations:List[Dict[str,Any]],\n                              aoi_quadkey_column:str='quadkey',\n                              data_quadkey_column:str='quadkey')\n\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\naoi\nDataFrame\n\nAn aoi with quadkey column\n\n\ndata\nDataFrame\n\nData with quadkey column\n\n\naggregations\nList\n\nList of agg specs, with each agg spec applied to a data column\n\n\naoi_quadkey_column\nstr\nquadkey\nColumn name of aoi quadkey\n\n\ndata_quadkey_column\nstr\nquadkey\nColumn name of data quadkey\n\n\nReturns\nDataFrame\n\n\n\n\n\nTo create bingtile zonal stats, we need to compute the quadkeys for the areas of interest (AOI) and the data.\nThe geowrangler.grids module provides a BingTileGridGenerator that will generate the quadkeys for the areas covered by your AOIs.\n\nimport geowrangler.grids as gr\n\n\nbgtile_generator = gr.BingTileGridGenerator(AOI_ZOOM_LEVEL)\nsimple_aoi_bingtiles = bgtile_generator.generate_grid(simple_aoi)\n\nUsing the data with the computed quadkeys, we can generate zonal stats for our bingtile grid aois. This just uses the regular pandas grouping and merging function and skips any geospatial joins which results in faster computation.\n\nbingtile_results = create_bingtile_zonal_stats(\n    simple_aoi_bingtiles,\n    simple_data_quadkey,\n    aggregations=[dict(func=\"count\", fillna=True)],\n)\n\nCPU times: user 35.7 ms, sys: 2.56 ms, total: 38.2 ms\nWall time: 37.5 ms\n\n\n\nbingtile_results\n\n\n\n\n\n\n\n\n\nquadkey\ngeometry\nindex_count\n\n\n\n\n0\n122222220\nPOLYGON ((-0.00000 0.70311, -0.00000 1.40611, ...\n0.0\n\n\n1\n122222222\nPOLYGON ((-0.00000 0.00000, -0.00000 0.70311, ...\n3.0\n\n\n2\n122222221\nPOLYGON ((0.70312 0.70311, 0.70312 1.40611, 1....\n0.0\n\n\n3\n122222223\nPOLYGON ((0.70312 0.00000, 0.70312 0.70311, 1....\n0.0\n\n\n4\n122222230\nPOLYGON ((1.40625 0.70311, 1.40625 1.40611, 2....\n0.0\n\n\n5\n122222232\nPOLYGON ((1.40625 0.00000, 1.40625 0.70311, 2....\n3.0\n\n\n6\n122222231\nPOLYGON ((2.10937 0.70311, 2.10937 1.40611, 2....\n0.0\n\n\n7\n122222233\nPOLYGON ((2.10937 0.00000, 2.10937 0.70311, 2....\n3.0\n\n\n8\n122222320\nPOLYGON ((2.81250 0.70311, 2.81250 1.40611, 3....\n0.0\n\n\n9\n122222322\nPOLYGON ((2.81250 0.00000, 2.81250 0.70311, 3....\n0.0\n\n\n\n\n\n\n\n\nWe can also use any bingtile grid for any zoom level lower than the data’s zoom level\n\nbgtile_generator10 = gr.BingTileGridGenerator(AOI_ZOOM_LEVEL + 1)\nsimple_aoi_bingtiles10 = bgtile_generator10.generate_grid(simple_aoi)\n\n\nbingtile_results10 = create_bingtile_zonal_stats(\n    simple_aoi_bingtiles10,\n    simple_data_quadkey,\n    aggregations=[dict(func=\"count\", fillna=True)],\n)\n\n\nbingtile_results10[bingtile_results10.index_count > 0]\n\n\n\n\n\n\n\n\n\nquadkey\ngeometry\nindex_count\n\n\n\n\n4\n1222222221\nPOLYGON ((0.35156 0.35156, 0.35156 0.70311, 0....\n3.0\n\n\n13\n1222222320\nPOLYGON ((1.40625 0.35156, 1.40625 0.70311, 1....\n3.0\n\n\n19\n1222222330\nPOLYGON ((2.10937 0.35156, 2.10937 0.70311, 2....\n2.0\n\n\n22\n1222222331\nPOLYGON ((2.46094 0.35156, 2.46094 0.70311, 2....\n1.0\n\n\n\n\n\n\n\n\n\nax = results.plot(ax=plt.axes(), column=\"index_count\", edgecolor=\"blue\", alpha=0.2)\nax = simple_data.plot(ax=ax, color=\"purple\")\nax = bingtile_results.plot(ax=ax, column=\"index_count\", edgecolor=\"black\", alpha=0.4)\nax = bingtile_results10.plot(ax=ax, column=\"index_count\", edgecolor=\"red\", alpha=0.4)",
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+    "text": "Loading DHS Data\nNote: This data is mocked data. Request official data from their available datasets through their website.\n\nimport geopandas as gpd\nimport pandas as pd\n\nfrom geowrangler import dhs\n\n\ndhs_household_data_path = \"../data/ph.DTA\"\ndhs_gps_coordinates = \"../data/ph_gps.shp\"\n\n\ndhs_df = dhs.load_dhs_file(dhs_household_data_path)\n\nCPU times: user 11.2 ms, sys: 583 µs, total: 11.8 ms\nWall time: 10.6 ms\n\n\n\ndhs_df.head()\n\n\n\n\n\n\n\n\n\n\ncountry code and phase\ncluster number\nsource of drinking water\ntype of toilet facility\nhas electricity\nhas radio\nhas television\nhas refrigerator\nhas motorcycle/scooter\nhas car/truck\nmain floor material\nnumber of rooms used for sleeping\nhas mobile telephone\nwealth index factor score combined (5 decimals)\n\n\n\n\n0\n0\nPH7\n725\n80\n61\n0\n1\n0\n1\n1\n1\n74\n20\n0\n-168581\n\n\n1\n1\nPH7\n1009\n19\n92\n1\n1\n1\n1\n1\n0\n51\n15\n1\n127550\n\n\n2\n2\nPH7\n1072\n91\n58\n1\n1\n0\n1\n1\n1\n26\n5\n0\n32616\n\n\n3\n3\nPH7\n242\n39\n93\n0\n1\n0\n0\n0\n1\n76\n11\n0\n80338\n\n\n4\n4\nPH7\n102\n11\n27\n0\n0\n1\n1\n1\n0\n56\n15\n0\n-178758",
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+    "text": "Renaming columns to match\nDHS files do not have uniform column names. To make analysis easier, we rename commonnly ones. Feel free to extend the config to your usecase.\n\nph_config = dhs.load_column_config(\"ph\")\nph_config\n\n{'cluster number': 'DHSCLUST',\n 'wealth index factor score combined (5 decimals)': 'Wealth Index',\n 'country code and phase': 'country code and phase',\n 'number of rooms used for sleeping': 'rooms',\n 'has electricity': 'electric',\n 'has mobile telephone': 'mobile telephone',\n 'has radio': 'radio',\n 'has television': 'television',\n 'has car/truck': 'car/truck',\n 'has refrigerator': 'refrigerator',\n 'has motorcycle/scooter': 'motorcycle',\n 'main floor material': 'floor',\n 'type of toilet facility': 'toilet',\n 'source of drinking water': 'drinking water'}\n\n\n\nrenamed_dhs_df = dhs_df.rename(columns=ph_config)\n\n\nrenamed_dhs_df.head()\n\n\n\n\n\n\n\n\n\n\ncountry code and phase\nDHSCLUST\ndrinking water\ntoilet\nelectric\nradio\ntelevision\nrefrigerator\nmotorcycle\ncar/truck\nfloor\nrooms\nmobile telephone\nWealth Index\n\n\n\n\n0\n0\nPH7\n725\n80\n61\n0\n1\n0\n1\n1\n1\n74\n20\n0\n-168581\n\n\n1\n1\nPH7\n1009\n19\n92\n1\n1\n1\n1\n1\n0\n51\n15\n1\n127550\n\n\n2\n2\nPH7\n1072\n91\n58\n1\n1\n0\n1\n1\n1\n26\n5\n0\n32616\n\n\n3\n3\nPH7\n242\n39\n93\n0\n1\n0\n0\n0\n1\n76\n11\n0\n80338\n\n\n4\n4\nPH7\n102\n11\n27\n0\n0\n1\n1\n1\n0\n56\n15\n0\n-178758",
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+    "text": "Cluster Summaries\n\nwealth_col_name = \"Wealth Index\"\ncluster_col_name = \"DHSCLUST\"\n\n\nsummarized = (\n    renamed_dhs_df[[wealth_col_name, cluster_col_name]].groupby(cluster_col_name).mean()\n)\nsummarized.reset_index(inplace=True)\n\n\nsummarized.head()\n\n\n\n\n\n\n\n\n\nDHSCLUST\nWealth Index\n\n\n\n\n0\n3\n-232188.0\n\n\n1\n4\n228860.0\n\n\n2\n5\n157620.0\n\n\n3\n6\n40308.5\n\n\n4\n7\n-37595.5\n\n\n\n\n\n\n\n\n\nlen(summarized)\n\n679\n\n\n\nsummarized.DHSCLUST.isna().sum()\n\n0\n\n\n\nsummarized.DHSCLUST.isna().sum()\n\n0\n\n\n\ndhs_shp = gpd.read_file(dhs_gps_coordinates)\n\n\ndhs_shp.head()\n\n\n\n\n\n\n\n\n\nDHSID\nDHSCC\nDHSYEAR\nDHSCLUST\nLATNUM\nLONGNUM\ngeometry\n\n\n\n\n0\nPH20XX725\nPH\n0\n725\n0.409441\n0.220510\nPOINT (0.22051 0.40944)\n\n\n1\nPH20XX1009\nPH\n0\n1009\n0.333693\n0.332499\nPOINT (0.33250 0.33369)\n\n\n2\nPH20XX1072\nPH\n0\n1072\n0.378053\n0.089852\nPOINT (0.08985 0.37805)\n\n\n3\nPH20XX242\nPH\n0\n242\n0.306277\n0.431677\nPOINT (0.43168 0.30628)\n\n\n4\nPH20XX102\nPH\n0\n102\n0.535456\n0.716025\nPOINT (0.71602 0.53546)\n\n\n\n\n\n\n\n\n\nsurvey_geo = pd.merge(summarized, dhs_shp, on=\"DHSCLUST\")\nsurvey_geo\n\n\n\n\n\n\n\n\n\nDHSCLUST\nWealth Index\nDHSID\nDHSCC\nDHSYEAR\nLATNUM\nLONGNUM\ngeometry\n\n\n\n\n0\n3\n-232188.000000\nPH20XX003\nPH\n0\n0.609945\n0.350830\nPOINT (0.35083 0.60995)\n\n\n1\n4\n228860.000000\nPH20XX004\nPH\n0\n0.363843\n0.281563\nPOINT (0.28156 0.36384)\n\n\n2\n5\n157620.000000\nPH20XX005\nPH\n0\n0.715438\n0.145014\nPOINT (0.14501 0.71544)\n\n\n3\n6\n40308.500000\nPH20XX006\nPH\n0\n0.758501\n0.628373\nPOINT (0.62837 0.75850)\n\n\n4\n6\n40308.500000\nPH20XX006\nPH\n0\n0.669415\n0.479379\nPOINT (0.47938 0.66942)\n\n\n...\n...\n...\n...\n...\n...\n...\n...\n...\n\n\n995\n1247\n90273.333333\nPH20XX1247\nPH\n0\n0.214682\n0.419448\nPOINT (0.41945 0.21468)\n\n\n996\n1248\n212729.500000\nPH20XX1248\nPH\n0\n0.189401\n0.152806\nPOINT (0.15281 0.18940)\n\n\n997\n1248\n212729.500000\nPH20XX1248\nPH\n0\n0.563460\n0.023900\nPOINT (0.02390 0.56346)\n\n\n998\n1250\n101508.500000\nPH20XX1250\nPH\n0\n0.166465\n0.617584\nPOINT (0.61758 0.16646)\n\n\n999\n1250\n101508.500000\nPH20XX1250\nPH\n0\n0.252438\n0.668243\nPOINT (0.66824 0.25244)\n\n\n\n\n1000 rows × 8 columns",
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+    "text": "Recalculating wealth index for a single country\n\nfeatures = [\n    \"rooms\",\n    \"electric\",\n    \"mobile telephone\",\n    \"radio\",\n    \"television\",\n    \"car/truck\",\n    \"refrigerator\",\n    \"motorcycle\",\n    \"floor\",\n    \"toilet\",\n    \"drinking water\",\n]\n\n\n# apply a threshold\ndhs.apply_threshold(renamed_dhs_df, columns=features, config={\"rooms\": [0, 25]})\n\n\n\n\n\n\n\n\n\n\ncountry code and phase\nDHSCLUST\ndrinking water\ntoilet\nelectric\nradio\ntelevision\nrefrigerator\nmotorcycle\ncar/truck\nfloor\nrooms\nmobile telephone\nWealth Index\n\n\n\n\n0\n0\nPH7\n725\n80\n61\n0\n1\n0\n1\n1\n1\n74\n20\n0\n-168581\n\n\n1\n1\nPH7\n1009\n19\n92\n1\n1\n1\n1\n1\n0\n51\n15\n1\n127550\n\n\n2\n2\nPH7\n1072\n91\n58\n1\n1\n0\n1\n1\n1\n26\n5\n0\n32616\n\n\n3\n3\nPH7\n242\n39\n93\n0\n1\n0\n0\n0\n1\n76\n11\n0\n80338\n\n\n4\n4\nPH7\n102\n11\n27\n0\n0\n1\n1\n1\n0\n56\n15\n0\n-178758\n\n\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n\n\n995\n995\nPH7\n834\n63\n83\n0\n1\n1\n1\n1\n1\n28\n10\n0\n241846\n\n\n996\n996\nPH7\n1170\n74\n78\n1\n0\n1\n1\n1\n0\n34\n17\n1\n106424\n\n\n997\n997\nPH7\n762\n81\n14\n1\n1\n0\n1\n1\n0\n83\n12\n1\n246898\n\n\n998\n998\nPH7\n63\n40\n21\n0\n1\n1\n1\n1\n1\n12\n2\n1\n-212590\n\n\n999\n999\nPH7\n748\n56\n38\n1\n1\n1\n0\n1\n1\n69\n7\n1\n-10774\n\n\n\n\n1000 rows × 15 columns\n\n\n\n\n\nrenamed_dhs_df[\"Recomputed Wealth Index\"] = dhs.assign_wealth_index(\n    renamed_dhs_df[features], features\n)\nrenamed_dhs_df.head()\n\n\n\n\n\n\n\n\n\n\ncountry code and phase\nDHSCLUST\ndrinking water\ntoilet\nelectric\nradio\ntelevision\nrefrigerator\nmotorcycle\ncar/truck\nfloor\nrooms\nmobile telephone\nWealth Index\nRecomputed Wealth Index\n\n\n\n\n0\n0\nPH7\n725\n80\n61\n0\n1\n0\n1\n1\n1\n74\n20\n0\n-168581\n-63.515731\n\n\n1\n1\nPH7\n1009\n19\n92\n1\n1\n1\n1\n1\n0\n51\n15\n1\n127550\n8.730482\n\n\n2\n2\nPH7\n1072\n91\n58\n1\n1\n0\n1\n1\n1\n26\n5\n0\n32616\n-60.121255\n\n\n3\n3\nPH7\n242\n39\n93\n0\n1\n0\n0\n0\n1\n76\n11\n0\n80338\n-14.959946\n\n\n4\n4\nPH7\n102\n11\n27\n0\n0\n1\n1\n1\n0\n56\n15\n0\n-178758\n-14.060453",
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+    "section": "Recalculating wealth index for multiple countries",
+    "text": "Recalculating wealth index for multiple countries\n\ndhs_ph_path = \"../data/ph.DTA\"\ndhs_kh_path = \"../data/kh.DTA\"\ndhs_mm_path = \"../data/mm.DTA\"\ndhs_tl_path = \"../data/tl.DTA\"\nph_config = dhs.load_column_config(\"ph\")\nkh_config = dhs.load_column_config(\"kh\")\nmm_config = dhs.load_column_config(\"mm\")\ntl_config = dhs.load_column_config(\"tl\")\ndhs_ph_df = dhs.load_dhs_file(dhs_ph_path).rename(columns=ph_config)\ndhs_kh_df = dhs.load_dhs_file(dhs_kh_path).rename(columns=kh_config)\ndhs_mm_df = dhs.load_dhs_file(dhs_mm_path).rename(columns=mm_config)\ndhs_tl_df = dhs.load_dhs_file(dhs_tl_path).rename(columns=tl_config)\n\n\ncols = list(ph_config.values()) + [\"country code and phase\"]\nmerged_df = pd.concat(\n    [\n        dhs_ph_df[cols],\n        dhs_kh_df[cols],\n        dhs_mm_df[cols],\n        dhs_tl_df[cols],\n    ]\n)\nmerged_df = merged_df.fillna(0)\n\n\nmerged_df[\"Recomputed Wealth Index\"] = dhs.assign_wealth_index(merged_df[features])\nmerged_df.head()\n\n\n\n\n\n\n\n\n\nDHSCLUST\nWealth Index\ncountry code and phase\nrooms\nelectric\nmobile telephone\nradio\ntelevision\ncar/truck\nrefrigerator\nmotorcycle\nfloor\ntoilet\ndrinking water\ncountry code and phase\nRecomputed Wealth Index\n\n\n\n\n0\n725\n-168581\nPH7\n20\n0\n0\n1\n0\n1\n1\n1\n74\n61\n80.0\nPH7\n122.888238\n\n\n1\n1009\n127550\nPH7\n15\n1\n1\n1\n1\n0\n1\n1\n51\n92\n19.0\nPH7\n78.430888\n\n\n2\n1072\n32616\nPH7\n5\n1\n0\n1\n0\n1\n1\n1\n26\n58\n91.0\nPH7\n108.113989\n\n\n3\n242\n80338\nPH7\n11\n0\n0\n1\n0\n1\n0\n0\n76\n93\n39.0\nPH7\n105.464687\n\n\n4\n102\n-178758\nPH7\n15\n0\n0\n0\n1\n0\n1\n1\n56\n27\n11.0\nPH7\n45.993876\n\n\n\n\n\n\n\n\n\nimport matplotlib.pyplot as plt  # noqa\n\n\nmerged_df.hist(\"Recomputed Wealth Index\")\n\narray([[<Axes: title={'center': 'Recomputed Wealth Index'}>]],\n      dtype=object)\n\n\n\n\n\n\n\n\n\n\nmerged_df[\"Recomputed Wealth Index Not PCA\"] = dhs.assign_wealth_index(\n    merged_df[features], use_pca=False\n)\nmerged_df.head()\n\n\n\n\n\n\n\n\n\nDHSCLUST\nWealth Index\ncountry code and phase\nrooms\nelectric\nmobile telephone\nradio\ntelevision\ncar/truck\nrefrigerator\nmotorcycle\nfloor\ntoilet\ndrinking water\ncountry code and phase\nRecomputed Wealth Index\nRecomputed Wealth Index Not PCA\n\n\n\n\n0\n725\n-168581\nPH7\n20\n0\n0\n1\n0\n1\n1\n1\n74\n61\n80.0\nPH7\n122.888238\n-10730.578127\n\n\n1\n1009\n127550\nPH7\n15\n1\n1\n1\n1\n0\n1\n1\n51\n92\n19.0\nPH7\n78.430888\n-57116.290440\n\n\n2\n1072\n32616\nPH7\n5\n1\n0\n1\n0\n1\n1\n1\n26\n58\n91.0\nPH7\n108.113989\n30004.584457\n\n\n3\n242\n80338\nPH7\n11\n0\n0\n1\n0\n1\n0\n0\n76\n93\n39.0\nPH7\n105.464687\n-48966.528484\n\n\n4\n102\n-178758\nPH7\n15\n0\n0\n0\n1\n0\n1\n1\n56\n27\n11.0\nPH7\n45.993876\n-39722.455483\n\n\n\n\n\n\n\n\n\nmerged_df.hist(\"Recomputed Wealth Index Not PCA\")\n\narray([[<Axes: title={'center': 'Recomputed Wealth Index Not PCA'}>]],\n      dtype=object)",
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+    "text": "Basic introduction to downloading open geospatial data using Geowrangler",
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+    "title": "Datasets Download",
+    "section": "Downloading Geofabrik Data",
+    "text": "Downloading Geofabrik Data\n\nimport geopandas as gpd\nfrom geowrangler.datasets import geofabrik\n\n\nListing down available regions\n\nregions = geofabrik.list_geofabrik_regions()\n# list down regions in asia\n{k: v for k, v in regions.items() if \"asia\" in v}\n\n{'afghanistan': 'https://download.geofabrik.de/asia/afghanistan-latest-free.shp.zip',\n 'armenia': 'https://download.geofabrik.de/asia/armenia-latest-free.shp.zip',\n 'azerbaijan': 'https://download.geofabrik.de/asia/azerbaijan-latest-free.shp.zip',\n 'bangladesh': 'https://download.geofabrik.de/asia/bangladesh-latest-free.shp.zip',\n 'bhutan': 'https://download.geofabrik.de/asia/bhutan-latest-free.shp.zip',\n 'cambodia': 'https://download.geofabrik.de/asia/cambodia-latest-free.shp.zip',\n 'central-zone': 'https://download.geofabrik.de/asia/india/central-zone-latest-free.shp.zip',\n 'china': 'https://download.geofabrik.de/asia/china-latest-free.shp.zip',\n 'chubu': 'https://download.geofabrik.de/asia/japan/chubu-latest-free.shp.zip',\n 'chugoku': 'https://download.geofabrik.de/asia/japan/chugoku-latest-free.shp.zip',\n 'east-timor': 'https://download.geofabrik.de/asia/east-timor-latest-free.shp.zip',\n 'eastern-zone': 'https://download.geofabrik.de/asia/india/eastern-zone-latest-free.shp.zip',\n 'gcc-states': 'https://download.geofabrik.de/asia/gcc-states-latest-free.shp.zip',\n 'hokkaido': 'https://download.geofabrik.de/asia/japan/hokkaido-latest-free.shp.zip',\n 'iran': 'https://download.geofabrik.de/asia/iran-latest-free.shp.zip',\n 'iraq': 'https://download.geofabrik.de/asia/iraq-latest-free.shp.zip',\n 'israel-and-palestine': 'https://download.geofabrik.de/asia/israel-and-palestine-latest-free.shp.zip',\n 'java': 'https://download.geofabrik.de/asia/indonesia/java-latest-free.shp.zip',\n 'jordan': 'https://download.geofabrik.de/asia/jordan-latest-free.shp.zip',\n 'kalimantan': 'https://download.geofabrik.de/asia/indonesia/kalimantan-latest-free.shp.zip',\n 'kansai': 'https://download.geofabrik.de/asia/japan/kansai-latest-free.shp.zip',\n 'kanto': 'https://download.geofabrik.de/asia/japan/kanto-latest-free.shp.zip',\n 'kazakhstan': 'https://download.geofabrik.de/asia/kazakhstan-latest-free.shp.zip',\n 'kyrgyzstan': 'https://download.geofabrik.de/asia/kyrgyzstan-latest-free.shp.zip',\n 'kyushu': 'https://download.geofabrik.de/asia/japan/kyushu-latest-free.shp.zip',\n 'laos': 'https://download.geofabrik.de/asia/laos-latest-free.shp.zip',\n 'lebanon': 'https://download.geofabrik.de/asia/lebanon-latest-free.shp.zip',\n 'malaysia-singapore-brunei': 'https://download.geofabrik.de/asia/malaysia-singapore-brunei-latest-free.shp.zip',\n 'maldives': 'https://download.geofabrik.de/asia/maldives-latest-free.shp.zip',\n 'maluku': 'https://download.geofabrik.de/asia/indonesia/maluku-latest-free.shp.zip',\n 'mongolia': 'https://download.geofabrik.de/asia/mongolia-latest-free.shp.zip',\n 'myanmar': 'https://download.geofabrik.de/asia/myanmar-latest-free.shp.zip',\n 'nepal': 'https://download.geofabrik.de/asia/nepal-latest-free.shp.zip',\n 'north-eastern-zone': 'https://download.geofabrik.de/asia/india/north-eastern-zone-latest-free.shp.zip',\n 'north-korea': 'https://download.geofabrik.de/asia/north-korea-latest-free.shp.zip',\n 'northern-zone': 'https://download.geofabrik.de/asia/india/northern-zone-latest-free.shp.zip',\n 'nusa-tenggara': 'https://download.geofabrik.de/asia/indonesia/nusa-tenggara-latest-free.shp.zip',\n 'pakistan': 'https://download.geofabrik.de/asia/pakistan-latest-free.shp.zip',\n 'papua': 'https://download.geofabrik.de/asia/indonesia/papua-latest-free.shp.zip',\n 'philippines': 'https://download.geofabrik.de/asia/philippines-latest-free.shp.zip',\n 'shikoku': 'https://download.geofabrik.de/asia/japan/shikoku-latest-free.shp.zip',\n 'south-korea': 'https://download.geofabrik.de/asia/south-korea-latest-free.shp.zip',\n 'southern-zone': 'https://download.geofabrik.de/asia/india/southern-zone-latest-free.shp.zip',\n 'sri-lanka': 'https://download.geofabrik.de/asia/sri-lanka-latest-free.shp.zip',\n 'sulawesi': 'https://download.geofabrik.de/asia/indonesia/sulawesi-latest-free.shp.zip',\n 'sumatra': 'https://download.geofabrik.de/asia/indonesia/sumatra-latest-free.shp.zip',\n 'syria': 'https://download.geofabrik.de/asia/syria-latest-free.shp.zip',\n 'taiwan': 'https://download.geofabrik.de/asia/taiwan-latest-free.shp.zip',\n 'tajikistan': 'https://download.geofabrik.de/asia/tajikistan-latest-free.shp.zip',\n 'thailand': 'https://download.geofabrik.de/asia/thailand-latest-free.shp.zip',\n 'tohoku': 'https://download.geofabrik.de/asia/japan/tohoku-latest-free.shp.zip',\n 'turkmenistan': 'https://download.geofabrik.de/asia/turkmenistan-latest-free.shp.zip',\n 'uzbekistan': 'https://download.geofabrik.de/asia/uzbekistan-latest-free.shp.zip',\n 'vietnam': 'https://download.geofabrik.de/asia/vietnam-latest-free.shp.zip',\n 'western-zone': 'https://download.geofabrik.de/asia/india/western-zone-latest-free.shp.zip',\n 'yemen': 'https://download.geofabrik.de/asia/yemen-latest-free.shp.zip'}\n\n\n\n\nDownloading a region file to a directory\n\ndownloaded_file = geofabrik.download_geofabrik_region(\"laos\", \"../test_dir\")\ndownloaded_file\n\n2023-02-01 17:00:04.495 | INFO     | geowrangler.datasets.utils:urlretrieve:27 - Retrieving https://download.geofabrik.de/asia/laos-latest-free.shp.zip into ../test_dir/laos-latest-free.shp.zip\n\n\n\n\n\n\n\n    \n      \n      100.00% [97345536/97341916 00:17<00:00]\n    \n    \n\n\nCPU times: user 1.58 s, sys: 980 ms, total: 2.56 s\nWall time: 18 s\n\n\nPath('../test_dir/laos-latest-free.shp.zip')\n\n\n\n\nLoading geofabrik files\n\ngdf = gpd.read_file(downloaded_file)\ngdf.head()\n\nCPU times: user 19.4 s, sys: 528 ms, total: 19.9 s\nWall time: 19.9 s\n\n\n\n\n\n\n\n\n\n\nosm_id\ncode\nfclass\nname\ntype\ngeometry\n\n\n\n\n0\n36192811\n1500\nbuilding\nDon Chan Palace\nNone\nPOLYGON ((102.61392 17.95514, 102.61419 17.955...\n\n\n1\n36195671\n1500\nbuilding\nຕະຫລາດເຊົ້າ. 早市塲\nNone\nPOLYGON ((102.61382 17.96520, 102.61393 17.965...\n\n\n2\n36195973\n1500\nbuilding\nMercure Hotel Laotel\nNone\nPOLYGON ((102.59386 17.96890, 102.59432 17.968...\n\n\n3\n36784437\n1500\nbuilding\nNone\nNone\nPOLYGON ((105.79786 15.11905, 105.79838 15.119...\n\n\n4\n37886432\n1500\nbuilding\nPresidential Palace\nNone\nPOLYGON ((102.60940 17.96246, 102.60951 17.962...\n\n\n\n\n\n\n\n\nYou can also list the contents of the zipped shape file as well as load the shape file within it\n\n!unzip -l {downloaded_file.as_posix()}\n\nArchive:  ../test_dir/laos-latest-free.shp.zip\n  Length      Date    Time    Name\n---------  ---------- -----   ----\n      647  2023-02-01 09:00   README\n        6  2023-02-01 09:00   gis_osm_buildings_a_free_1.cpg\n 68322734  2023-02-01 09:00   gis_osm_buildings_a_free_1.dbf\n      144  2023-02-01 09:00   gis_osm_buildings_a_free_1.prj\n 60089724  2023-02-01 09:00   gis_osm_buildings_a_free_1.shp\n  3312708  2023-02-01 09:00   gis_osm_buildings_a_free_1.shx\n        6  2023-02-01 09:00   gis_osm_landuse_a_free_1.cpg\n  2845642  2023-02-01 09:00   gis_osm_landuse_a_free_1.dbf\n      144  2023-02-01 09:00   gis_osm_landuse_a_free_1.prj\n 14407732  2023-02-01 09:00   gis_osm_landuse_a_free_1.shp\n   157092  2023-02-01 09:00   gis_osm_landuse_a_free_1.shx\n        6  2023-02-01 09:00   gis_osm_natural_a_free_1.cpg\n     7702  2023-02-01 09:00   gis_osm_natural_a_free_1.dbf\n      144  2023-02-01 09:00   gis_osm_natural_a_free_1.prj\n    15556  2023-02-01 09:00   gis_osm_natural_a_free_1.shp\n      516  2023-02-01 09:00   gis_osm_natural_a_free_1.shx\n        6  2023-02-01 09:00   gis_osm_natural_free_1.cpg\n   753872  2023-02-01 09:00   gis_osm_natural_free_1.dbf\n     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2023-02-01 09:00   gis_osm_railways_free_1.prj\n   101636  2023-02-01 09:00   gis_osm_railways_free_1.shp\n     5124  2023-02-01 09:00   gis_osm_railways_free_1.shx\n        6  2023-02-01 09:00   gis_osm_roads_free_1.cpg\n 20909019  2023-02-01 09:00   gis_osm_roads_free_1.dbf\n      144  2023-02-01 09:00   gis_osm_roads_free_1.prj\n 50224636  2023-02-01 09:00   gis_osm_roads_free_1.shp\n   914140  2023-02-01 09:00   gis_osm_roads_free_1.shx\n        6  2023-02-01 09:00   gis_osm_traffic_a_free_1.cpg\n   102532  2023-02-01 09:00   gis_osm_traffic_a_free_1.dbf\n      144  2023-02-01 09:00   gis_osm_traffic_a_free_1.prj\n   131304  2023-02-01 09:00   gis_osm_traffic_a_free_1.shp\n     5748  2023-02-01 09:00   gis_osm_traffic_a_free_1.shx\n        6  2023-02-01 09:00   gis_osm_traffic_free_1.cpg\n   275807  2023-02-01 09:00   gis_osm_traffic_free_1.dbf\n      144  2023-02-01 09:00   gis_osm_traffic_free_1.prj\n    53328  2023-02-01 09:00   gis_osm_traffic_free_1.shp\n    15308  2023-02-01 09:00   gis_osm_traffic_free_1.shx\n        6  2023-02-01 09:00   gis_osm_transport_a_free_1.cpg\n    10892  2023-02-01 09:00   gis_osm_transport_a_free_1.dbf\n      144  2023-02-01 09:00   gis_osm_transport_a_free_1.prj\n    28840  2023-02-01 09:00   gis_osm_transport_a_free_1.shp\n      692  2023-02-01 09:00   gis_osm_transport_a_free_1.shx\n        6  2023-02-01 09:00   gis_osm_transport_free_1.cpg\n    82522  2023-02-01 09:00   gis_osm_transport_free_1.dbf\n      144  2023-02-01 09:00   gis_osm_transport_free_1.prj\n    16004  2023-02-01 09:00   gis_osm_transport_free_1.shp\n     4644  2023-02-01 09:00   gis_osm_transport_free_1.shx\n        6  2023-02-01 09:00   gis_osm_water_a_free_1.cpg\n   979927  2023-02-01 09:00   gis_osm_water_a_free_1.dbf\n      144  2023-02-01 09:00   gis_osm_water_a_free_1.prj\n 14026044  2023-02-01 09:00   gis_osm_water_a_free_1.shp\n    54156  2023-02-01 09:00   gis_osm_water_a_free_1.shx\n        6  2023-02-01 09:00   gis_osm_waterways_free_1.cpg\n  1662494  2023-02-01 09:00   gis_osm_waterways_free_1.dbf\n      144  2023-02-01 09:00   gis_osm_waterways_free_1.prj\n 14396164  2023-02-01 09:00   gis_osm_waterways_free_1.shp\n    88756  2023-02-01 09:00   gis_osm_waterways_free_1.shx\n---------                     -------\n259339618                     91 files\n\n\n\ngdf2 = gpd.read_file(f\"{downloaded_file.as_posix()}!gis_osm_pois_free_1.shp\")\n\nCPU times: user 745 ms, sys: 19.4 ms, total: 764 ms\nWall time: 773 ms\n\n\n\n\nUnzipping and Caching OSM\nIn addition downloading, the geofabrik module provides an unzipping and caching facility (default cache directory: ~/.cache/geowrangler/osm ) to make it easier to access OSM data.\n\ndownload_path = geofabrik.download_osm_region_data(\"afghanistan\")\ndownload_path\n\n2023-02-01 17:00:42.767 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:127 - OSM Data: Cached data available for afghanistan at /home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip? False\n2023-02-01 17:00:43.526 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:141 - OSM Data: Re-initializing OSM region zip file at /home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip...\n2023-02-01 17:00:43.529 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:148 - OSM Data: Downloading Geofabrik in /home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip...\n2023-02-01 17:00:44.247 | INFO     | geowrangler.datasets.utils:urlretrieve:27 - Retrieving https://download.geofabrik.de/asia/afghanistan-latest-free.shp.zip into /home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip\n2023-02-01 17:01:23.951 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:159 - OSM Data: Successfully downloaded and cached OSM data for afghanistan at /home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip!\n\n\n\n\n\n\n\n    \n      \n      100.00% [241524736/241516716 00:39<00:00]\n    \n    \n\n\nCPU times: user 3.22 s, sys: 1.76 s, total: 4.98 s\nWall time: 41.2 s\n\n\nPath('/home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip')\n\n\nDownloading it the second time around should be much faster as it only checks if its in the cache and returns the path\n\ndownload_path2 = geofabrik.download_osm_region_data(\"afghanistan\")\ndownload_path2\n\n2023-02-01 17:01:23.973 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:127 - OSM Data: Cached data available for afghanistan at /home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip? True\n\n\nCPU times: user 2.22 ms, sys: 349 µs, total: 2.57 ms\nWall time: 2.08 ms\n\n\nPath('/home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip')\n\n\n\n!ls -aldh  {download_path2}\n\n-rw-r--r-- 1 butchtm butchtm 231M Feb  1 17:01 /home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip\n\n\n\n\nUsing the OSM Data Manager\nWe also provide an OSM Data Manager which, in addition to using the default cache (~/.cache/geowrangler/osm), also caches the geodataframe for either the pois or the roads datasets from OSM in memory to avoid having to reload the OSM data from disk.\n\n# Create the osm data manager\nodm = geofabrik.OsmDataManager()\n\n\npois_ph = odm.load_pois(\"philippines\")\n\n2023-02-01 17:01:24.214 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:127 - OSM Data: Cached data available for philippines at /home/butchtm/.cache/geowrangler/osm/philippines-latest-free.shp.zip? False\n2023-02-01 17:01:25.631 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:141 - OSM Data: Re-initializing OSM region zip file at /home/butchtm/.cache/geowrangler/osm/philippines-latest-free.shp.zip...\n2023-02-01 17:01:25.634 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:148 - OSM Data: Downloading Geofabrik in /home/butchtm/.cache/geowrangler/osm/philippines-latest-free.shp.zip...\n2023-02-01 17:01:26.355 | INFO     | geowrangler.datasets.utils:urlretrieve:27 - Retrieving https://download.geofabrik.de/asia/philippines-latest-free.shp.zip into /home/butchtm/.cache/geowrangler/osm/philippines-latest-free.shp.zip\n2023-02-01 17:09:06.368 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:159 - OSM Data: Successfully downloaded and cached OSM data for philippines at /home/butchtm/.cache/geowrangler/osm/philippines-latest-free.shp.zip!\n2023-02-01 17:09:06.370 | DEBUG    | geowrangler.datasets.geofabrik:load_pois:218 - OSM POIs for philippines being loaded from /home/butchtm/.cache/geowrangler/osm/philippines-latest-free.shp.zip\n\n\n\n\n\n\n\n    \n      \n      100.08% [1185939456/1185011560 07:39<00:00]\n    \n    \n\n\n\npois_ph.head()\n\n\n\n\n\n\n\n\n\nosm_id\ncode\nfclass\nname\ngeometry\n\n\n\n\n0\n21717820\n2907\ncamera_surveillance\nNone\nPOINT (121.02120 14.57608)\n\n\n1\n21717872\n2722\nmuseum\nAyala Museum\nPOINT (121.02324 14.55358)\n\n\n2\n24078301\n2402\nmotel\nSogo Grand Hotel\nPOINT (121.04515 14.56449)\n\n\n3\n24078959\n2907\ncamera_surveillance\nNone\nPOINT (121.05945 14.60098)\n\n\n4\n24797511\n2542\nbicycle_shop\nChristine Sports Cycle Marketing\nPOINT (120.99506 14.55224)\n\n\n\n\n\n\n\n\n\nroads_ph = odm.load_roads(\"philippines\")\n\n2023-02-01 17:09:10.649 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:127 - OSM Data: Cached data available for philippines at /home/butchtm/.cache/geowrangler/osm/philippines-latest-free.shp.zip? True\n2023-02-01 17:09:10.653 | DEBUG    | geowrangler.datasets.geofabrik:load_roads:274 - OSM Roads for philippines being loaded from /home/butchtm/.cache/geowrangler/osm/philippines-latest-free.shp.zip\n\n\n\nroads_ph.head()\n\n\n\n\n\n\n\n\n\nosm_id\ncode\nfclass\nname\nref\noneway\nmaxspeed\nlayer\nbridge\ntunnel\ngeometry\n\n\n\n\n0\n267\n5115\ntertiary\nMaharlika Street\nNone\nF\n30\n0\nF\nF\nLINESTRING (121.05195 14.65011, 121.05209 14.6...\n\n\n1\n2508359\n5153\nfootway\nSagada Mission Compound\nNone\nB\n0\n0\nF\nF\nLINESTRING (120.90199 17.08090, 120.90197 17.0...\n\n\n2\n2667097\n5113\nprimary\nTaft Avenue\n170\nF\n60\n0\nF\nF\nLINESTRING (120.99682 14.55577, 120.99671 14.5...\n\n\n3\n2667099\n5113\nprimary\nLerma Street\n170\nF\n60\n0\nF\nF\nLINESTRING (120.98539 14.60460, 120.98545 14.6...\n\n\n4\n2667105\n5122\nresidential\nManalito Street\nNone\nB\n0\n0\nF\nF\nLINESTRING (120.99380 14.54125, 120.99497 14.5...",
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+    "section": "Loading OSM data from other years",
+    "text": "Loading OSM data from other years\nIn addition to providing access to the latest OSM shape files, we also provide an optional year parameter, which allows you to download OSM data from previous years.\n\n\n\n\n\n\nNote\n\n\n\nThe availability of data from previous years is dependent on what geofabrik has made available. Please check the Geofabrik download site for the list of available data.\n\n\n\npois_ph = odm.load_pois(\"philippines\", year=\"2017\")\n\n2023-02-01 17:10:30.351 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:127 - OSM Data: Cached data available for philippines at /home/butchtm/.cache/geowrangler/osm/philippines-170101-free.shp.zip? False\n2023-02-01 17:10:33.834 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:141 - OSM Data: Re-initializing OSM region zip file at /home/butchtm/.cache/geowrangler/osm/philippines-170101-free.shp.zip...\n2023-02-01 17:10:33.838 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:148 - OSM Data: Downloading Geofabrik in /home/butchtm/.cache/geowrangler/osm/philippines-170101-free.shp.zip...\n2023-02-01 17:10:35.780 | INFO     | geowrangler.datasets.utils:urlretrieve:27 - Retrieving https://download.geofabrik.de/asia/philippines-170101-free.shp.zip into /home/butchtm/.cache/geowrangler/osm/philippines-170101-free.shp.zip\n2023-02-01 17:12:24.933 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:163 - OSM Data: Successfully downloaded and cached OSM data for philippines and 2017 at /home/butchtm/.cache/geowrangler/osm/philippines-170101-free.shp.zip!\n2023-02-01 17:12:24.935 | DEBUG    | geowrangler.datasets.geofabrik:load_pois:220 - OSM POIs for philippines and year 2017 being loaded from /home/butchtm/.cache/geowrangler/osm/philippines-170101-free.shp.zip\n\n\n\n\n\n\n\n    \n      \n      100.25% [412090368/411074511 01:49<00:00]\n    \n    \n\n\n\npois_ph\n\n\n\n\n\n\n\n\n\nosm_id\ncode\nfclass\nname\ngeometry\n\n\n\n\n0\n14401658\n2742\nviewpoint\nEast Point\nPOINT (120.62020 14.38562)\n\n\n1\n14446500\n2721\nattraction\nIntramuros\nPOINT (120.97533 14.59059)\n\n\n2\n21717872\n2722\nmuseum\nAyala Museum\nPOINT (121.02324 14.55358)\n\n\n3\n24078301\n2401\nhotel\nSogo Grand Hotel\nPOINT (121.04515 14.56449)\n\n\n4\n24078959\n2907\ncamera_surveillance\nNone\nPOINT (121.05945 14.60098)\n\n\n...\n...\n...\n...\n...\n...\n\n\n67400\n4584129489\n2502\nbakery\nCome-on bakery\nPOINT (123.26424 9.19066)\n\n\n67401\n4584130190\n2401\nhotel\nL-mansion\nPOINT (121.00280 14.55968)\n\n\n67402\n4584164592\n2721\nattraction\nLugnason Falls\nPOINT (123.53406 9.14808)\n\n\n67403\n4584215391\n2742\nviewpoint\nNone\nPOINT (123.53406 9.14804)\n\n\n67404\n4584253097\n2511\nconvenience\nHerlyn's Burger House\nPOINT (120.58250 14.99107)\n\n\n\n\n67405 rows × 5 columns",
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+    "text": "Downloading Ookla Data\n\nimport geopandas as gpd\nimport pandas as pd\n\nfrom geowrangler.datasets import ookla\n\n\nListing down available files\n\nookla_files = ookla.list_ookla_files()\nookla_files\n\n{OoklaQuarter(type='fixed', year='2019', quarter='1'): '2019-01-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2019', quarter='2'): '2019-04-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2019', quarter='3'): '2019-07-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2019', quarter='4'): '2019-10-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2020', quarter='1'): '2020-01-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2020', quarter='2'): '2020-04-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2020', quarter='3'): '2020-07-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2020', quarter='4'): '2020-10-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2021', quarter='1'): '2021-01-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2021', quarter='2'): '2021-04-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2021', quarter='3'): '2021-07-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2021', quarter='4'): '2021-10-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2022', quarter='1'): '2022-01-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2022', quarter='2'): '2022-04-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2022', quarter='3'): '2022-07-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2022', quarter='4'): '2022-10-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='mobile', year='2019', quarter='1'): '2019-01-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2019', quarter='2'): '2019-04-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2019', quarter='3'): '2019-07-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2019', quarter='4'): '2019-10-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2020', quarter='1'): '2020-01-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2020', quarter='2'): '2020-04-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2020', quarter='3'): '2020-07-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2020', quarter='4'): '2020-10-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2021', quarter='1'): '2021-01-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2021', quarter='2'): '2021-04-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2021', quarter='3'): '2021-07-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2021', quarter='4'): '2021-10-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2022', quarter='1'): '2022-01-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2022', quarter='2'): '2022-04-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2022', quarter='3'): '2022-07-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2022', quarter='4'): '2022-10-01_performance_mobile_tiles.parquet'}\n\n\n\n\nDownloading an ookla file to a directory\n\n\n\n\n\n\nWarning\n\n\n\nOokla files are >300MB and can reach ~550MB. Download with caution.\n\n\n\n!mkdir -p ../test_dir\ndownloaded_file = ookla.download_ookla_file(\n    type_=\"fixed\", year=\"2019\", quarter=\"1\", directory=\"../test_dir\"\n)\ndownloaded_file\n\nPath('../test_dir/2019-01-01_performance_fixed_tiles.parquet')\n\n\n\n\nLoading ookla data\n\ndf = pd.read_parquet(downloaded_file)\ndf.head()\n\n\n\n\n\n\n\n\n\nquadkey\ntile\navg_d_kbps\navg_u_kbps\navg_lat_ms\ntests\ndevices\n\n\n\n\n0\n0022133222312322\nPOLYGON((-160.02685546875 70.6435894914449, -1...\n8763\n3646\n45\n1\n1\n\n\n1\n0022133222330013\nPOLYGON((-160.032348632812 70.6399478155463, -...\n9195\n3347\n43\n1\n1\n\n\n2\n0022133222330023\nPOLYGON((-160.043334960938 70.6363054807905, -...\n6833\n3788\n42\n1\n1\n\n\n3\n0022133222330100\nPOLYGON((-160.02685546875 70.6417687358462, -1...\n8895\n3429\n43\n2\n2\n\n\n4\n0022320121121332\nPOLYGON((-166.739501953125 68.3526207780586, -...\n4877\n935\n45\n3\n2",
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+    "text": "source\n\nload_column_config\n\n load_column_config (country:str)\n\nGet predined column mapping for some countries. The following countries area supported: - ph Philippines - tl East Timor - mm Myanmar - kh Cambodia\n\n\n\n\nType\nDetails\n\n\n\n\ncountry\nstr\n2 letter character representing the country\n\n\nReturns\ndict\n\n\n\n\n\nsource\n\n\nload_dhs_file\n\n load_dhs_file (household_data:str)\n\nLoads household data and renames columns based on variable labels of the file\n\n\n\n\nType\nDetails\n\n\n\n\nhousehold_data\nstr\nstr or pathlike object to the household data\n\n\nReturns\nDataFrame\n\n\n\n\n\nsource\n\n\napply_threshold\n\n apply_threshold (df:pandas.core.frame.DataFrame, columns:List[str],\n                  config:dict)\n\nApplies a threshold to a list of columns\n\n\n\n\nType\nDetails\n\n\n\n\ndf\nDataFrame\nDataframe\n\n\ncolumns\nList\nList of columns to apply the threshold\n\n\nconfig\ndict\nConfig containing the min and max of each columns\n\n\nReturns\nDataFrame\n\n\n\n\n\nsource\n\n\nassign_wealth_index\n\n assign_wealth_index (asset_df:pandas.core.frame.DataFrame, use_pca=True)\n\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nasset_df\nDataFrame\n\nDataframe containg only the features to apply wealth index\n\n\nuse_pca\nbool\nTrue\nif calculating wealth index should be done via PCA or via Sigular Value Decomposition",
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+    "text": "source\n\nurlretrieve\n\n urlretrieve (url, filename, headers=None, reporthook=None, timeout=None,\n              chunksize=8192)\n\nSame as urllib.request.urlretrieve but also works with Request objects\n\nsource\n\n\nmake_report_hook\n\n make_report_hook (show_progress)",
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+    "title": "Grid Generation Tutorial",
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+    "text": "A basic introduction to geospatial grids",
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+    "section": "Basic Usage",
+    "text": "Basic Usage\nCreating a grid from a geojson file\n\nImport geopandas and the geowrangler.grids module\n\nimport geopandas as gpd\nimport pandas as pd\n\nfrom geowrangler import grids\n\n\n\nLoad a sample geojson file into pandas\nIn our case, we are loading the Region 3 (Central Luzon Administrative Region) of the Philippines.\n\nregion3_gdf = gpd.read_file(\"../data/region3_admin.geojson\")\n\nThis geopandas dataframe has the size:\n\n\nlength of region3_gdf: 1 row(s)\n\n\n\ndisplay(region3_gdf)\n\n\n\n\n\n\n\n\n\nReg_Code\nReg_Name\nReg_Alt_Name\ngeometry\n\n\n\n\n0\n030000000\nRegion III\nCentral Luzon\nMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...\n\n\n\n\n\n\n\n\nShow the original plot\n\nimport matplotlib.pyplot as plt\n\n\nax = region3_gdf.plot(ax=plt.axes())\n\n\n\n\n\n\n\n\n\nregion3_gdf.crs  # CRS info\n\n<Geographic 2D CRS: EPSG:4326>\nName: WGS 84\nAxis Info [ellipsoidal]:\n- Lat[north]: Geodetic latitude (degree)\n- Lon[east]: Geodetic longitude (degree)\nArea of Use:\n- name: World.\n- bounds: (-180.0, -90.0, 180.0, 90.0)\nDatum: World Geodetic System 1984 ensemble\n- Ellipsoid: WGS 84\n- Prime Meridian: Greenwich\n\n\n\naoi_total_bounds = region3_gdf.total_bounds\naoi_total_bounds\n\narray([119.7858332,  14.4075257, 122.2429921,  16.5092548])",
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+    "text": "Square Grid Generator\n\nCreating Grids\nCreate a grid generator with a size of 5,000 m. The units of the grid size are dependent on the projection parameter\nof the grid generator. In this case, the default is EPSG:3857.\n\ngrids.SquareGridGenerator?\n\nInit signature:\ngrids.SquareGridGenerator(\n    cell_size: float,\n    grid_projection: str = 'EPSG:3857',\n    boundary: Union[geowrangler.grids.SquareGridBoundary, List[float]] = None,\n)\nDocstring:      <no docstring>\nFile:           ~/geowrangler/geowrangler/grids.py\nType:           type\nSubclasses:     \n\n\n\ngrid_generator5k = grids.SquareGridGenerator(5_000)  # 5 km x 5 km square cells\n\nGenerate square grids > Notice the time taken to grid the multi polygon at 5K resolution\n\n# slow\ngrid_gdf5k = grid_generator5k.generate_grid(region3_gdf)\n\nCPU times: user 614 ms, sys: 0 ns, total: 614 ms\nWall time: 617 ms\n\n\n\ngrid_gdf5k.plot()\n\n\n\n\n\n\n\n\nShow gridded version of sample geojson file at 5K resolution\n\n\nlength of grid_gdf5k: 1074 row(s)\n\n\n\n# slow\nax = region3_gdf.plot(facecolor=\"none\", edgecolor=\"blue\")\nax = grid_gdf5k.plot(ax=ax, facecolor=\"none\", edgecolor=\"green\")\n\n\n\n\n\n\n\n\nIn addition to the grid cells, there are 2 extra columns x and y when combined are unique per grid. It can also tell us which grids are adjacent to each other.\n\n# slow\ngrid_gdf5k.head()\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\n\n\n\n\n0\n7\n8\nPOLYGON ((120.10024 14.75528, 120.14516 14.755...\n\n\n1\n6\n8\nPOLYGON ((120.05533 14.75528, 120.10024 14.755...\n\n\n2\n9\n8\nPOLYGON ((120.19008 14.75528, 120.23499 14.755...\n\n\n3\n2\n24\nPOLYGON ((119.87566 15.44910, 119.92058 15.449...\n\n\n4\n2\n25\nPOLYGON ((119.87566 15.49239, 119.92058 15.492...\n\n\n\n\n\n\n\n\n\n# slow\nax = region3_gdf.plot(facecolor=\"none\", edgecolor=\"blue\")\nax = grid_gdf5k[grid_gdf5k[\"x\"] == 10].plot(ax=ax, facecolor=\"none\", edgecolor=\"green\")\n\n\n\n\n\n\n\n\n\n\nGridding at a coarser resolution\nLet’s try the same process but with a bigger grid (15K)\n\ngrid_generator15k = grids.SquareGridGenerator(15_000)  # 15 km x 15 km grids\n\nGenerate square grids > Notice the time taken to grid the multi polygon at 15K resolution (compared to 5K resolution)\n\ngrid_gdf15k = grid_generator15k.generate_grid(region3_gdf)\n\nCPU times: user 548 ms, sys: 0 ns, total: 548 ms\nWall time: 547 ms\n\n\n\nax = region3_gdf.plot(facecolor=\"none\", edgecolor=\"blue\")\ngrid_gdf15k.plot(ax=ax, facecolor=\"none\", edgecolor=\"green\")\n\n\n\n\n\n\n\n\nShow gridded version of sample geojson file at 15K resolution\n\n\nlength of grid_gdf15k: 154 row(s)\n\n\n\n\nReusing boundaries\nIf you have 2 polygons that are far from each other but wish to have them follow the reference same reference x and y, you can use pass in custom boudaries.\nLet’s load some grids that are from each other\n\ncell1 = grid_gdf15k.head(1)\ncell2 = grid_gdf15k.tail(1)\n\n\npd.concat([cell1, cell2])\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\n\n\n\n\n0\n2\n2\nPOLYGON ((120.05533 14.66839, 120.19008 14.668...\n\n\n153\n18\n16\nPOLYGON ((122.21128 16.48548, 122.34603 16.485...\n\n\n\n\n\n\n\n\n\nax = region3_gdf.plot(facecolor=\"none\", edgecolor=\"red\")\nax = grid_gdf15k.plot(ax=ax, facecolor=\"none\", edgecolor=\"green\")\nax = pd.concat([cell1, cell2]).plot(ax=ax)\n\n\n\n\n\n\n\n\n\ngrid_generator1k = grids.SquareGridGenerator(1_000, boundary=aoi_total_bounds)\n\n\ngridcell1 = grid_generator1k.generate_grid(cell1)\n\nCPU times: user 27.1 ms, sys: 0 ns, total: 27.1 ms\nWall time: 27.8 ms\n\n\n\nlen(gridcell1)\n\n272\n\n\n\ngridcell1.head()\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\n\n\n\n\n0\n29\n30\nPOLYGON ((120.04634 14.66839, 120.05533 14.668...\n\n\n1\n29\n31\nPOLYGON ((120.04634 14.67708, 120.05533 14.677...\n\n\n2\n29\n32\nPOLYGON ((120.04634 14.68577, 120.05533 14.685...\n\n\n3\n29\n33\nPOLYGON ((120.04634 14.69446, 120.05533 14.694...\n\n\n4\n29\n34\nPOLYGON ((120.04634 14.70315, 120.05533 14.703...\n\n\n\n\n\n\n\n\n\nax = gridcell1.plot(facecolor=\"none\", edgecolor=\"green\")\n\n\n\n\n\n\n\n\n\ngridcell2 = grid_generator1k.generate_grid(cell2)\n\nCPU times: user 20.2 ms, sys: 0 ns, total: 20.2 ms\nWall time: 18.7 ms\n\n\n\nlen(gridcell2)\n\n20\n\n\n\ngridcell2.head()\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\n\n\n\n\n0\n269\n239\nPOLYGON ((122.20230 16.47686, 122.21128 16.476...\n\n\n1\n269\n240\nPOLYGON ((122.20230 16.48548, 122.21128 16.485...\n\n\n2\n269\n241\nPOLYGON ((122.20230 16.49409, 122.21128 16.494...\n\n\n3\n269\n242\nPOLYGON ((122.20230 16.50270, 122.21128 16.502...\n\n\n4\n270\n239\nPOLYGON ((122.21128 16.47686, 122.22027 16.476...\n\n\n\n\n\n\n\n\n\nax = gridcell2.plot(facecolor=\"none\", edgecolor=\"red\")\n\n\n\n\n\n\n\n\n\nax = region3_gdf.plot(facecolor=\"none\", edgecolor=\"blue\")\nax = gridcell1.plot(ax=ax, color=\"green\")\nax = gridcell2.plot(ax=ax, color=\"red\")",
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+    "section": "Generating grids for multiple distant areas",
+    "text": "Generating grids for multiple distant areas\nIf you are using AOIs that are vary far from each other, Grid Generator already optmizes the implementation for you\n\nsparse_aois = grid_gdf15k.iloc[\n    0:1000:3,\n]  # Get areas that far from each other\nax = region3_gdf.plot(facecolor=\"none\", edgecolor=\"blue\")\nax = sparse_aois.plot(ax=ax, facecolor=\"none\", edgecolor=\"green\")\n\n\n\n\n\n\n\n\n\nsparse_grid = grid_generator1k.generate_grid(sparse_aois)\n\n\nax = region3_gdf.plot(facecolor=\"none\", edgecolor=\"blue\")\nax = sparse_grid.plot(ax=ax, facecolor=\"none\", edgecolor=\"green\")\n\n\n\n\n\n\n\n\nIt is a bit hard to see the grids so, we get a subset to verify.\n\nax = sparse_grid.head(1000).plot(facecolor=\"none\", edgecolor=\"green\")",
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+    "text": "H3 Grid Generator\n\nGenerating grids\nLet us generate grids of resolution 5. To learn more about the different resolution, visit: https://h3geo.org/docs/core-library/restable/\n\nh3_generator = grids.H3GridGenerator(resolution=5)\n\n\nh3_5_gdf = h3_generator.generate_grid(region3_gdf)\n\nCPU times: user 2.79 s, sys: 6.39 ms, total: 2.8 s\nWall time: 2.81 s\n\n\n\nax = region3_gdf.plot(aspect=\"equal\")\nax = h3_5_gdf.plot(ax=ax, facecolor=\"none\", edgecolor=\"blue\", aspect=\"equal\")\n\n\n\n\n\n\n\n\n\n\nNot exporting geometry\nIf you do not plan to use use geometry, you can pass return_geometry=False to just get a normal pandas DataFrame with the ids.\n\nh3_generator_no_geom = grids.H3GridGenerator(resolution=5, return_geometry=False)\n\n\nh3_region3_no_geom = h3_generator_no_geom.generate_grid(region3_gdf)\n\nCPU times: user 2.75 s, sys: 26.9 ms, total: 2.78 s\nWall time: 2.78 s\n\n\n\nlen(h3_region3_no_geom)\n\n84\n\n\n\nh3_region3_no_geom.head()\n\n\n\n\n\n\n\n\n\nhex_id\n\n\n\n\n0\n85694103fffffff\n\n\n1\n856941d7fffffff\n\n\n2\n85694e83fffffff\n\n\n3\n85694e93fffffff\n\n\n4\n85694387fffffff",
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+    "section": "Bing Tile Grid Generator",
+    "text": "Bing Tile Grid Generator\n\nGenerating grids\nLet us generate grids of zoom_level 12. To learn more about the different resolution, visit: https://docs.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system\n\nbing_tile_grid_generator = grids.BingTileGridGenerator(12)\n\n\n# slow\nbing_tile_gdf = bing_tile_grid_generator.generate_grid(region3_gdf)\n\nCPU times: user 7.37 s, sys: 0 ns, total: 7.37 s\nWall time: 7.37 s\n\n\n\nax = region3_gdf.plot(facecolor=\"none\", edgecolor=\"blue\")\nax = bing_tile_gdf.plot(ax=ax, facecolor=\"none\", edgecolor=\"green\")\n\n\n\n\n\n\n\n\n\n\nNot exporting geometry\nIf you do not plan to use use geometry, you can pass return_geometry=False to just get a normal pandas DataFrame with the ids.\n\nbing_tile_grid_generator_no_geom = grids.BingTileGridGenerator(\n    12, return_geometry=False\n)\n\n\n# slow\nbing_region3_keys = bing_tile_grid_generator_no_geom.generate_grid(region3_gdf)\n\nCPU times: user 7.45 s, sys: 7.19 ms, total: 7.45 s\nWall time: 7.45 s\n\n\n\nbing_region3_keys.head()\n\n\n\n\n\n\n\n\n\nquadkey\n\n\n\n\n0\n132303030330\n\n\n1\n132303030331\n\n\n2\n132303030313\n\n\n3\n132303012302\n\n\n4\n132303012031\n\n\n\n\n\n\n\n\n\n\nConverting quadkey to x,y,z format\nIf you need to convert the quadkey to x,y format, you can pass add_xyz_cols=True to add the x and y columns to the returned dataframe.\n\nbing_tile_grid_generator_add_xyz = grids.BingTileGridGenerator(12, add_xyz_cols=True)\n\n\n# slow\nbing_region3_keys = bing_tile_grid_generator_add_xyz.generate_grid(region3_gdf)\n\nCPU times: user 8.85 s, sys: 20 ms, total: 8.87 s\nWall time: 8.87 s\n\n\n\nbing_region3_keys.head()\n\n\n\n\n\n\n\n\n\nquadkey\nx\ny\nz\ngeometry\n\n\n\n\n0\n132303030330\n3414\n1878\n12\nPOLYGON ((120.05859 14.68988, 120.05859 14.774...\n\n\n1\n132303030331\n3415\n1878\n12\nPOLYGON ((120.14648 14.68988, 120.14648 14.774...\n\n\n2\n132303030313\n3415\n1877\n12\nPOLYGON ((120.14648 14.77488, 120.14648 14.859...\n\n\n3\n132303012302\n3412\n1869\n12\nPOLYGON ((119.88281 15.45368, 119.88281 15.538...\n\n\n4\n132303012031\n3411\n1866\n12\nPOLYGON ((119.79492 15.70766, 119.79492 15.792...",
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+    "text": "A basic introduction to raster processing",
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+    "section": "Summary",
+    "text": "Summary\nCropping rasters based on the borders of a polygon. Usage of this function assumes an input raster and a geodataframe from which to extract bounds. There is also an option to crop multiple geometries at once (e.g., crop raster using bounds of each cell in a grid).",
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+    "text": "How does it work?\nReturns a subset of a rasterio dataset. This function assumes that the bounding coordinates for the desired cropped rasters are present in the input geodataframe’s geometry column.\n\nquery_window_by_polygon(raster, output_folder, gdf, mask)\nFor cropping a raster based on boundaries of a single polygon. A technical step-by-step explanation of how query_window_by_polygon works is detailed in the cell blocks below.\n\n\n\n\n\n\n\n\n\n\n\ntype\ndefault\noptional/ required\ndetails\n\n\n\n\nraster\nString\nnone\nrequired\nlocal filename of raster or open raster dataset\n\n\noutput_folder\nString\nnone\nrequired\nfile path where cropped raster outputs will be saved\n\n\ngdf\nGeoDataFrame\nnone\nrequired\npolygon that will become basis of boundaries for cropping\n\n\nmask\nBoolean\nnone\nrequired\nTrue- Assign NULL to areas outside borders of a non-rectangular polygon  False- Retain values outside borders of a non-rectangular polygon\n\n\n\n\nDefine the function and its arguments.\n\ndef query_window_by_polygon(\n    input_raster: Union[str, DatasetReader, PosixPath],\n    output_path: str,\n    geometry: Polygon,\n    mask=False,\n) -> None:\n\nCheck if raster is specified as PosixPath and convert to String.\n\n    if isinstance(input_raster, PosixPath):\n        input_raster = str(input_raster)\n\nCheck if raster is path to a file or an open raster dataset.\n\n    if isinstance(input_raster,str):\n         input_dset = rio.open(input_raster)\n    else:\n         input_dset = input_raster\n\nGet the window bounds (left, right, top, bottom coordinates) from polygon geometry and check if it has the correct number of elements.\n\n    window_bounds = geometry.bounds\n    assert (\n        len(window_bounds) == 4\n    ), \n\nUnroll window bounds.\n\n    left, bottom, right, top = window_bounds\n\nOpen raster as input.\n\n    with rio.open(input_raster) as input_dst:\n\nGet profile of input_dst:\n\n        input_profile = input_dst.profile\n\nSpecify window and query subset.\n\n        window = rio.windows.from_bounds(left, bottom, right, top, input_dst.transform)\n        subset = input_dst.read(window=window)\n\nGet the shape of the output subset.\n\n        number_of_bands, height, width = subset.shape\n\nGet the transformation of the subset based on the window.\n\n        win_transform = input_dst.window_transform(window)\n\nUpdate metadata for the output.\n\n        output_profile = input_profile.copy()\n        update_params = {\n            \"count\": number_of_bands,\n            \"height\": height,\n            \"width\": width,\n            \"transform\": win_transform,\n        }\n        output_profile.update(update_params)\n\nWrite image to output file.\n\n        with rio.open(output_path, \"w\", **output_profile) as output_dst:\n            output_dst.write(subset)\n            output_dst.colorinterp = input_dst.colorinterp\n\nApply mask according to shape of the polygon.\n\n    if mask:\n        with rio.open(output_path) as dst:\n            masked_image, masked_transform = rio.mask.mask(dst, [geometry], crop=True)\n        with rio.open(output_path, \"w\", **output_profile) as output_dst:\n            update_params = {\n                \"height\": masked_image.shape[1],\n                \"width\": masked_image.shape[2],\n                \"transform\": masked_transform,\n            }\n            output_profile.update(update_params)\n            output_dst.write(masked_image)\n\nReturn output.\n\n    return \n\n\nquery_window_by_gdf(raster, output_folder, gdf, name_col, mask)\nFor cropping a raster based on boundaries of multiple polygons. A step-by-step explanation of how query_window_by_gdf works is detailed in the cell blocks below.\n\n\n\n\n\n\n\n\n\n\n\ntype\ndefault\noptional/ required\ndetails\n\n\n\n\nraster\nString\nnone\nrequired\nlocal filename of raster or open raster dataset\n\n\noutput_folder\nString\nnone\nrequired\nfile path where cropped raster outputs will be saved\n\n\ngdf\nGeoDataFrame\nnone\nrequired\npolygon that will become basis of boundaries for cropping\n\n\nname_col\nString\nnone\noptional\ncolumn name to base output filepath on. if left blank, outputs will be name sequentially as ‘output_0.tif’\n\n\nmask\nBoolean\nnone\nrequired\nTrue- Assign NULL to areas outside borders of a non-rectangular polygon  False- Retain values outside borders of a non-rectangular polygon\n\n\n\n\nDefine the function and create a copy of the geodataframe.\n\ndef query_window_by_gdf(\n    input_raster: Union[str, DatasetReader, PosixPath],\n    output_folder: str,\n    gdf,\n    name_col=None,\n    mask=False,\n) -> None:\n    gdf = gdf.copy()\n\nCheck if the coordinate reference systems of the raster and geodataframe match.\n\n    with rio.open(input_raster) as dst:\n        assert dst.meta[\"crs\"] == gdf.crs, \"input_raster and gdf CRS must match!\"\n\nName outputs based on values from name_col.\n\n    if name_col is None:\n        name_col = \"name\"\n        gdf[name_col] = \"output_\" + gdf.reset_index().index.astype(str) + \".tif\"\n    else:\n        gdf[name_col] = gdf[name_col] + \".tif\"\n\n    for i, row in gdf.iterrows():\n        polygon = row.geometry\n        output_name = row[name_col]\n        output_path = output_folder / output_name\n        print(output_path)\n        query_window_by_polygon(input_raster, output_path, polygon, mask)",
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+    "title": "Raster Processing Tutorial",
+    "section": "Sample use case 1- Crop raster with single circular polygon",
+    "text": "Sample use case 1- Crop raster with single circular polygon\nInput: - input_image - raster of 2020 Philippine population - output_folder - filepath to where output raster will be saved - circle_gdf - GeoDataFrame containining single circular polygon - mask - True- to assign NULL values to cells outside the circular polygon\nOutput: - cropped raster (GeoTiff) containing only values within circular polygon\n\nwarnings.filterwarnings(\"ignore\", category=UserWarning, module=\"geopandas\")\n\n\nStep 1: Import packages\n\nimport rasterio as rio\n\nimport geowrangler.raster_process as raster_process\n\n\n\nStep 2: Load raster\n\ninput_image\n\n'../data/phl_ppp_2020_constrained.tif'\n\n\n\nraster = rio.open(input_image)\n\n\nshow(raster.read(1), cmap=\"plasma\", transform=raster.transform);\n\n\n\n\n\n\n\n\n\n\nStep 3: Load polygon\n\ncircle_gdf\n\n\n\n\n\n\n\n\n\nlat\nlon\ngeometry\n\n\n\n\n0\n14.599512\n120.984222\nPOLYGON ((121.98422 14.59951, 121.97941 14.501...\n\n\n\n\n\n\n\n\n\n\nStep 4: Check if the polygon is within bounds of the raster.\n\nfig, ax = plt.subplots(1, 1, figsize=(4, 8))\nshow(raster.read(1), cmap=\"viridis\", ax=ax, transform=raster.transform)\ncircle_gdf.plot(ax=ax, facecolor=\"none\", edgecolor=\"yellow\")\nax;\n\n\n\n\n\n\n\n\n\n\nStep 5: State filepath of output folder.\n\noutput_folder = Path(\"../data\")\n\n\n\nStep 6: Crop raster\n\nraster_process.query_window_by_gdf(input_image, output_folder, circle_gdf, mask=True)\n\n../data/output_0.tif\n\n\n\nwith rio.open(output_folder / \"output_0.tif\") as dst:\n    fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n\n    show(dst.read(1), cmap=\"viridis\", ax=ax, transform=dst.transform)\n    circle_gdf.plot(facecolor=\"none\", edgecolor=\"yellow\", ax=ax)\n    print(dst.read(1))\nax;\n\n[[-99999. -99999. -99999. ... -99999. -99999. -99999.]\n [-99999. -99999. -99999. ... -99999. -99999. -99999.]\n [-99999. -99999. -99999. ... -99999. -99999. -99999.]\n ...\n [-99999. -99999. -99999. ... -99999. -99999. -99999.]\n [-99999. -99999. -99999. ... -99999. -99999. -99999.]\n [-99999. -99999. -99999. ... -99999. -99999. -99999.]]",
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+    "title": "Raster Processing Tutorial",
+    "section": "Sample use case 2- Crop raster with multiple cells from a grid",
+    "text": "Sample use case 2- Crop raster with multiple cells from a grid\nInput: - input_image - raster of 2020 Philippine population - output_folder - filepath to where output rasters will be saved - grid_gdf - GeoDataFrame containining multiple polygons (grid cells) - mask - False- no need to assign NULL values because grid cells occupy full space from image center to boundaries\nOutput: - multiple cropped rasters (GeoTiff) split according to grid cell polygons\n\nStep 1: Import packages\n\nimport rasterio as rio\n\nimport geowrangler.raster_process\n\n\n\nStep 2: Load raster\n\ninput_image\n\n'../data/phl_ppp_2020_constrained.tif'\n\n\n\nraster = rio.open(input_image)\nshow(raster.read(1), cmap=\"plasma\", transform=raster.transform)\n\n\n\n\n\n\n\n\n\n\nStep 3: Load grid\n\ngrid_gdf\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\nname\n\n\n\n\n0\n0\n0\nPOLYGON ((119.98422 13.59951, 120.88254 13.599...\ngridxy-0-0\n\n\n1\n0\n1\nPOLYGON ((119.98422 14.47100, 120.88254 14.471...\ngridxy-0-1\n\n\n2\n0\n2\nPOLYGON ((119.98422 15.33908, 120.88254 15.339...\ngridxy-0-2\n\n\n3\n1\n0\nPOLYGON ((120.88254 13.59951, 121.78085 13.599...\ngridxy-1-0\n\n\n4\n1\n1\nPOLYGON ((120.88254 14.47100, 121.78085 14.471...\ngridxy-1-1\n\n\n5\n1\n2\nPOLYGON ((120.88254 15.33908, 121.78085 15.339...\ngridxy-1-2\n\n\n6\n2\n0\nPOLYGON ((121.78085 13.59951, 122.67917 13.599...\ngridxy-2-0\n\n\n7\n2\n1\nPOLYGON ((121.78085 14.47100, 122.67917 14.471...\ngridxy-2-1\n\n\n\n\n\n\n\n\n\n\nStep 4: Check if the grid is within bounds of the raster.\n\nfig, ax = plt.subplots(1, 1, figsize=(4, 8))\nshow(raster.read(1), cmap=\"viridis\", ax=ax, transform=raster.transform)\ngrid_gdf.plot(ax=ax, facecolor=\"none\", edgecolor=\"yellow\")\nax\n\n\n\n\n\n\n\n\n\n\nStep 5: State filepath of output folder.\n\noutput_folder = Path(\"../data\")\n\n\n\nStep 6: Crop raster\n\nraster_process.query_window_by_gdf(\n    input_image, output_folder, grid_gdf, name_col=\"name\", mask=False\n)\n\n../data/gridxy-0-0.tif\n../data/gridxy-0-1.tif\n../data/gridxy-0-2.tif\n../data/gridxy-1-0.tif\n../data/gridxy-1-1.tif\n../data/gridxy-1-2.tif\n../data/gridxy-2-0.tif\n../data/gridxy-2-1.tif\n\n\n\nfor name in grid_gdf[\"name\"]:\n    image_path = output_folder / (name + \".tif\")\n    with rio.open(image_path) as dst:\n        fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n        ax.set_title(image_path)\n        show(dst.read(1), cmap=\"viridis\", ax=ax, transform=dst.transform)",
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+    "text": "from rasterio import features\nfrom rasterio.plot import show\nfrom rasterio.windows import Window, transform",
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+    "text": "Test data\n\nGenerating a raster mask\n\n# Get filepaths\ntiff_file = \"../data/vector_to_raster_mask_sample/cabanglasan.tif\"\nshape_file = \"../data/vector_to_raster_mask_sample/labels_20220816.gpkg\"\ntarget_file = shape_file.replace(\"gpkg\", \"tiff\")\n\nGiven a raster image of a certain area that will be masked to use as a reference and a shape file that contains that area. Note that the shape file must include a column that contains labels/categories.\n\ngpd.read_file(shape_file).head(3)\n\n\n\n\n\n\n\n\n\nyear\nlabel\nuid\nADM3_EN\nADM3_PCODE\nADM2_EN\nADM2_PCODE\nADM1_EN\nADM1_PCODE\ngeometry\n\n\n\n\n0\n2017.0\nmining\n72_2017_mining\nSofronio Española\nPH175324000\nPalawan\nPH175300000\nRegion IV-B\nPH170000000\nMULTIPOLYGON Z (((117.95961 9.03303 0.00000, 1...\n\n\n1\n2017.0\nmining\n71_2017_mining\nSofronio Española\nPH175324000\nPalawan\nPH175300000\nRegion IV-B\nPH170000000\nMULTIPOLYGON Z (((117.95507 9.03809 0.00000, 1...\n\n\n2\n2017.0\nmining\n70_2017_mining\nSofronio Española\nPH175324000\nPalawan\nPH175300000\nRegion IV-B\nPH170000000\nMULTIPOLYGON Z (((117.95663 9.03869 0.00000, 1...\n\n\n\n\n\n\n\n\nAnd a dictionary with your desired labels and assigned values in creating a mask\n\nlabels = {\n    \"mining\": 1,\n    \"neg\": 2,\n    \"agriculture\": 3,\n    \"product_extraction\": 4,\n    \"kaingin\": 5,\n    \"biophysical\": 6,\n}\n\nInput them in the generate_mask function to create a raster mask of the same dimension as the reference raster image\n\n# Generate masks for a file\nmasks, grids, values = generate_mask(\n    tiff_file=tiff_file,\n    shape_file=shape_file,\n    output_file=target_file,\n    labels_column=\"label\",\n    labels_dict=labels,\n    plot=True,\n)\n\n\n\n\n\n\n\n\n\nmasks\n\narray([[0, 0, 0, ..., 0, 0, 0],\n       [0, 0, 0, ..., 0, 0, 0],\n       [0, 0, 0, ..., 0, 0, 0],\n       ...,\n       [0, 0, 0, ..., 0, 0, 0],\n       [0, 0, 0, ..., 0, 0, 0],\n       [0, 0, 0, ..., 0, 0, 0]], dtype=uint16)\n\n\n\ngrids\n\narray([[0, 0, 0, ..., 0, 0, 0],\n       [0, 0, 0, ..., 0, 0, 0],\n       [0, 0, 0, ..., 0, 0, 0],\n       ...,\n       [0, 0, 0, ..., 0, 0, 0],\n       [0, 0, 0, ..., 0, 0, 0],\n       [0, 0, 0, ..., 0, 0, 0]], dtype=uint16)\n\n\n\nvalues\n\n{'mining': 1,\n 'neg': 2,\n 'agriculture': 3,\n 'product_extraction': 4,\n 'kaingin': 5,\n 'biophysical': 6}\n\n\nChoose raster images to convert into a dataframe and use the generated raster mask to add the labels the converted dataframe.\n\ntiff_files = [\"../data/vector_to_raster_mask_sample/cabanglasan.tif\"]\nmask_file = \"../data/vector_to_raster_mask_sample/labels_20220816.tiff\"\n\n\ndata = read_bands(tiff_files, mask_file)\n\n\ndata\n\n\n\n\n\n\n\n\n\nB1_0\nB2_0\nB3_0\nB4_0\nB5_0\nB6_0\nB7_0\nB8_0\nB9_0\nB10_0\nB11_0\nB12_0\nlabel\n\n\n\n\n0\n0.1198\n0.09635\n0.09330\n0.0698\n0.10665\n0.20250\n0.2490\n0.23525\n0.28125\n0.0377\n0.19925\n0.1002\n0\n\n\n1\n0.1198\n0.09580\n0.09245\n0.0708\n0.10665\n0.20250\n0.2490\n0.23925\n0.28125\n0.0377\n0.19925\n0.1002\n0\n\n\n2\n0.1148\n0.09420\n0.09460\n0.0707\n0.10380\n0.20395\n0.2478\n0.23150\n0.27165\n0.0385\n0.18240\n0.0902\n0\n\n\n3\n0.1148\n0.09190\n0.08850\n0.0631\n0.10380\n0.20395\n0.2478\n0.23300\n0.27165\n0.0385\n0.18240\n0.0902\n0\n\n\n4\n0.1148\n0.09350\n0.09080\n0.0643\n0.10565\n0.20830\n0.2466\n0.24205\n0.26990\n0.0385\n0.18050\n0.0894\n0\n\n\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n\n\n775824\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.0000\n0\n\n\n775825\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.0000\n0\n\n\n775826\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.0000\n0\n\n\n775827\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.0000\n0\n\n\n775828\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.0000\n0\n\n\n\n\n775829 rows × 13 columns",
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+    "text": "source\n\nSquareGridBoundary\n\n SquareGridBoundary (x_min:float, y_min:float, x_max:float, y_max:float)\n\nReusing Boundary. x_min, y_min, x_max, and y_max are in the the target crs\n\nsource\n\n\nSquareGridGenerator\n\n SquareGridGenerator (cell_size:float, grid_projection:str='EPSG:3857',\n                      boundary:Union[__main__.SquareGridBoundary,List[floa\n                      t]]=None)\n\nInitialize self. See help(type(self)) for accurate signature.\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\ncell_size\nfloat\n\nheight and width of a square cell in meters\n\n\ngrid_projection\nstr\nEPSG:3857\nprojection of grid output\n\n\nboundary\nUnion\nNone\noriginal boundary\n\n\n\n\nsource\n\n\nSquareGridGenerator.create_cell\n\n SquareGridGenerator.create_cell (x:float, y:float)\n\nCreate a square cell based on the top left coordinates and cell_size\n\n\n\n\nType\nDetails\n\n\n\n\nx\nfloat\nx coord of top left\n\n\ny\nfloat\ny coord of top left\n\n\nReturns\nPolygon\n\n\n\n\n\nsource\n\n\nSquareGridGenerator.create_grid_for_polygon\n\n SquareGridGenerator.create_grid_for_polygon (boundary, geometry)\n\n\nsource\n\n\nSquareGridGenerator.generate_grid\n\n SquareGridGenerator.generate_grid\n                                    (gdf:geopandas.geodataframe.GeoDataFra\n                                    me)\n\n\nsource\n\n\nH3GridGenerator\n\n H3GridGenerator (resolution:int, return_geometry:bool=True)\n\nInitialize self. See help(type(self)) for accurate signature.\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nresolution\nint\n\nResolution of hexagon. See: https://h3geo.org/docs/core-library/restable/ for more info\n\n\nreturn_geometry\nbool\nTrue\nIf geometry should be returned. Setting this to false will only return hex_ids\n\n\n\n\nsource\n\n\nH3GridGenerator.get_hexes_for_polygon\n\n H3GridGenerator.get_hexes_for_polygon\n                                        (poly:shapely.geometry.polygon.Pol\n                                        ygon)\n\n\nsource\n\n\nH3GridGenerator.generate_grid\n\n H3GridGenerator.generate_grid (gdf:geopandas.geodataframe.GeoDataFrame)\n\n\nsource\n\n\nBingTileGridGenerator\n\n BingTileGridGenerator (zoom_level:int, return_geometry:bool=True,\n                        add_xyz_cols:bool=False)\n\nInitialize self. See help(type(self)) for accurate signature.\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nzoom_level\nint\n\nZoom level of tile. See: https://docs.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system for more info\n\n\nreturn_geometry\nbool\nTrue\nIf geometry should be returned. Setting this to false will only return quadkeys\n\n\nadd_xyz_cols\nbool\nFalse\nIf quadkey should be converted to their xy values.\n\n\n\n\nsource\n\n\nBingTileGridGenerator.get_all_tiles_for_polygon\n\n BingTileGridGenerator.get_all_tiles_for_polygon\n                                                  (polygon:shapely.geometr\n                                                  y.polygon.Polygon)\n\nGet the interseting tiles with polygon for a zoom level. Polygon should be in EPSG:4326\n\nsource\n\n\nBingTileGridGenerator.generate_grid\n\n BingTileGridGenerator.generate_grid\n                                      (gdf:geopandas.geodataframe.GeoDataF\n                                      rame)\n\n\nsource\n\n\nBingTileGridGenerator.generate_grid_join\n\n BingTileGridGenerator.generate_grid_join\n                                           (gdf:geopandas.geodataframe.Geo\n                                           DataFrame, filter:bool=True,\n                                           n_workers=4, progress=True)",
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+    "title": "Area Zonal Stats",
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+    "text": "Test data\n\nSimple squares\nGiven an aoi (simple_aoi) and geodataframe containing sample data (simple_data)\n\nsimple_aoi\n\n\n\n\n\n\n\n\n\ngeometry\n\n\n\n\n0\nPOLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...\n\n\n1\nPOLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...\n\n\n2\nPOLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...\n\n\n3\nPOLYGON ((3.000 0.000, 3.000 1.000, 4.000 1.00...\n\n\n4\nPOLYGON ((4.000 0.000, 4.000 1.000, 5.000 1.00...\n\n\n\n\n\n\n\n\n\nsimple_data\n\n\n\n\n\n\n\n\n\ngeometry\npopulation\ninternet_speed\n\n\n\n\n0\nPOLYGON ((0.250 0.000, 0.250 1.000, 1.250 1.00...\n100\n20.0\n\n\n1\nPOLYGON ((1.250 0.000, 1.250 1.000, 2.250 1.00...\n200\n10.0\n\n\n2\nPOLYGON ((2.250 0.000, 2.250 1.000, 3.250 1.00...\n300\n5.0\n\n\n\n\n\n\n\n\n\nax = plt.axes()\nax = simple_data.plot(\n    ax=ax, color=[\"orange\", \"brown\", \"purple\"], edgecolor=\"yellow\", alpha=0.4\n)\nax = simple_aoi.plot(\n    ax=ax, facecolor=\"none\", edgecolor=[\"r\", \"g\", \"b\", \"orange\", \"purple\"]\n)\n\n\n\n\n\n\n\n\nThe red,green,blue, orange and purple outlines are the 5 regions of interest (aoi) while the orange,brown, purple areas are the data areas.\n\nempty_aoi_results = create_area_zonal_stats(simple_aoi, simple_data)\n\n\nempty_aoi_results\n\n\n\n\n\n\n\n\n\ngeometry\nintersect_area_sum\n\n\n\n\n0\nPOLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...\n0.75\n\n\n1\nPOLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...\n1.00\n\n\n2\nPOLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...\n1.00\n\n\n3\nPOLYGON ((3.000 0.000, 3.000 1.000, 4.000 1.00...\n0.25\n\n\n4\nPOLYGON ((4.000 0.000, 4.000 1.000, 5.000 1.00...\n0.00\n\n\n\n\n\n\n\n\n\nsimple_aoi_results = create_area_zonal_stats(\n    simple_aoi,\n    simple_data,\n    [\n        dict(func=\"count\", output=\"sample_count\"),\n        dict(func=[\"sum\", \"count\"], column=\"population\"),\n        dict(func=[\"mean\", \"max\", \"min\", \"std\"], column=\"internet_speed\"),\n    ],\n)\n\nCPU times: user 61.4 ms, sys: 0 ns, total: 61.4 ms\nWall time: 58.1 ms\n\n\n\nsimple_aoi_results\n\n\n\n\n\n\n\n\n\ngeometry\nintersect_area_sum\nsample_count\npopulation_sum\npopulation_count\ninternet_speed_mean\ninternet_speed_max\ninternet_speed_min\ninternet_speed_std\n\n\n\n\n0\nPOLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...\n0.75\n1.0\n75.0\n1.0\n15.000\n20.0\n0.0\nNaN\n\n\n1\nPOLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...\n1.00\n2.0\n175.0\n2.0\n6.250\n20.0\n10.0\n7.071068\n\n\n2\nPOLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...\n1.00\n2.0\n275.0\n2.0\n3.125\n10.0\n5.0\n3.535534\n\n\n3\nPOLYGON ((3.000 0.000, 3.000 1.000, 4.000 1.00...\n0.25\n1.0\n75.0\n1.0\n1.250\n5.0\n0.0\nNaN\n\n\n4\nPOLYGON ((4.000 0.000, 4.000 1.000, 5.000 1.00...\n0.00\nNaN\nNaN\nNaN\nNaN\nNaN\n0.0\nNaN\n\n\n\n\n\n\n\n\n\nsimple_aoi_results.population_sum.sum(axis=None)\n\n600.0\n\n\n\ncorrected_aoi_results = create_area_zonal_stats(\n    simple_aoi,\n    simple_data,\n    [\n        dict(func=[\"sum\", \"count\"], column=\"population\"),\n        dict(\n            func=[\"mean\", \"imputed_mean\", \"raw_max\", \"raw_min\", \"raw_std\"],\n            column=\"internet_speed\",\n            output=[\n                \"internet_speed_mean\",\n                \"internet_speed_imputed_mean\",\n                \"internet_speed_max\",\n                \"internet_speed_min\",\n                \"internet_speed_std\",\n            ],\n        ),\n    ],\n    fix_min=False,\n)\n\nCPU times: user 53.9 ms, sys: 956 µs, total: 54.9 ms\nWall time: 50.1 ms\n\n\n\ncorrected_aoi_results\n\n\n\n\n\n\n\n\n\ngeometry\nintersect_area_sum\npopulation_sum\npopulation_count\ninternet_speed_mean\ninternet_speed_imputed_mean\ninternet_speed_max\ninternet_speed_min\ninternet_speed_std\n\n\n\n\n0\nPOLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...\n0.75\n75.0\n1.0\n15.000\n20.000\n20.0\n20.0\nNaN\n\n\n1\nPOLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...\n1.00\n175.0\n2.0\n6.250\n6.250\n20.0\n10.0\n7.071068\n\n\n2\nPOLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...\n1.00\n275.0\n2.0\n3.125\n3.125\n10.0\n5.0\n3.535534\n\n\n3\nPOLYGON ((3.000 0.000, 3.000 1.000, 4.000 1.00...\n0.25\n75.0\n1.0\n1.250\n5.000\n5.0\n5.0\nNaN\n\n\n4\nPOLYGON ((4.000 0.000, 4.000 1.000, 5.000 1.00...\n0.00\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n\n\n\n\n\n\n\n\n\naois_no_nas = create_area_zonal_stats(\n    simple_aoi,\n    simple_data,\n    [\n        dict(func=[\"sum\", \"count\"], column=\"population\", fillna=[True, True]),\n        dict(\n            func=[\"mean\", \"imputed_mean\", \"raw_max\", \"raw_min\", \"raw_std\"],\n            column=\"internet_speed\",\n            output=[\n                \"internet_speed_mean\",\n                \"internet_speed_imputed_mean\",\n                \"internet_speed_max\",\n                \"internet_speed_min\",\n                \"internet_speed_std\",\n            ],\n            fillna=[True, True, True, True, True],\n        ),\n    ],\n    fix_min=False,\n)\n\nCPU times: user 34 ms, sys: 12.1 ms, total: 46 ms\nWall time: 42.6 ms\n\n\n\naois_no_nas\n\n\n\n\n\n\n\n\n\ngeometry\nintersect_area_sum\npopulation_sum\npopulation_count\ninternet_speed_mean\ninternet_speed_imputed_mean\ninternet_speed_max\ninternet_speed_min\ninternet_speed_std\n\n\n\n\n0\nPOLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...\n0.75\n75.0\n1.0\n15.000\n20.000\n20.0\n20.0\n0.000000\n\n\n1\nPOLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...\n1.00\n175.0\n2.0\n6.250\n6.250\n20.0\n10.0\n7.071068\n\n\n2\nPOLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...\n1.00\n275.0\n2.0\n3.125\n3.125\n10.0\n5.0\n3.535534\n\n\n3\nPOLYGON ((3.000 0.000, 3.000 1.000, 4.000 1.00...\n0.25\n75.0\n1.0\n1.250\n5.000\n5.0\n5.0\n0.000000\n\n\n4\nPOLYGON ((4.000 0.000, 4.000 1.000, 5.000 1.00...\n0.00\n0.0\n0.0\n0.000\n0.000\n0.0\n0.0\n0.000000",
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+    "title": "Datasets Ookla",
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+    "text": "source\n\nlist_ookla_files\n\n list_ookla_files ()\n\nGet list of ookla data\n\nookla_files = list_ookla_files()\nassert ookla_files.get(OoklaFile(\"fixed\", \"2021\", \"2\"), None) is not None\n\n\nsource\n\n\ndownload_ookla_file\n\n download_ookla_file (type_:str, year:str, quarter:str,\n                      directory:str='data/', overwrite:bool=False,\n                      show_progress=True, chunksize=8192, reporthook=None)\n\nDownload ookla file to path\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\ntype_\nstr\n\nInternet connection type: ‘fixed’ or ‘mobile’\n\n\nyear\nstr\n\nYear (e.g. ‘2020’)\n\n\nquarter\nstr\n\nQuarter (valid values: ‘1’,‘2’,‘3’,‘4’)\n\n\ndirectory\nstr\ndata/\nDownload directory\n\n\noverwrite\nbool\nFalse\nOverwrite if existing\n\n\nshow_progress\nbool\nTrue\nshow progres bar\n\n\nchunksize\nint\n8192\nDownload chunksize\n\n\nreporthook\nNoneType\nNone\nUse custom progress bar\n\n\nReturns\nOptional\n\n\n\n\n\n\nsource\n\n\nparallel_download\n\n parallel_download (item)\n\n\nsource\n\n\ndownload_ookla_parallel\n\n download_ookla_parallel (num_expected_ookla_files, type_, year,\n                          directory, overwrite, show_progress, chunksize,\n                          reporthook)\n\n\nsource\n\n\ndownload_ookla_year_data\n\n download_ookla_year_data (type_, year, cache_dir, use_cache=True,\n                           show_progress=True, chunksize=8192,\n                           reporthook=None)\n\nDownload ookla data for a specifed type (fixed or mobile) and year. Data for all 4 quarters will be downloaded.\n\nsource\n\n\nlookup_ookla_file\n\n lookup_ookla_file (filename)\n\nGet OoklaFile for the given filename\n\nassert lookup_ookla_file(\"2021-04-01_performance_fixed_tiles.parquet\") == OoklaFile(\n    \"fixed\", \"2021\", \"2\"\n)\n\n\nsource\n\n\ncompute_datakey\n\n compute_datakey (aoi_bounds, type_, year, return_geometry)\n\n\nsource\n\n\nwrite_ookla_metajson\n\n write_ookla_metajson (cache_dir, data_key, total_bounds, type_, year,\n                       return_geometry)\n\n\nsource\n\n\nOoklaDataManager\n\n OoklaDataManager (cache_dir='~/.cache/geowrangler')\n\nAn instance of this class provides convenience functoins for loading and caching Ookla data\n\nsource\n\n\nOoklaDataManager.reinitialize_processed_cache\n\n OoklaDataManager.reinitialize_processed_cache ()\n\nReinitialize processed_cache_dir to start over from scratch.\n\nsource\n\n\nOoklaDataManager.reinitialize_aggregated_cache\n\n OoklaDataManager.reinitialize_aggregated_cache ()\n\nReinitialize aggregated_cache_dir to start over from scratch.\n\nsource\n\n\nOoklaDataManager.load_type_year_data\n\n OoklaDataManager.load_type_year_data\n                                       (aoi:geopandas.geodataframe.GeoData\n                                       Frame, type_:str, year:str,\n                                       use_cache=True,\n                                       return_geometry=False,\n                                       show_progress=True, chunksize=8192,\n                                       reporthook=None)\n\nLoad Ookla data across all quarters for a specified aoi, type (fixed, mobile) and year\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\naoi\nGeoDataFrame\n\narea of interest\n\n\ntype_\nstr\n\nookla data type: fixed or mobile\n\n\nyear\nstr\n\nyear\n\n\nuse_cache\nbool\nTrue\nuse cache dir\n\n\nreturn_geometry\nbool\nFalse\ninclude geometry in returned values\n\n\nshow_progress\nbool\nTrue\ndisplay progress bar\n\n\nchunksize\nint\n8192\ndownload buffer size\n\n\nreporthook\nNoneType\nNone\ncustom progress bar\n\n\n\n\nsource\n\n\nOoklaDataManager.aggregate_ookla_features\n\n OoklaDataManager.aggregate_ookla_features\n                                            (aoi:geopandas.geodataframe.Ge\n                                            oDataFrame, type_:str,\n                                            year:str, use_cache=True,\n                                            return_geometry=False,\n                                            output_crs='epsg:4326', aggreg\n                                            ations:Dict[str,Any]={'mean_av\n                                            g_d_kbps': ('avg_d_kbps',\n                                            'mean'), 'mean_avg_u_kbps':\n                                            ('avg_u_kbps', 'mean'),\n                                            'mean_avg_lat_ms':\n                                            ('avg_lat_ms', 'mean'),\n                                            'mean_num_tests': ('tests',\n                                            'mean'), 'mean_num_devices':\n                                            ('devices', 'mean')},\n                                            show_progress=True,\n                                            chunksize=8192,\n                                            reporthook=None)\n\nGenerates yearly aggregate features for the AOI based on Ookla data for a given type (fixed, mobile) and year.\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\naoi\nGeoDataFrame\n\nArea of interest\n\n\ntype_\nstr\n\nOokla speed type: ’fixedormobile`\n\n\nyear\nstr\n\nYear to aggregate (over 4 quarters)\n\n\nuse_cache\nbool\nTrue\nUse cached data in cache dir as specified in ookla_data_manager\n\n\nreturn_geometry\nbool\nFalse\nSave aggregated data as geojson\n\n\noutput_crs\nstr\nepsg:4326\ncrs to use in creating aggregated geodataframe\n\n\naggregations\nDict\n{‘mean_avg_d_kbps’: (‘avg_d_kbps’, ‘mean’), ‘mean_avg_u_kbps’: (‘avg_u_kbps’, ‘mean’), ‘mean_avg_lat_ms’: (‘avg_lat_ms’, ‘mean’), ‘mean_num_tests’: (‘tests’, ‘mean’), ‘mean_num_devices’: (‘devices’, ‘mean’)}\nAggregation functions on ookla data (see https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.agg.html)\n\n\nshow_progress\nbool\nTrue\ndisplay progress bar\n\n\nchunksize\nint\n8192\ndownload buffer size\n\n\nreporthook\nNoneType\nNone\ncustom progress bar",
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+    "title": "Raster Processing",
+    "section": "Download raster",
+    "text": "Download raster\n\ninput_image = \"../data/phl_ppp_2020_constrained.tif\"\n\n\nraster = rio.open(input_image)\nraster.meta\n\n{'driver': 'GTiff',\n 'dtype': 'float32',\n 'nodata': -99999.0,\n 'width': 11613,\n 'height': 19781,\n 'count': 1,\n 'crs': CRS.from_epsg(4326),\n 'transform': Affine(0.0008333333299750276, 0.0, 116.927916214,\n        0.0, -0.0008333333300136493, 21.070416784)}\n\n\n\nshow(raster.read(1), cmap=\"plasma\", transform=raster.transform)",
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+    "title": "Raster Processing",
+    "section": "Define bounding box",
+    "text": "Define bounding box\n\nbbox = (120.888062, 14.394778, 121.199112, 14.705822)\n\n\ncircle_gdf\n\n\n\n\n\n\n\n\n\nlat\nlon\ngeometry\n\n\n\n\n0\n14.599512\n120.984222\nPOLYGON ((121.98422 14.59951, 121.97941 14.501...\n\n\n\n\n\n\n\n\n\nprint(circle_gdf.crs)\n\nepsg:4326\n\n\n\nfig, ax = plt.subplots(1, 1, figsize=(4, 8))\nshow(raster.read(1), cmap=\"viridis\", ax=ax, transform=raster.transform)\ncircle_gdf.plot(ax=ax, facecolor=\"none\", edgecolor=\"yellow\")\n\nax",
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+    "section": "Crop a raster without masking",
+    "text": "Crop a raster without masking\n\noutput_folder = Path(\"../data\")\n\n\nquery_window_by_gdf(input_image, output_folder, circle_gdf, mask=False)\n\n../data/output_0.tif\n\n\n\nwith rio.open(output_folder / \"output_0.tif\") as dst:\n    fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n\n    show(dst.read(1), cmap=\"viridis\", ax=ax, transform=dst.transform)\n    circle_gdf.plot(facecolor=\"none\", edgecolor=\"yellow\", ax=ax)\n    print(dst.read(1))\nax\n\n[[-99999. -99999. -99999. ... -99999. -99999. -99999.]\n [-99999. -99999. -99999. ... -99999. -99999. -99999.]\n [-99999. -99999. -99999. ... -99999. -99999. -99999.]\n ...\n [-99999. -99999. -99999. ... -99999. -99999. -99999.]\n [-99999. -99999. -99999. ... -99999. -99999. -99999.]\n [-99999. -99999. -99999. ... -99999. -99999. -99999.]]",
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+    "section": "Crop a raster with masking",
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+    "title": "Raster Processing",
+    "section": "Crop on multiple geometries at once",
+    "text": "Crop on multiple geometries at once\n\ngrid_generator = grids.SquareGridGenerator(100_000)\n\n\ngrid_gdf = grid_generator.generate_grid(circle_gdf)\ngrid_gdf[\"name\"] = (\n    \"gridxy-\" + grid_gdf[\"x\"].astype(str) + \"-\" + grid_gdf[\"y\"].astype(str)\n)\n\n\ngrid_gdf\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\nname\n\n\n\n\n0\n0\n0\nPOLYGON ((119.98422 13.59951, 120.88254 13.599...\ngridxy-0-0\n\n\n1\n0\n1\nPOLYGON ((119.98422 14.47100, 120.88254 14.471...\ngridxy-0-1\n\n\n2\n0\n2\nPOLYGON ((119.98422 15.33908, 120.88254 15.339...\ngridxy-0-2\n\n\n3\n1\n0\nPOLYGON ((120.88254 13.59951, 121.78085 13.599...\ngridxy-1-0\n\n\n4\n1\n1\nPOLYGON ((120.88254 14.47100, 121.78085 14.471...\ngridxy-1-1\n\n\n5\n1\n2\nPOLYGON ((120.88254 15.33908, 121.78085 15.339...\ngridxy-1-2\n\n\n6\n2\n0\nPOLYGON ((121.78085 13.59951, 122.67917 13.599...\ngridxy-2-0\n\n\n7\n2\n1\nPOLYGON ((121.78085 14.47100, 122.67917 14.471...\ngridxy-2-1\n\n\n\n\n\n\n\n\n\nfig, ax = plt.subplots(1, 1, figsize=(4, 8))\nshow(raster.read(1), cmap=\"viridis\", ax=ax, transform=raster.transform)\ngrid_gdf.plot(ax=ax, facecolor=\"none\", edgecolor=\"yellow\")\n\nax\n\n\n\n\n\n\n\n\n\nquery_window_by_gdf(input_image, output_folder, grid_gdf, name_col=\"name\", mask=False)\n\n../data/gridxy-0-0.tif\n../data/gridxy-0-1.tif\n../data/gridxy-0-2.tif\n../data/gridxy-1-0.tif\n../data/gridxy-1-1.tif\n../data/gridxy-1-2.tif\n../data/gridxy-2-0.tif\n../data/gridxy-2-1.tif\n\n\n\nfor name in grid_gdf[\"name\"]:\n    image_path = output_folder / (name + \".tif\")\n    with rio.open(image_path) as dst:\n        fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n        ax.set_title(image_path)\n        show(dst.read(1), cmap=\"viridis\", ax=ax, transform=dst.transform)",
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+    "text": "A basic introduction to using spatial join using highest intersection",
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+    "title": "Spatial Join Using Highest Intersection Tutorial",
+    "section": "Summary",
+    "text": "Summary\nJoins attributes of two tables based on largest area of overlap. Usage of this function assumes that the two datasets are both vector polygon data.",
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+    "title": "Spatial Join Using Highest Intersection Tutorial",
+    "section": "How does it work?",
+    "text": "How does it work?\nSpatial join using highest intersection works by assigning a unique ID to the first dataframe (gdf1) and using its geometry as basis for the intersection. Data from the second dataframe (gdf2), apart from its geometry, is retained in the output.\n\nget_highest_intersection (gdf1, gdf2, proj_crs)\n\n\n\n\n\n\n\n\n\n\n\ntype\ndefault\noptional/ required\ndetails\n\n\n\n\ngdf1\nGeoDataFrame\nnone\nrequired\nbasis of output geometry\n\n\ngdf2\nGeoDataFrame\nnone\nrequired\ndata to be included during intersection\n\n\nproj_crs\nString\nnone\nrequired\nmetric CRS (e.g., Philippines uses EPSG:32651)\n\n\n\nA technical step-by-step explanation of how get_highest_intersection works is detailed in the cell blocks below. An example on how to use it with its arguments is shown in the sample use case section thereafter.\n\nDefine the function and its arguments.\n\ndef get_highest_intersection(\n    gdf1: gpd.GeoDataFrame,\n    gdf2: gpd.GeoDataFrame,\n    proj_crs: str,\n) -> gpd.GeoDataFrame:\n\nCreate a copy of the two geodataframes.\n\n    gdf1 = gdf1.copy()\n    gdf2 = gdf2.copy()\n\nRename new columns with “__” prefixes and suffixes to prevent overwriting existing columns in the original geodataframes.\n\n    uid_col = \"__uid__\"\n    area_col = \"__area_highest_intersection__\"\n    auxiliary_cols = [uid_col, area_col]\n\nConduct checks to make sure we are not overwriting existing columns.\n\n    for col in auxiliary_cols:\n        if col in gdf1.columns:\n            raise ValueError(f\"Make sure {col} isn't already a column in gdf1\")\n        if col in gdf2.columns:\n            raise ValueError(f\"Make sure {col} isn't already a column in gdf2\")\n\nAssign a unique ID to the first geodataframe. Note that uid_col is also defined in Step 2.\n\n    gdf1[uid_col] = range(len(gdf1))\n\nGet intersection of the geodataframes.\n\n    overlay = gdf1.overlay(gdf2, how=\"intersection\")\n\nAdd a column for overlapping area. Note that area_col is also defined in Step 2.\n\n    overlay[\"geometry\"] = overlay[\"geometry\"].to_crs(proj_crs)\n    overlay[area_col] = (overlay.geometry.area)\n\nSort values by area. Drop duplicates and null values.\n\n    overlay = overlay.sort_values(by=area_col, ascending=True)\n    overlay = overlay.drop_duplicates(subset=[uid_col], keep=\"last\")\n    overlay = overlay.dropna(subset=[uid_col])\n    assert not overlay[uid_col].duplicated().any()\n    overlay = overlay.sort_values(by=[uid_col], ascending=True)\n\nDrop geometry from the overlay dataframe and merge the original geometry from gdf1. Also drop additional columns (uid_col, area_col) used to accomplish calculate overlapping area for the function.\n\n    overlay_merge = overlay.drop(\"geometry\", axis=1)\n\n    output = pd.merge(\n        left=gdf1[[uid_col, \"geometry\"]],\n        right=overlay_merge,\n        on=uid_col,\n        how=\"left\",\n        validate=\"one_to_one\",\n    )\n\n    output = output.drop(columns=auxiliary_cols)\n\nDrop the additional columns (uid_col, area_col) and return the final output.\n\n    return output",
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+    "title": "Spatial Join Using Highest Intersection Tutorial",
+    "section": "Sample use case - Labeling grid with administrative boundaries",
+    "text": "Sample use case - Labeling grid with administrative boundaries\nInput: - grid - GeoDataFrame that will become the basis of geometries for the output - adm_bounds - GeoDataFrame containing attributes that we want to append to the grid - proj_crs - metric coordinate reference system (e.g., “EPSG:32651” for the Philippines)\nOutput - grid with one province name per cell\n\nStep 1: Import package\n\nimport geopandas as gpd\nimport geowrangler.grids as grids\n\n\nimport geowrangler.spatialjoin_highest_intersection as spatial_join\n\n\n\nStep 2: Load the first dataset (grid)\nThis dataset will become the basis of geometry for the output.\n\ngrid\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\n\n\n\n\n0\n3\n3\nPOLYGON ((118.27581 5.92871, 118.72497 5.92871...\n\n\n1\n2\n3\nPOLYGON ((117.82665 5.92871, 118.27581 5.92871...\n\n\n2\n3\n4\nPOLYGON ((118.27581 6.37528, 118.72497 6.37528...\n\n\n3\n3\n5\nPOLYGON ((118.27581 6.82146, 118.72497 6.82146...\n\n\n4\n0\n6\nPOLYGON ((116.92834 7.26723, 117.37749 7.26723...\n\n\n...\n...\n...\n...\n\n\n318\n19\n1\nPOLYGON ((125.46233 5.03451, 125.91149 5.03451...\n\n\n319\n19\n12\nPOLYGON ((125.46233 9.93163, 125.91149 9.93163...\n\n\n320\n19\n13\nPOLYGON ((125.46233 10.37375, 125.91149 10.373...\n\n\n321\n20\n12\nPOLYGON ((125.91149 9.93163, 126.36065 9.93163...\n\n\n322\n20\n13\nPOLYGON ((125.91149 10.37375, 126.36065 10.373...\n\n\n\n\n323 rows × 3 columns\n\n\n\n\n\ngrid.plot(facecolor=\"none\", edgecolor=\"blue\");\n\n\n\n\n\n\n\n\n\n# grid.explore()\n\n\n\nStep 3: Load the second dataset (admin_bounds)\nInfo from this dataset (apart from geometry) will be appended to the output based on highest spatial intersection.\n\nadmin_bounds\n\n\n\n\n\n\n\n\n\nshapeName\nshapeISO\nshapeID\nshapeGroup\nshapeType\ngeometry\n\n\n\n\n0\nAbra\nNone\nPHL-ADM2-3_0_0-B1\nPHL\nADM2\nMULTIPOLYGON (((120.96795 17.95706, 120.97803 ...\n\n\n1\nAgusan del Norte\nNone\nPHL-ADM2-3_0_0-B2\nPHL\nADM2\nMULTIPOLYGON (((125.57724 9.45679, 125.59687 9...\n\n\n2\nAgusan del Sur\nNone\nPHL-ADM2-3_0_0-B3\nPHL\nADM2\nMULTIPOLYGON (((125.91087 8.85625, 125.91461 8...\n\n\n3\nAklan\nNone\nPHL-ADM2-3_0_0-B4\nPHL\nADM2\nMULTIPOLYGON (((122.43667 11.59833, 122.43667 ...\n\n\n4\nAlbay\nNone\nPHL-ADM2-3_0_0-B5\nPHL\nADM2\nMULTIPOLYGON (((123.28764 13.04923, 123.28686 ...\n\n\n...\n...\n...\n...\n...\n...\n...\n\n\n76\nTawi-Tawi\nNone\nPHL-ADM2-3_0_0-B77\nPHL\nADM2\nMULTIPOLYGON (((119.46694 4.58694, 119.46639 4...\n\n\n77\nZambales\nNone\nPHL-ADM2-3_0_0-B78\nPHL\nADM2\nMULTIPOLYGON (((120.08285 14.75048, 120.08222 ...\n\n\n78\nZamboanga del Norte\nNone\nPHL-ADM2-3_0_0-B79\nPHL\nADM2\nMULTIPOLYGON (((122.09467 7.53152, 122.09467 7...\n\n\n79\nZamboanga del Sur\nNone\nPHL-ADM2-3_0_0-B80\nPHL\nADM2\nMULTIPOLYGON (((122.06223 6.87278, 122.06250 6...\n\n\n80\nZamboanga Sibugay\nNone\nPHL-ADM2-3_0_0-B81\nPHL\nADM2\nMULTIPOLYGON (((122.84063 7.27694, 122.84048 7...\n\n\n\n\n81 rows × 6 columns\n\n\n\n\n\nadmin_bounds.plot(facecolor=\"none\", edgecolor=\"blue\");\n\n\n\n\n\n\n\n\n\n# admin_bounds.explore()\n\n\n\nStep 4: Get spatial join with highest intersection\n\noutput = spatial_join.get_highest_intersection(grid, admin_bounds, \"EPSG:32651\")\n\n\nNote that each grid now has admin bounds columns (shapeName, shapeISO, shapeID, … etc.) based on the intersection with the admin boundaries with the highest overlapping area over each grid.\n\n\noutput\n\n\n\n\n\n\n\n\n\ngeometry\nx\ny\nshapeName\nshapeISO\nshapeID\nshapeGroup\nshapeType\n\n\n\n\n0\nPOLYGON ((118.27581 5.92871, 118.72497 5.92871...\n3\n3\nTawi-Tawi\nNone\nPHL-ADM2-3_0_0-B77\nPHL\nADM2\n\n\n1\nPOLYGON ((117.82665 5.92871, 118.27581 5.92871...\n2\n3\nTawi-Tawi\nNone\nPHL-ADM2-3_0_0-B77\nPHL\nADM2\n\n\n2\nPOLYGON ((118.27581 6.37528, 118.72497 6.37528...\n3\n4\nTawi-Tawi\nNone\nPHL-ADM2-3_0_0-B77\nPHL\nADM2\n\n\n3\nPOLYGON ((118.27581 6.82146, 118.72497 6.82146...\n3\n5\nTawi-Tawi\nNone\nPHL-ADM2-3_0_0-B77\nPHL\nADM2\n\n\n4\nPOLYGON ((116.92834 7.26723, 117.37749 7.26723...\n0\n6\nPalawan\nNone\nPHL-ADM2-3_0_0-B59\nPHL\nADM2\n\n\n...\n...\n...\n...\n...\n...\n...\n...\n...\n\n\n318\nPOLYGON ((125.46233 5.03451, 125.91149 5.03451...\n19\n1\nDavao del Sur\nNone\nPHL-ADM2-3_0_0-B28\nPHL\nADM2\n\n\n319\nPOLYGON ((125.46233 9.93163, 125.91149 9.93163...\n19\n12\nDinagat Islands\nNone\nPHL-ADM2-3_0_0-B30\nPHL\nADM2\n\n\n320\nPOLYGON ((125.46233 10.37375, 125.91149 10.373...\n19\n13\nEastern Samar\nNone\nPHL-ADM2-3_0_0-B31\nPHL\nADM2\n\n\n321\nPOLYGON ((125.91149 9.93163, 126.36065 9.93163...\n20\n12\nSurigao del Norte\nNone\nPHL-ADM2-3_0_0-B74\nPHL\nADM2\n\n\n322\nPOLYGON ((125.91149 10.37375, 126.36065 10.373...\n20\n13\nEastern Samar\nNone\nPHL-ADM2-3_0_0-B31\nPHL\nADM2\n\n\n\n\n323 rows × 8 columns\n\n\n\n\n\noutput.plot(facecolor=\"none\", edgecolor=\"blue\");\n\n\n\n\n\n\n\n\n\n# output.explore()",
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+    "title": "Exploring Internet Speeds with Ookla",
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+    "text": "A simple use case demo for using geowrangler modules to find the Philippine provinces/towns with the slowest/fastest internet speeds.",
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+    "title": "Exploring Internet Speeds with Ookla",
+    "section": "Summary",
+    "text": "Summary\nThis Use Case Demo shows how to use the geowrangler.datasets.ookla and the geowrangler.area_zonal_stats modules to find the slowest/fastest internet speeds within an area or region of interest.\n\nHow geowrangler can make this process easier\nGeowrangler can:\n\nValidate your geodataframes\nHelp you download Ookla data (internet speed)\nGenerate zonal stats for your province/town\n\n\n\nWhat you need to do\n\nGet your AOIs (areas of interest) - get the boundaries of your administrative regions\nDownload ookla data\nValidate AOIs and ookla data\nGenerate zonal Stats\nAnalyze and find fastest/slowest internet speeds",
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+    "title": "Exploring Internet Speeds with Ookla",
+    "section": "Tutorial",
+    "text": "Tutorial\n\nImport libraries\nLets start by importing the required libraries\n\nimport geopandas as gpd\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nimport geowrangler.area_zonal_stats as azs\n\n\n\nDownload Admin Areas\nNext, we get the administrative boundaries geodataset using data from Humanitarian Data Exchange\n\nSet Region Filter\n\n\n\n\n\n\nImportant\n\n\n\nSetting the region filter to None will compute the zonal stats for the whole Philippines and can take a lot longer than for a region.\n\n\nSet the REGION_FILTER to your Region (or None for the whole Philippines).\n\n# REGION_FILTER = None  # All regions\nREGION_FILTER = \"National Capital Region\"  # limit to 1 to speed up processing\n\nDownload the geodataset containing the admin areas of your country.\nHere we are using the data for the Philippines.\n\nphl_admin3_file = \"phl_adminboundaries_candidate_adm3\"\nphl_admin3_zip = f\"{phl_admin3_file}.zip\"\n# shapefiles\nphl_admin3_link = f\"https://data.humdata.org/dataset/caf116df-f984-4deb-85ca-41b349d3f313/resource/12457689-6a86-4474-8032-5ca9464d38a8/download/{phl_admin3_zip}\"\n\nDownload the zipped file. Depending on your internet connection, it can take several minutes.\n\n![ ! -e ../data/{phl_admin3_zip} ] && curl -L -o ../data/{phl_admin3_zip} {phl_admin3_link}\n\nCPU times: user 3.9 ms, sys: 12.6 ms, total: 16.5 ms\nWall time: 123 ms\n\n\n\n!mkdir -p ../data/{phl_admin3_file}\n\n\nmain_file = \"phl_admbnda_adm3_psa_namria_20200529\"\nphl_admin3_shp = f\"../data/{phl_admin3_file}/{main_file}.shp\"\n\n\n![ ! -e {phl_admin3_shp} ] && unzip -d ../data/{phl_admin3_file} ../data/{phl_admin3_zip}\n\nCPU times: user 5.08 ms, sys: 3.09 ms, total: 8.17 ms\nWall time: 112 ms\n\n\nLoad the admin area geo dataset.\nIn our example we are loading the .shp or shape file as a geopandas dataframe.\n\nprint(f\"loading {phl_admin3_shp}\")\nadmin3 = gpd.read_file(phl_admin3_shp)\n\nloading ../data/phl_adminboundaries_candidate_adm3/phl_admbnda_adm3_psa_namria_20200529.shp\nCPU times: user 4.4 s, sys: 4.54 s, total: 8.93 s\nWall time: 9.1 s\n\n\n\nadmin3.head()\n\n\n\n\n\n\n\n\n\nShape_Leng\nShape_Area\nADM3_EN\nADM3_PCODE\nADM3_REF\nADM3ALT1EN\nADM3ALT2EN\nADM2_EN\nADM2_PCODE\nADM1_EN\nADM1_PCODE\nADM0_EN\nADM0_PCODE\ndate\nvalidOn\nvalidTo\ngeometry\n\n\n\n\n0\n1.601219\n0.063496\nAborlan\nPH175301000\nNone\nNone\nNone\nPalawan\nPH175300000\nRegion IV-B\nPH170000000\nPhilippines (the)\nPH\n2016-06-30\n2020-05-29\nNone\nMULTIPOLYGON (((118.58350 9.37700, 118.58398 9...\n\n\n1\n1.078749\n0.050232\nAbra de Ilog\nPH175101000\nNone\nNone\nNone\nOccidental Mindoro\nPH175100000\nRegion IV-B\nPH170000000\nPhilippines (the)\nPH\n2016-06-30\n2020-05-29\nNone\nPOLYGON ((120.58412 13.50198, 120.58420 13.501...\n\n\n2\n0.424301\n0.006453\nAbucay\nPH030801000\nNone\nNone\nNone\nBataan\nPH030800000\nRegion III\nPH030000000\nPhilippines (the)\nPH\n2016-06-30\n2020-05-29\nNone\nPOLYGON ((120.49873 14.75614, 120.49891 14.755...\n\n\n3\n0.566053\n0.011343\nAbulug\nPH021501000\nNone\nNone\nNone\nCagayan\nPH021500000\nRegion II\nPH020000000\nPhilippines (the)\nPH\n2016-06-30\n2020-05-29\nNone\nPOLYGON ((121.43455 18.46651, 121.43502 18.466...\n\n\n4\n1.013649\n0.026124\nAbuyog\nPH083701000\nNone\nNone\nNone\nLeyte\nPH083700000\nRegion VIII\nPH080000000\nPhilippines (the)\nPH\n2016-06-30\n2020-05-29\nNone\nMULTIPOLYGON (((125.02684 10.73500, 125.02683 ...\n\n\n\n\n\n\n\n\n\nlist(admin3.columns.values)\n\n['Shape_Leng',\n 'Shape_Area',\n 'ADM3_EN',\n 'ADM3_PCODE',\n 'ADM3_REF',\n 'ADM3ALT1EN',\n 'ADM3ALT2EN',\n 'ADM2_EN',\n 'ADM2_PCODE',\n 'ADM1_EN',\n 'ADM1_PCODE',\n 'ADM0_EN',\n 'ADM0_PCODE',\n 'date',\n 'validOn',\n 'validTo',\n 'geometry']\n\n\n\nadmin3.ADM1_EN.unique()\n\narray(['Region IV-B', 'Region III', 'Region II', 'Region VIII',\n       'Region I', 'Region IV-A', 'Cordillera Administrative Region',\n       'Region VI', 'Autonomous Region in Muslim Mindanao', 'Region XII',\n       'Region VII', 'Region XIII', 'Region IX', 'Region X', 'Region V',\n       'Region XI', 'National Capital Region'], dtype=object)\n\n\nLimit the admin regions to only 1 in order to make the process run faster.\nThe REGION FILTER is set in the Set Region Filter Section\n\nif REGION_FILTER:\n    admin3 = admin3[admin3.ADM1_EN == REGION_FILTER]\n\n\nimport matplotlib.pyplot as plt\n\n\nax = plt.axes()\nax = admin3.plot(ax=ax, facecolor=\"none\", edgecolor=\"blue\")\n\n\n\n\n\n\n\n\n\n\n\nAdmin 3 Levels for NCR\n\n\n\nax = plt.axes()\nax = admin3[admin3.ADM3_EN == \"Pateros\"].plot(ax=ax, facecolor=\"none\", edgecolor=\"blue\")\n\nCPU times: user 115 ms, sys: 92.5 ms, total: 207 ms\nWall time: 105 ms\n\n\n\n\n\n\n\n\n\n\nadmin3.crs\n\n<Geographic 2D CRS: EPSG:4326>\nName: WGS 84\nAxis Info [ellipsoidal]:\n- Lat[north]: Geodetic latitude (degree)\n- Lon[east]: Geodetic longitude (degree)\nArea of Use:\n- name: World.\n- bounds: (-180.0, -90.0, 180.0, 90.0)\nDatum: World Geodetic System 1984 ensemble\n- Ellipsoid: WGS 84\n- Prime Meridian: Greenwich\n\n\n\nadmin3.total_bounds\n\narray([120.90639543,  14.35172957, 121.13503641,  14.78529173])\n\n\n\nlen(admin3)\n\n30\n\n\n\n\n\nDownload Ookla data\nUse the geowrangler.datasets.ookla module to explore and download ookla (internet speed) data\n\nfrom geowrangler.datasets import ookla\n\nList the publically available ookla datasets\n\nookla_dsets = ookla.list_ookla_files()\nookla_dsets\n\nCPU times: user 76.1 ms, sys: 14.5 ms, total: 90.5 ms\nWall time: 1.13 s\n\n\n{OoklaQuarter(type='fixed', year='2019', quarter='1'): '2019-01-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2019', quarter='2'): '2019-04-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2019', quarter='3'): '2019-07-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2019', quarter='4'): '2019-10-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2020', quarter='1'): '2020-01-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2020', quarter='2'): '2020-04-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2020', quarter='3'): '2020-07-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2020', quarter='4'): '2020-10-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2021', quarter='1'): '2021-01-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2021', quarter='2'): '2021-04-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2021', quarter='3'): '2021-07-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2021', quarter='4'): '2021-10-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2022', quarter='1'): '2022-01-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='fixed', year='2022', quarter='2'): '2022-04-01_performance_fixed_tiles.parquet',\n OoklaQuarter(type='mobile', year='2019', quarter='1'): '2019-01-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2019', quarter='2'): '2019-04-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2019', quarter='3'): '2019-07-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2019', quarter='4'): '2019-10-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2020', quarter='1'): '2020-01-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2020', quarter='2'): '2020-04-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2020', quarter='3'): '2020-07-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2020', quarter='4'): '2020-10-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2021', quarter='1'): '2021-01-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2021', quarter='2'): '2021-04-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2021', quarter='3'): '2021-07-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2021', quarter='4'): '2021-10-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2022', quarter='1'): '2022-01-01_performance_mobile_tiles.parquet',\n OoklaQuarter(type='mobile', year='2022', quarter='2'): '2022-04-01_performance_mobile_tiles.parquet'}\n\n\nDownload the latest data (as of the time of writing this)\n\nookla_params = dict(year=\"2022\", quarter=\"2\", directory=\"../data\")\n\n\nookla_fixed = ookla.download_ookla_file(type_=\"fixed\", **ookla_params)\n\nCPU times: user 143 µs, sys: 118 µs, total: 261 µs\nWall time: 182 µs\n\n\n\nookla_fixed\n\nPosixPath('../data/2022-04-01_performance_fixed_tiles.parquet')\n\n\n\n\nConvert ookla data into a GeoDataFrame\nThe downloaded file from ookla is not yet a geodataset, so we will h have to convert it into a GeoDataFrame suitable for use in geowrangler.\n\nimport pandas as pd\n\nThe downloaded ookla data contains the internet speed data for the whole world and can take a minute or two to load.\nLater, we will show how to filter the data to only include the data relevant to our AOI.\n\nfixed = pd.read_parquet(ookla_fixed)\n\nCPU times: user 7.72 s, sys: 14.2 s, total: 21.9 s\nWall time: 19.2 s\n\n\n\nlen(fixed)\n\n6581735\n\n\n\nfixed.head()\n\n\n\n\n\n\n\n\n\nquadkey\ntile\navg_d_kbps\navg_u_kbps\navg_lat_ms\ntests\ndevices\n\n\n\n\n0\n0000122200000000\nPOLYGON((-168.75 84.1249731939109, -168.744506...\n97075\n42458\n65\n7\n1\n\n\n1\n0022133222312322\nPOLYGON((-160.02685546875 70.6435894914449, -1...\n8324\n3798\n91\n2\n1\n\n\n2\n0022133222330100\nPOLYGON((-160.02685546875 70.6417687358462, -1...\n14972\n4461\n94\n29\n2\n\n\n3\n0022133222330201\nPOLYGON((-160.043334960938 70.6344840663086, -...\n6253\n2818\n91\n1\n1\n\n\n4\n0022202203233313\nPOLYGON((-179.478149414062 68.9169336615194, -...\n700\n126\n1090\n1\n1\n\n\n\n\n\n\n\n\n\nfixed.dtypes\n\nquadkey       object\ntile          object\navg_d_kbps     int64\navg_u_kbps     int64\navg_lat_ms     int64\ntests          int64\ndevices        int64\ndtype: object\n\n\nThe data is now a Pandas DataFrame but this needs to be converted to a GeoDataFrame by converting the tile column into a geometry with a crs (Coordinate Reference System).\nSee EPSG:4326 for more details about the CRS.\nConverting the data can also take a minute or two.\n\nfixed[\"geometry\"] = gpd.GeoSeries.from_wkt(fixed.tile, crs=\"EPSG:4326\")\n\nCPU times: user 30.1 s, sys: 1.55 s, total: 31.6 s\nWall time: 31.3 s\n\n\n\nfixed.head()\n\n\n\n\n\n\n\n\n\nquadkey\ntile\navg_d_kbps\navg_u_kbps\navg_lat_ms\ntests\ndevices\ngeometry\n\n\n\n\n0\n0000122200000000\nPOLYGON((-168.75 84.1249731939109, -168.744506...\n97075\n42458\n65\n7\n1\nPOLYGON ((-168.75000 84.12497, -168.74451 84.1...\n\n\n1\n0022133222312322\nPOLYGON((-160.02685546875 70.6435894914449, -1...\n8324\n3798\n91\n2\n1\nPOLYGON ((-160.02686 70.64359, -160.02136 70.6...\n\n\n2\n0022133222330100\nPOLYGON((-160.02685546875 70.6417687358462, -1...\n14972\n4461\n94\n29\n2\nPOLYGON ((-160.02686 70.64177, -160.02136 70.6...\n\n\n3\n0022133222330201\nPOLYGON((-160.043334960938 70.6344840663086, -...\n6253\n2818\n91\n1\n1\nPOLYGON ((-160.04333 70.63448, -160.03784 70.6...\n\n\n4\n0022202203233313\nPOLYGON((-179.478149414062 68.9169336615194, -...\n700\n126\n1090\n1\n1\nPOLYGON ((-179.47815 68.91693, -179.47266 68.9...\n\n\n\n\n\n\n\n\n\nfixed.drop(columns=[\"tile\"], inplace=True)\n\nCPU times: user 515 ms, sys: 95.8 ms, total: 610 ms\nWall time: 606 ms\n\n\n\nfixed = gpd.GeoDataFrame(fixed, geometry=\"geometry\", crs=\"EPSG:4326\")\n\nCPU times: user 71.8 ms, sys: 61.1 ms, total: 133 ms\nWall time: 131 ms\n\n\n\n\nValidate AOI and Data Geometries\nIn order to prevent more headaches as we process and analyze geospatial datasets down the line, it is prudent to check that our datasets have valid geometries.\nWe can use the geowrangler.validation module to check as well fix these problems.\n\nfrom geowrangler.validation import GeometryValidation\n\n\nValidate AOI\n\nadmin3_gvm = GeometryValidation(admin3)\n\n\nvalid_admin3 = admin3_gvm.validate_all()\n\nCPU times: user 306 ms, sys: 1.56 ms, total: 308 ms\nWall time: 306 ms\n\n\n\nvalid_admin3.head()\n\n\n\n\n\n\n\n\n\nShape_Leng\nShape_Area\nADM3_EN\nADM3_PCODE\nADM3_REF\nADM3ALT1EN\nADM3ALT2EN\nADM2_EN\nADM2_PCODE\nADM1_EN\nADM1_PCODE\nADM0_EN\nADM0_PCODE\ndate\nvalidOn\nvalidTo\ngeometry\nis_not_null\nis_not_self_intersecting\nis_oriented_properly\nis_within_crs_bounds\narea_is_not_zero\n\n\n\n\n211\n0.037803\n0.000056\nBinondo\nPH133902000\nNone\nNone\nNone\nNCR, City of Manila, First District\nPH133900000\nNational Capital Region\nPH130000000\nPhilippines (the)\nPH\n2016-06-30\n2020-05-29\nNone\nPOLYGON ((120.97738 14.60226, 120.97765 14.602...\nTrue\nTrue\nFalse\nTrue\nTrue\n\n\n308\n0.692758\n0.004468\nCaloocan City\nPH137501000\nNone\nNone\nNone\nNCR, Third District\nPH137500000\nNational Capital Region\nPH130000000\nPhilippines (the)\nPH\n2016-06-30\n2020-05-29\nNone\nMULTIPOLYGON (((121.01856 14.69176, 121.01846 ...\nTrue\nTrue\nFalse\nTrue\nTrue\n\n\n395\n0.362306\n0.002745\nCity of Las Piñas\nPH137601000\nCity of Las Pinas\nNone\nNone\nNCR, Fourth District\nPH137600000\nNational Capital Region\nPH130000000\nPhilippines (the)\nPH\n2016-06-30\n2020-05-29\nNone\nMULTIPOLYGON (((120.98122 14.48720, 120.98077 ...\nTrue\nTrue\nFalse\nTrue\nTrue\n\n\n398\n0.260317\n0.002094\nCity of Makati\nPH137602000\nNone\nNone\nNone\nNCR, Fourth District\nPH137600000\nNational Capital Region\nPH130000000\nPhilippines (the)\nPH\n2016-06-30\n2020-05-29\nNone\nPOLYGON ((121.03468 14.56725, 121.03416 14.567...\nTrue\nTrue\nFalse\nTrue\nTrue\n\n\n399\n0.262034\n0.001323\nCity of Malabon\nPH137502000\nNone\nNone\nNone\nNCR, Third District\nPH137500000\nNational Capital Region\nPH130000000\nPhilippines (the)\nPH\n2016-06-30\n2020-05-29\nNone\nPOLYGON ((120.92733 14.70265, 120.92690 14.702...\nTrue\nTrue\nFalse\nTrue\nTrue\n\n\n\n\n\n\n\n\n\nvalid_cols = [\n    \"is_oriented_properly\",\n    \"is_not_null\",\n    \"is_not_self_intersecting\",\n    \"is_within_crs_bounds\",\n    \"area_is_not_zero\",\n]\n\n\n[valid_admin3[col].value_counts() for col in valid_cols]\n\n[False    30\n Name: is_oriented_properly, dtype: int64,\n True    30\n Name: is_not_null, dtype: int64,\n True    30\n Name: is_not_self_intersecting, dtype: int64,\n True    30\n Name: is_within_crs_bounds, dtype: int64,\n True    30\n Name: area_is_not_zero, dtype: int64]\n\n\nSo the admin areas have their geometry improperly oriented (i.e. layed out in a counter clockwise direction instead of clockwise direction) and this has been fixed, but passes all the other default validations.\n\nvalid_admin3.drop(\n    columns=valid_cols,\n    inplace=True,\n)\n\n\n\nValidate Data Geometries\nBefore validating, let’s filter the data to only those intersecting our AOI because we don’t need to check all the data from around the world when we’re only interested in our AOI.\nBefore we validate, we need to create the spatial indexes for both the AOI and data geometries. Generating the spatial indexes of the data geometries can take a minute or two due to the size of the data and their geometries.\n\nvalid_admin3.geometry.sindex\n\nCPU times: user 1.12 ms, sys: 541 µs, total: 1.66 ms\nWall time: 1.61 ms\n\n\n<geopandas.sindex.PyGEOSSTRTreeIndex at 0x7f6e434b17f0>\n\n\n\nfixed.geometry.sindex\n\nCPU times: user 6.12 s, sys: 4.01 s, total: 10.1 s\nWall time: 10.1 s\n\n\n<geopandas.sindex.PyGEOSSTRTreeIndex at 0x7f6e434b1880>\n\n\nLets now filter the data (filtered_fixed).\nFiltering the data using a spatial join can also take a minute or two.\n\nfiltered_fixed = fixed.sjoin(\n    valid_admin3[[\"geometry\"]], how=\"inner\", predicate=\"intersects\"\n)\n\nCPU times: user 1.97 s, sys: 688 ms, total: 2.66 s\nWall time: 2.67 s\n\n\n\nfiltered_fixed.drop(columns=[\"index_right\"], inplace=True)\n\n\nfixed_gvm = GeometryValidation(filtered_fixed)\n\n\nvalid_fixed = fixed_gvm.validate_all()\n\nCPU times: user 676 ms, sys: 0 ns, total: 676 ms\nWall time: 673 ms\n\n\n\n[valid_fixed[col].value_counts() for col in valid_cols]\n\nCPU times: user 1.96 ms, sys: 961 µs, total: 2.92 ms\nWall time: 2.28 ms\n\n\n[False    2300\n Name: is_oriented_properly, dtype: int64,\n True    2300\n Name: is_not_null, dtype: int64,\n True    2300\n Name: is_not_self_intersecting, dtype: int64,\n True    2300\n Name: is_within_crs_bounds, dtype: int64,\n True    2300\n Name: area_is_not_zero, dtype: int64]\n\n\nAgain the data geometries have an improperly oriented geometry and have been fixed by the validator\n\nvalid_fixed.drop(columns=valid_cols, inplace=True)\nfiltered_fixed = valid_fixed\n\n\n\n\nfiltered fixed ookla data\n\n\n\n\n\nGenerate Zonal Stats\nLets now generate the zonal stats – the statistics of the data we are interested in.\n\nfiltered_fixed.head()\n\n\n\n\n\n\n\n\n\nquadkey\navg_d_kbps\navg_u_kbps\navg_lat_ms\ntests\ndevices\ngeometry\n\n\n\n\n5646006\n1323030313311323\n43565\n47889\n8\n93\n23\nPOLYGON ((120.92102 14.73770, 120.92102 14.732...\n\n\n5646008\n1323030313311332\n72579\n91717\n11\n149\n36\nPOLYGON ((120.92651 14.73770, 120.92651 14.732...\n\n\n5646009\n1323030313311333\n52572\n67337\n3\n1\n1\nPOLYGON ((120.93201 14.73770, 120.93201 14.732...\n\n\n5646018\n1323030313313110\n63548\n77538\n14\n601\n69\nPOLYGON ((120.92651 14.73239, 120.92651 14.727...\n\n\n5646019\n1323030313313111\n105903\n103874\n13\n115\n22\nPOLYGON ((120.93201 14.73239, 120.93201 14.727...\n\n\n\n\n\n\n\n\n\nlist(filtered_fixed.columns.values)\n\n['quadkey',\n 'avg_d_kbps',\n 'avg_u_kbps',\n 'avg_lat_ms',\n 'tests',\n 'devices',\n 'geometry']\n\n\n\nlen(filtered_fixed)\n\n2300\n\n\nConvert to planar CRS (since we are computing areas)\n\nvalid_admin3 = valid_admin3.to_crs(\"EPSG:3857\")\n\nCPU times: user 38.6 ms, sys: 0 ns, total: 38.6 ms\nWall time: 37.2 ms\n\n\n\nfiltered_fixed = filtered_fixed.to_crs(\"EPSG:3857\")\n\nCPU times: user 23.3 ms, sys: 1.52 ms, total: 24.8 ms\nWall time: 23.4 ms\n\n\nLets save the files so we can retrieve them as necessary without having to reprocess them again.\n\nvalid_admin3.to_file(\"../data/valid_admin3.geojson\", driver=\"GeoJSON\")\n\nCPU times: user 539 ms, sys: 37.6 ms, total: 577 ms\nWall time: 574 ms\n\n\n\nfiltered_fixed.to_file(\"../data/filtered_fixed.geojson\", driver=\"GeoJSON\")\n\nCPU times: user 583 ms, sys: 38.6 ms, total: 622 ms\nWall time: 620 ms\n\n\n\n# %%time\n# valid_admin3 = gpd.read_file(\"../data/valid_admin3.geojson\")\n\n\n# %%time\n# filtered_fixed = gpd.read_file(\"../data/filtered_fixed.geojson\")\n\nWe want the following statistics - mean (aka average), min (minimum), max (maximum) and the standard deviation (std) for the 3 data columns that ookla provides - average download speed (avg_d_kbps), average upload speed (avg_u_kbps) and average latency in milliseconds (avg_lat_ms)\n\nfuncs = [\"mean\", \"min\", \"max\", \"std\"]\ncolumns = [\"avg_d_kbps\", \"avg_u_kbps\", \"avg_lat_ms\"]\n\n\naggregations = [dict(func=funcs, column=c) for c in columns]\n\nThese are the aggregations to be performed for each data column\n\naggregations\n\n[{'func': ['mean', 'min', 'max', 'std'], 'column': 'avg_d_kbps'},\n {'func': ['mean', 'min', 'max', 'std'], 'column': 'avg_u_kbps'},\n {'func': ['mean', 'min', 'max', 'std'], 'column': 'avg_lat_ms'}]\n\n\nThe output columns use the default {column}_{func} format if not explicitly specified.\n\naoi = azs.create_area_zonal_stats(\n    valid_admin3, filtered_fixed, aggregations=aggregations\n)\n\nCPU times: user 3.74 s, sys: 0 ns, total: 3.74 s\nWall time: 3.73 s\n\n\n\n\nAnalyze Zonal Stats\nThese are the results - the same dataframe as the original, with the addition of zonal statistics we specified as the aggregations.\n\naoi.head()\n\n\n\n\n\n\n\n\n\nShape_Leng\nShape_Area\nADM3_EN\nADM3_PCODE\nADM3_REF\nADM3ALT1EN\nADM3ALT2EN\nADM2_EN\nADM2_PCODE\nADM1_EN\nADM1_PCODE\nADM0_EN\nADM0_PCODE\ndate\nvalidOn\nvalidTo\ngeometry\nintersect_area_sum\navg_d_kbps_mean\navg_d_kbps_min\navg_d_kbps_max\navg_d_kbps_std\navg_u_kbps_mean\navg_u_kbps_min\navg_u_kbps_max\navg_u_kbps_std\navg_lat_ms_mean\navg_lat_ms_min\navg_lat_ms_max\navg_lat_ms_std\n\n\n\n\n211\n0.037803\n0.000056\nBinondo\nPH133902000\nNone\nNone\nNone\nNCR, City of Manila, First District\nPH133900000\nNational Capital Region\nPH130000000\nPhilippines (the)\nPH\n2016-06-30\n2020-05-29\nNone\nPOLYGON ((13467140.279 1643403.808, 13467170.0...\n2.330890e+06\n15125.377199\n0.0\n97827\n4141.081774\n12152.916247\n0.0\n80923\n5625.791089\n1.228832\n0.0\n8\n0.923381\n\n\n308\n0.692758\n0.004468\nCaloocan City\nPH137501000\nNone\nNone\nNone\nNCR, Third District\nPH137500000\nNational Capital Region\nPH130000000\nPhilippines (the)\nPH\n2016-06-30\n2020-05-29\nNone\nMULTIPOLYGON (((13471724.654 1653701.565, 1347...\n7.425998e+07\n309.956521\n0.0\n123236\n15346.775252\n307.716950\n0.0\n117885\n15118.576034\n0.035243\n0.0\n59\n6.147227\n\n\n395\n0.362306\n0.002745\nCity of Las Piñas\nPH137601000\nCity of Las Pinas\nNone\nNone\nNCR, Fourth District\nPH137600000\nNational Capital Region\nPH130000000\nPhilippines (the)\nPH\n2016-06-30\n2020-05-29\nNone\nMULTIPOLYGON (((13467568.063 1630171.435, 1346...\n4.080667e+07\n649.603501\n0.0\n175810\n22328.120899\n555.324817\n0.0\n136167\n19944.226971\n0.045298\n0.0\n146\n11.110505\n\n\n398\n0.260317\n0.002094\nCity of Makati\nPH137602000\nNone\nNone\nNone\nNCR, Fourth District\nPH137600000\nNational Capital Region\nPH130000000\nPhilippines (the)\nPH\n2016-06-30\n2020-05-29\nNone\nPOLYGON ((13473518.917 1639376.906, 13473461.1...\n3.840895e+07\n765.462383\n0.0\n159496\n16771.721664\n663.200995\n0.0\n144939\n17818.361374\n0.091436\n0.0\n25\n5.288884\n\n\n399\n0.262034\n0.001323\nCity of Malabon\nPH137502000\nNone\nNone\nNone\nNCR, Third District\nPH137500000\nNational Capital Region\nPH130000000\nPhilippines (the)\nPH\n2016-06-30\n2020-05-29\nNone\nPOLYGON ((13461569.031 1654955.161, 13461520.7...\n2.583556e+07\n1006.838781\n0.0\n119622\n13444.542392\n933.345049\n0.0\n130002\n14309.585589\n0.096779\n0.0\n17\n2.893162\n\n\n\n\n\n\n\n\nLets save the results\n\naoi.to_file(\"../data/admin3_internet_aoi.geojson\", driver=\"GeoJSON\")\n\nCPU times: user 415 ms, sys: 12.3 ms, total: 428 ms\nWall time: 424 ms\n\n\n\nlist(aoi.columns.values)\n\n['Shape_Leng',\n 'Shape_Area',\n 'ADM3_EN',\n 'ADM3_PCODE',\n 'ADM3_REF',\n 'ADM3ALT1EN',\n 'ADM3ALT2EN',\n 'ADM2_EN',\n 'ADM2_PCODE',\n 'ADM1_EN',\n 'ADM1_PCODE',\n 'ADM0_EN',\n 'ADM0_PCODE',\n 'date',\n 'validOn',\n 'validTo',\n 'geometry',\n 'intersect_area_sum',\n 'avg_d_kbps_mean',\n 'avg_d_kbps_min',\n 'avg_d_kbps_max',\n 'avg_d_kbps_std',\n 'avg_u_kbps_mean',\n 'avg_u_kbps_min',\n 'avg_u_kbps_max',\n 'avg_u_kbps_std',\n 'avg_lat_ms_mean',\n 'avg_lat_ms_min',\n 'avg_lat_ms_max',\n 'avg_lat_ms_std']\n\n\nLet’s sort the different admin level 3 areas (cities/municipalities/district) by average download speed (avg_d_kbps_mean) in descending order\n\nfastest_mean_download = aoi.sort_values(\"avg_d_kbps_mean\", ascending=False)\n\nCPU times: user 3.72 ms, sys: 0 ns, total: 3.72 ms\nWall time: 3.16 ms\n\n\n\nlen(fastest_mean_download)\n\n30\n\n\nSo according the latest ookla data (as of this writing) the district/town/province within the NCR Region with the fastest average download speed is Binondo!! :)\n\nfastest_mean_download.iloc[0]\n\nShape_Leng                                                     0.037803\nShape_Area                                                     0.000056\nADM3_EN                                                         Binondo\nADM3_PCODE                                                  PH133902000\nADM3_REF                                                           None\nADM3ALT1EN                                                         None\nADM3ALT2EN                                                         None\nADM2_EN                             NCR, City of Manila, First District\nADM2_PCODE                                                  PH133900000\nADM1_EN                                         National Capital Region\nADM1_PCODE                                                  PH130000000\nADM0_EN                                               Philippines (the)\nADM0_PCODE                                                           PH\ndate                                                         2016-06-30\nvalidOn                                                      2020-05-29\nvalidTo                                                            None\ngeometry              POLYGON ((13467140.2793258 1643403.80801774, 1...\nintersect_area_sum                                       2330890.174196\navg_d_kbps_mean                                            15125.377199\navg_d_kbps_min                                                      0.0\navg_d_kbps_max                                                    97827\navg_d_kbps_std                                              4141.081774\navg_u_kbps_mean                                            12152.916247\navg_u_kbps_min                                                      0.0\navg_u_kbps_max                                                    80923\navg_u_kbps_std                                              5625.791089\navg_lat_ms_mean                                                1.228832\navg_lat_ms_min                                                      0.0\navg_lat_ms_max                                                        8\navg_lat_ms_std                                                 0.923381\nName: 211, dtype: object\n\n\nWhile the district/city/municipality within the NCR Regions with the slowest average download speed is Quezon City!!!\n\nfastest_mean_download.iloc[-1]\n\nShape_Leng                                                     0.773902\nShape_Area                                                      0.01363\nADM3_EN                                                     Quezon City\nADM3_PCODE                                                  PH137404000\nADM3_REF                                                           None\nADM3ALT1EN                                                         None\nADM3ALT2EN                                                         None\nADM2_EN                                            NCR, Second District\nADM2_PCODE                                                  PH137400000\nADM1_EN                                         National Capital Region\nADM1_PCODE                                                  PH130000000\nADM0_EN                                               Philippines (the)\nADM0_PCODE                                                           PH\ndate                                                         2016-06-30\nvalidOn                                                      2020-05-29\nvalidTo                                                            None\ngeometry              POLYGON ((13484683.37714301 1663482.482347114,...\nintersect_area_sum                                      174544789.29521\navg_d_kbps_mean                                              145.111131\navg_d_kbps_min                                                      0.0\navg_d_kbps_max                                                   269544\navg_d_kbps_std                                             20232.269854\navg_u_kbps_mean                                              133.033955\navg_u_kbps_min                                                      0.0\navg_u_kbps_max                                                   260425\navg_u_kbps_std                                             20141.871761\navg_lat_ms_mean                                                0.011798\navg_lat_ms_min                                                      0.0\navg_lat_ms_max                                                       29\navg_lat_ms_std                                                  2.90016\nName: 1191, dtype: object\n\n\nThe top 5 fastest areas are\n\nfastest_mean_download[[\"ADM3_EN\", \"ADM2_EN\", \"avg_d_kbps_mean\"]].head()\n\n\n\n\n\n\n\n\n\nADM3_EN\nADM2_EN\navg_d_kbps_mean\n\n\n\n\n211\nBinondo\nNCR, City of Manila, First District\n15125.377199\n\n\n1343\nSan Nicolas\nNCR, City of Manila, First District\n10897.783018\n\n\n1192\nQuiapo\nNCR, City of Manila, First District\n9799.121085\n\n\n1336\nSan Miguel\nNCR, City of Manila, First District\n8708.791412\n\n\n1112\nPateros\nNCR, Fourth District\n7581.975774\n\n\n\n\n\n\n\n\nThe top 5 slowest areas are\n\nfastest_mean_download[[\"ADM3_EN\", \"ADM2_EN\", \"avg_d_kbps_mean\"]].tail()\n\n\n\n\n\n\n\n\n\nADM3_EN\nADM2_EN\navg_d_kbps_mean\n\n\n\n\n1521\nTaguig City\nNCR, Fourth District\n531.559943\n\n\n411\nCity of Parañaque\nNCR, Fourth District\n470.491966\n\n\n436\nCity of Valenzuela\nNCR, Third District\n420.083766\n\n\n308\nCaloocan City\nNCR, Third District\n309.956521\n\n\n1191\nQuezon City\nNCR, Second District\n145.111131\n\n\n\n\n\n\n\n\n\nax = plt.axes()\nax = fastest_mean_download.plot(ax=ax, facecolor=\"none\", edgecolor=\"blue\")\nax = fastest_mean_download.iloc[:1].plot(ax=ax, facecolor=\"red\", edgecolor=\"red\")\nax = fastest_mean_download.iloc[29:].plot(ax=ax, facecolor=\"green\", edgecolor=\"green\")\nax = fastest_mean_download.plot(ax=ax, column=\"avg_d_kbps_mean\", alpha=0.8)\n\n\n\n\n\n\n\n\n\n\n\nfastest fixed ookla by adm3\n\n\nSo now, we’ve answered the question of where the fastest/slowest internet speeds are for the National Capital Region (NCR)\n\n\nAdditional Exercises\nYou can further experiment and try out other exercises to explore ookla data, or even try it out using a different country using a different geodataset from the Humanitarian Data Exchange site.\nThe following are more exercises you can try:\n\nRepeat the same process to find the fastest and slowest mobile internet speeds\nRepeat the same process for a different REGION_FILTER\nAggregate by adm2 level instead of adm3",
+    "crumbs": [
+      "Exploring Internet Speeds with Ookla"
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+    "objectID": "validation.html",
+    "href": "validation.html",
+    "title": "Geometry Validation",
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+    "text": "source\n\nValidationError\nCommon base class for all non-exit exceptions.\n\nsource\n\n\nBaseValidator\n\n BaseValidator (add_new_column:bool=True, apply_fix:bool=True)\n\nAbstract Base Class for single validator\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nadd_new_column\nbool\nTrue\nAdd new column to show errors\n\n\napply_fix\nbool\nTrue\nUpdate geometry\n\n\n\n\nsource\n\n\nBaseValidator.validate\n\n BaseValidator.validate (gdf:geopandas.geodataframe.GeoDataFrame,\n                         clone=True)\n\nMethod that checks the validity of a each geometry and applies a fix to these geometries or raise a warning\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\ngdf\nGeoDataFrame\n\nGeoDataFrame to validate\n\n\nclone\nbool\nTrue\nApply validation to copy\n\n\nReturns\nGeoDataFrame\n\n\n\n\n\n\nsource\n\n\nOrientationValidator\n\n OrientationValidator (add_new_column:bool=True, apply_fix:bool=True)\n\nChecks and fixes Orientation of the geometry to ensure it follows a counter-clockwise orientation\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nadd_new_column\nbool\nTrue\nAdd new column to show errors\n\n\napply_fix\nbool\nTrue\nUpdate geometry\n\n\n\n\nsource\n\n\nOrientationValidator.check\n\n OrientationValidator.check (geometry:shapely.geometry.base.BaseGeometry)\n\nChecks if orientation is counter clockwise\n\n\n\n\nType\nDetails\n\n\n\n\ngeometry\nBaseGeometry\nGeometry to validate\n\n\nReturns\nbool\n\n\n\n\n\nsource\n\n\nOrientationValidator.fix\n\n OrientationValidator.fix (geometry:shapely.geometry.base.BaseGeometry)\n\nFixes orientation if orientation is clockwise\n\n\n\n\nType\nDetails\n\n\n\n\ngeometry\nBaseGeometry\nGeometry to fix\n\n\nReturns\nBaseGeometry\n\n\n\n\n\nsource\n\n\nCrsBoundsValidator\n\n CrsBoundsValidator (add_new_column:bool=True, apply_fix:bool=True)\n\nChecks bounds of the geometry to ensure it is within bounds of the crs\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nadd_new_column\nbool\nTrue\nAdd new column to show errors\n\n\napply_fix\nbool\nTrue\nUpdate geometry\n\n\n\n\nsource\n\n\nCrsBoundsValidator.get_check_arguments\n\n CrsBoundsValidator.get_check_arguments\n                                         (gdf:geopandas.geodataframe.GeoDa\n                                         taFrame)\n\nReturn check arguments\n\n\n\n\nType\nDetails\n\n\n\n\ngdf\nGeoDataFrame\nGeoDataFrame to check\n\n\nReturns\ndict\n\n\n\n\n\nsource\n\n\nCrsBoundsValidator.check\n\n CrsBoundsValidator.check (geometry:shapely.geometry.base.BaseGeometry,\n                           gdf:geopandas.geodataframe.GeoDataFrame)\n\nChecks if polygon is within bounds of crs.\n\n\n\n\nType\nDetails\n\n\n\n\ngeometry\nBaseGeometry\nGeometry to validate\n\n\ngdf\nGeoDataFrame\nGeoDataframe to check\n\n\nReturns\nbool\n\n\n\n\n\nsource\n\n\nCrsBoundsValidator.fix\n\n CrsBoundsValidator.fix (geometry:shapely.geometry.base.BaseGeometry)\n\nNo fix available\n\n\n\n\nType\nDetails\n\n\n\n\ngeometry\nBaseGeometry\nGeometry to fix\n\n\nReturns\nBaseGeometry\npragma: no cover\n\n\n\n\nsource\n\n\nSelfIntersectingValidator\n\n SelfIntersectingValidator (add_new_column:bool=True, apply_fix:bool=True)\n\nChecks bounds of the geometry to ensure it is within bounds or crs\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nadd_new_column\nbool\nTrue\nAdd new column to show errors\n\n\napply_fix\nbool\nTrue\nUpdate geometry\n\n\n\n\nsource\n\n\nSelfIntersectingValidator.check\n\n SelfIntersectingValidator.check\n                                  (geometry:shapely.geometry.base.BaseGeom\n                                  etry)\n\n\n\n\n\nType\nDetails\n\n\n\n\ngeometry\nBaseGeometry\nGeometry to check\n\n\nReturns\nbool\n\n\n\n\n\nsource\n\n\nSelfIntersectingValidator.fix\n\n SelfIntersectingValidator.fix\n                                (geometry:shapely.geometry.base.BaseGeomet\n                                ry)\n\nFix intersection geometry by applying shapely.validation.make_valid\n\nsource\n\n\nNullValidator\n\n NullValidator (add_new_column:bool=True, apply_fix:bool=True)\n\nChecks bounds of the geometry to ensure it is within bounds or crs\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nadd_new_column\nbool\nTrue\nAdd new column to show errors\n\n\napply_fix\nbool\nTrue\nUpdate geometry\n\n\n\n\nsource\n\n\nNullValidator.check\n\n NullValidator.check (geometry:shapely.geometry.base.BaseGeometry)\n\nChecks if polygon is null\n\n\n\n\nType\nDetails\n\n\n\n\ngeometry\nBaseGeometry\n\n\n\nReturns\nbool\nGeometry to check\n\n\n\n\nsource\n\n\nNullValidator.fix\n\n NullValidator.fix (geometry:shapely.geometry.base.BaseGeometry)\n\nNo fix available\n\n\n\n\nType\nDetails\n\n\n\n\ngeometry\nBaseGeometry\nGeometry to fix\n\n\nReturns\nBaseGeometry\npragma: no cover\n\n\n\n\nsource\n\n\nAreaValidator\n\n AreaValidator (add_new_column:bool=True, apply_fix:bool=True)\n\nChecks area of the geometry to ensure it greater than 0\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nadd_new_column\nbool\nTrue\nAdd new column to show errors\n\n\napply_fix\nbool\nTrue\nUpdate geometry\n\n\n\n\nsource\n\n\nAreaValidator.check\n\n AreaValidator.check (geometry:shapely.geometry.base.BaseGeometry)\n\nChecks if area is greater than 0\n\nsource\n\n\nAreaValidator.fix\n\n AreaValidator.fix (geometry:shapely.geometry.base.BaseGeometry)\n\nNo fix available\n\n\n\n\nType\nDetails\n\n\n\n\ngeometry\nBaseGeometry\nGeometry to fix\n\n\nReturns\nBaseGeometry\npragma: no cover\n\n\n\n\nsource\n\n\nGeometryValidation\n\n GeometryValidation (gdf:geopandas.geodataframe.GeoDataFrame,\n                     validators:Sequence[Union[str,__main__.BaseValidator]\n                     ]=('null', 'self_intersecting', 'orientation',\n                     'crs_bounds', 'area'),\n                     add_validation_columns:bool=True,\n                     apply_fixes:bool=True)\n\nApplies a list of validation checks and tries to fix them\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\ngdf\nGeoDataFrame\n\nGeoDataFrame to validate\n\n\nvalidators\nSequence\n(‘null’, ‘self_intersecting’, ‘orientation’, ‘crs_bounds’, ‘area’)\nValidators to apply\n\n\nadd_validation_columns\nbool\nTrue\nAdd column to show errors\n\n\napply_fixes\nbool\nTrue\nUpdate geometry\n\n\nReturns\nGeoDataFrame\n\n\n\n\n\n\nsource\n\n\nGeometryValidation.validate_all\n\n GeometryValidation.validate_all ()\n\nSequentially run validators",
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+    "title": "Vector Zonal Stats Tutorial",
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+    "text": "A basic introduction to vector zonal stats",
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+    "objectID": "tutorial.vector_zonal_stats.html#basic-usage",
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+    "title": "Vector Zonal Stats Tutorial",
+    "section": "Basic Usage",
+    "text": "Basic Usage\nGenerate zonal stats for a GeoDataframe containing areas of interest\nTerms:\n\naoi - (area of interest) a geodataframe which we are interested in generating zonal statistics for\ndata - the source geodataframe containing the features which are interested in collecting zonal stats for our aoi.\n\n\nUse case 1 - Count POIs (Points of Interest)\n\nExample 1: Count POIs for Regions\n\nInput:\n\naoi - region3,4,ncr regions (Admin Level 1) (Central Luzon) geometry (geom_type - polygon, multipolygon\ndata - Philippine pois (geom_type - points)\noverlap_method = ‘intersects’\naggregations:\n\ncount - number of pois within aoi\n\n\nOutput\n\naoi with pois count (default output column: index_count)\n\n\n\nimport geopandas as gpd\nimport matplotlib.pyplot as plt\nimport geowrangler.vector_zonal_stats as vzs\n\n\n# area multipolygons for regions 3,4,ncr of the philippines\naoi = gpd.read_file(\"../data/region34ncr_admin.geojson\")\n\nCPU times: user 3 s, sys: 219 ms, total: 3.22 s\nWall time: 3.21 s\n\n\n\nax = aoi.plot(\n    ax=plt.axes(),\n    facecolor=\"none\",\n    edgecolor=[\n        \"#C62828\",\n        \"#283593\",\n        \"#FF9800\",\n    ],\n)\n\n\n\n\n\n\n\n\n\naoi\n\n\n\n\n\n\n\n\n\nReg_Code\nReg_Name\nReg_Alt_Name\ngeometry\n\n\n\n\n0\n130000000\nNational Capital Region\nNCR\nMULTIPOLYGON (((121.03842 14.78525, 121.03815 ...\n\n\n1\n030000000\nRegion III\nCentral Luzon\nMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...\n\n\n2\n040000000\nRegion IV-A\nCalabarzon\nMULTIPOLYGON (((122.72165 13.36485, 122.72143 ...\n\n\n\n\n\n\n\n\n\n# raw pois from osm data (subset of region3,4, ncr only)\nraw_data = gpd.read_file(\"../data/region34ncr_osm_pois.geojson\")\n\nCPU times: user 94.6 ms, sys: 4.12 ms, total: 98.7 ms\nWall time: 96.1 ms\n\n\n\nraw_data.columns.values\n\narray(['osm_id', 'code', 'fclass', 'name', 'BARANGAY_CODE', 'geometry'],\n      dtype=object)\n\n\n\nraw_data.head()\n\n\n\n\n\n\n\n\n\nosm_id\ncode\nfclass\nname\nBARANGAY_CODE\ngeometry\n\n\n\n\n0\n311568428\n2701\ntourist_info\nManila American Cemetery and Memorial Visitor ...\n137602022\nPOINT (121.04852 14.54398)\n\n\n1\n672565496\n2701\ntourist_info\necopark paging and first aid station\n137404141\nPOINT (121.07479 14.71173)\n\n\n2\n672565498\n2701\ntourist_info\nEcopark ticket counter\n137404141\nPOINT (121.07326 14.71291)\n\n\n3\n1585389544\n2701\ntourist_info\nArea Formerly Occupied by Fort Bonifacio Museum\n137602021\nPOINT (121.05837 14.55071)\n\n\n4\n1834855424\n2701\ntourist_info\nLotto Booth\n137601020\nPOINT (120.99216 14.42312)\n\n\n\n\n\n\n\n\n\nax = aoi.plot(\n    ax=plt.axes(),\n    facecolor=\"none\",\n    edgecolor=[\n        \"#C62828\",\n        \"#283593\",\n        \"#FF9800\",\n    ],\n)\nax = raw_data.plot(ax=ax)\n\n\n\n\n\n\n\n\nCompute POIs count per region\n\naoi = vzs.create_zonal_stats(\n    aoi,\n    raw_data,\n    overlap_method=\"intersects\",\n    aggregations=[{\"func\": \"count\"}],\n)\n\nCPU times: user 274 ms, sys: 0 ns, total: 274 ms\nWall time: 272 ms\n\n\nNew aoi with pois count in the column index_count. (The column name can be overridden as shown in the next example)\n\naoi\n\nCPU times: user 2 µs, sys: 0 ns, total: 2 µs\nWall time: 4.29 µs\n\n\n\n\n\n\n\n\n\n\nReg_Code\nReg_Name\nReg_Alt_Name\ngeometry\nindex_count\n\n\n\n\n0\n130000000\nNational Capital Region\nNCR\nMULTIPOLYGON (((121.03842 14.78525, 121.03815 ...\n880\n\n\n1\n030000000\nRegion III\nCentral Luzon\nMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...\n701\n\n\n2\n040000000\nRegion IV-A\nCalabarzon\nMULTIPOLYGON (((122.72165 13.36485, 122.72143 ...\n1253\n\n\n\n\n\n\n\n\n\n\nExample 2: Count attractions for Regions\n\nInput:\n\naoi - region3,4,ncr regions (Admin Level 1) (Central Luzon, NCR, Calabarzon) geometry (geom_type - polygon, multipolygon)\ndata - attractions: filtered Philippine pois (Central Luzon, NCR, Calabarzon only) (geom_type - points) with fclass == ‘attraction’\noverlap_method = ‘intersects’\naggregations:\n\ncount\n\nnumber of pois within aoi\noutput column name attractions\n\n\n\nOutput\n\naoi with attractions count\n\n\nFilter the raw data\n\n# select only 'attraction' pois\nattractions = raw_data[raw_data.fclass == \"attraction\"]\n\n\nattractions.head()\n\n\n\n\n\n\n\n\n\nosm_id\ncode\nfclass\nname\nBARANGAY_CODE\ngeometry\n\n\n\n\n49\n159473554\n2721\nattraction\nChinatown Arch\n133902005\nPOINT (120.97671 14.59672)\n\n\n50\n622413978\n2721\nattraction\nPeace Bell\n137404020\nPOINT (121.04934 14.65026)\n\n\n51\n625180701\n2721\nattraction\nThe Glass Garden\n137403027\nPOINT (121.08194 14.61932)\n\n\n52\n681222977\n2721\nattraction\nLa Madre Filipina\n133908008\nPOINT (120.97773 14.58172)\n\n\n53\n820634039\n2721\nattraction\nIndependence Flag Pole\n133908008\nPOINT (120.97659 14.58155)\n\n\n\n\n\n\n\n\nCreate zonal stats for filtered data. Add output key to specify output column name for count\n\naoi_attr = vzs.create_zonal_stats(\n    aoi, attractions, aggregations=[{\"func\": \"count\", \"output\": \"attractions\"}]\n)\n\nCPU times: user 290 ms, sys: 0 ns, total: 290 ms\nWall time: 288 ms\n\n\n\naoi_attr\n\n\n\n\n\n\n\n\n\nReg_Code\nReg_Name\nReg_Alt_Name\ngeometry\nattractions\n\n\n\n\n0\n130000000\nNational Capital Region\nNCR\nMULTIPOLYGON (((121.03842 14.78525, 121.03815 ...\n136\n\n\n1\n030000000\nRegion III\nCentral Luzon\nMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...\n205\n\n\n2\n040000000\nRegion IV-A\nCalabarzon\nMULTIPOLYGON (((122.72165 13.36485, 122.72143 ...\n312\n\n\n\n\n\n\n\n\n\n\nExample 3: Grid Tiles over POIs\n\nInput:\n\naoi - gridded tiles for Region 3 (Central Luzon) at 15km x 15km size\ndata - region 3 data filtered from philippine pois (geom_type - points)\noverlap_method = ‘intersects’\naggregations:\n\ncount\n\nnumber of pois within aoi\noutput column name:: pois_count\n\n\n\nOutput\n\naoi with pois count\n\n\n\n# load gridded tiles\ngrid_aoi = gpd.read_file(\"../data/region3_admin_grids.geojson\")\n\nCPU times: user 96.3 ms, sys: 0 ns, total: 96.3 ms\nWall time: 87.4 ms\n\n\n\nax = aoi[aoi.Reg_Name == \"Region III\"].plot(\n    ax=plt.axes(), facecolor=\"none\", edgecolor=\"#C62828\"\n)\nax = grid_aoi.plot(ax=ax, facecolor=\"none\", edgecolor=\"blue\")\n\n\n\n\n\n\n\n\n\nregion3_pois = gpd.read_file(\"../data/region3_osm_pois.geojson\")\n\nCPU times: user 50.4 ms, sys: 0 ns, total: 50.4 ms\nWall time: 48 ms\n\n\n\nregion3_pois.head()\n\n\n\n\n\n\n\n\n\nosm_id\ncode\nfclass\nname\nBARANGAY_CODE\ngeometry\n\n\n\n\n0\n560410986\n2701\ntourist_info\nGawad Kalinga Office\n031420009\nPOINT (121.08516 14.83601)\n\n\n1\n1244301672\n2701\ntourist_info\nN 15deg 26' 11.1\", E 120deg 25' 50.2\", El...\n036918006\nPOINT (120.43045 15.43663)\n\n\n2\n1666684393\n2701\ntourist_info\nEco Park Tourist Information & DENR Environmen...\n036918006\nPOINT (120.44958 15.46446)\n\n\n3\n1679992929\n2701\ntourist_info\nLa Paz Tarlac - Zaragoza Nueva Ecija Boundary\n034932027\nPOINT (120.75832 15.44284)\n\n\n4\n1714645729\n2701\ntourist_info\nLucy Pineda\n035409019\nPOINT (120.61452 15.23359)\n\n\n\n\n\n\n\n\n\nlen(region3_pois)\n\n701\n\n\n\nax = aoi[aoi.Reg_Name == \"Region III\"].plot(\n    ax=plt.axes(), facecolor=\"none\", edgecolor=\"#C62828\"\n)\nax = region3_pois.plot(ax=ax)\n\n\n\n\n\n\n\n\nCompute pois count per grid\n\ngrid_aoi = vzs.create_zonal_stats(\n    grid_aoi,\n    region3_pois,\n    overlap_method=\"intersects\",\n    aggregations=[{\"func\": \"count\", \"output\": \"pois_count\"}],\n)\n\nCPU times: user 33.9 ms, sys: 0 ns, total: 33.9 ms\nWall time: 31.5 ms\n\n\n\ngrid_aoi[grid_aoi.pois_count > 0].head()\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\npois_count\n\n\n\n\n11\n2\n28\nPOLYGON ((119.87566 15.62220, 119.92058 15.622...\n1.0\n\n\n14\n2\n31\nPOLYGON ((119.87566 15.75193, 119.92058 15.751...\n1.0\n\n\n18\n3\n23\nPOLYGON ((119.92058 15.40581, 119.96550 15.405...\n1.0\n\n\n21\n3\n26\nPOLYGON ((119.92058 15.53567, 119.96550 15.535...\n4.0\n\n\n22\n3\n27\nPOLYGON ((119.92058 15.57894, 119.96550 15.578...\n1.0\n\n\n\n\n\n\n\n\n\ngrid_aoi.pois_count.sum()\n\n701.0\n\n\n\n# show grids with at least 1 poi\nax = aoi[aoi.Reg_Name == \"Region III\"].plot(\n    ax=plt.axes(),\n    facecolor=\"none\",\n    edgecolor=[\n        \"#C62828\",\n    ],\n)\nax = grid_aoi[grid_aoi.pois_count > 0].plot(ax=ax, facecolor=\"none\", edgecolor=\"green\")\n\n\n\n\n\n\n\n\n\n\n\nUse case 2 - Stats on a metric column\n\nExample 1 Regions over Population per Bgy Level\n\nInput:\n\naoi - region3,4,ncr geometry (geom_type - polygon, multipolygon)\ndata - region3,4, ncr population data (geom_type - pois)\ndata_type: ‘individual_pois’\noverlap_method = ‘intersects’\naggregations:\n\nmetric_columns: ‘population’,‘men’, etc.\n\neach row in the data has a column: population, men, women, etc. with numeric value\naggregation_functions - ‘min’,‘max’, ‘mean’, ‘sum’, etc.\noutput_columns - ‘pop_min’, ‘pop_max’, for each column\n\n\n\nOutput:\n\naoi with new output columns pop_min, pop_max etc\n\n\nLoad 2020 Region 3, Region 4, and NCR Population Data at Barangay level (Admin Level 4)\n\n\n\n\n\n\nNote\n\n\n\nThe population data came from the 2020 Facebook HRSL Dataset filtered for barangays covering Regions 3, Region 4, and NCR and converted the geometries from polygons to points by computing their centroids (projected to EPSG:3969). The land area was computed by projecting to EPSG:3123 and getting the geometry.area .\n\n\n\n# load region3,4,ncr population data at barangay level\nregion34ncr_pop_data = gpd.read_file(\"../data/region34ncr_population_land.geojson\")\n\nCPU times: user 543 ms, sys: 9.45 ms, total: 552 ms\nWall time: 548 ms\n\n\n\nregion34ncr_pop_data.head()\n\n\n\n\n\n\n\n\n\nBARANGAY_CODE\npopulation\nmen\nwomen\nchildren_under_five\nyouth_15_24\nwomen_of_reproductive_age_15_49\nelderly_60_plus\nland_area\ngeometry\n\n\n\n\n0\n31420020.0\n1807\n920\n887\n202\n359\n468\n77\n2.183518e+05\nPOINT (121.05919 14.85825)\n\n\n1\n34915006.0\n3093\n1594\n1499\n352\n658\n826\n159\n7.327482e+06\nPOINT (120.99590 15.62242)\n\n\n2\n35403009.0\n4241\n2158\n2083\n474\n821\n1112\n256\n8.740450e+05\nPOINT (120.77730 15.14580)\n\n\n3\n35409023.0\n3373\n1750\n1623\n296\n611\n895\n244\n6.484611e+05\nPOINT (120.58052 15.22315)\n\n\n4\n35413009.0\n20884\n10539\n10344\n2198\n3893\n5589\n1260\n2.015571e+06\nPOINT (120.68523 15.09163)\n\n\n\n\n\n\n\n\n\nregion34ncr_pop_data.columns.values\n\narray(['BARANGAY_CODE', 'population', 'men', 'women',\n       'children_under_five', 'youth_15_24',\n       'women_of_reproductive_age_15_49', 'elderly_60_plus', 'land_area',\n       'geometry'], dtype=object)\n\n\nCompute zonal stats for regions 3,4,NCR * barangay count per region (bgy_count) * sum and mean for each population statistic (population, men, women, etc) * sum, mean, std, min, max for land area statistic\n\naoi = vzs.create_zonal_stats(\n    aoi,\n    region34ncr_pop_data,\n    aggregations=[\n        {\"func\": \"count\", \"output\": \"bgy_count\"},\n        {\n            \"column\": \"population\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"pop_total\", \"pop_avg\"],\n        },\n        {\"column\": \"men\", \"func\": [\"sum\", \"mean\"], \"output\": [\"men_total\", \"men_avg\"]},\n        {\n            \"column\": \"women\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"women_total\", \"women_avg\"],\n        },\n        {\n            \"column\": \"children_under_five\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"under5_total\", \"under5_avg\"],\n        },\n        {\n            \"column\": \"youth_15_24\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"youth_total\", \"youth_avg\"],\n        },\n        {\n            \"column\": \"women_of_reproductive_age_15_49\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"women_repro_total\", \"women_repro_avg\"],\n        },\n        {\n            \"column\": \"elderly_60_plus\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"elderly_total\", \"elderly_avg\"],\n        },\n        {\n            \"column\": \"land_area\",\n            \"func\": [\"sum\", \"mean\", \"min\", \"max\", \"std\"],\n            \"output\": [\"land_total\", \"land_avg\", \"land_min\", \"land_max\", \"land_std\"],\n        },\n    ],\n    overlap_method=\"intersects\",\n)\n\nCPU times: user 313 ms, sys: 9.88 ms, total: 322 ms\nWall time: 320 ms\n\n\n\naoi.columns.values\n\narray(['Reg_Code', 'Reg_Name', 'Reg_Alt_Name', 'geometry', 'bgy_count',\n       'pop_total', 'pop_avg', 'men_total', 'men_avg', 'women_total',\n       'women_avg', 'under5_total', 'under5_avg', 'youth_total',\n       'youth_avg', 'women_repro_total', 'women_repro_avg',\n       'elderly_total', 'elderly_avg', 'land_total', 'land_avg',\n       'land_min', 'land_max', 'land_std'], dtype=object)\n\n\n\naoi.head()\n\n\n\n\n\n\n\n\n\nReg_Code\nReg_Name\nReg_Alt_Name\ngeometry\nbgy_count\npop_total\npop_avg\nmen_total\nmen_avg\nwomen_total\n...\nyouth_avg\nwomen_repro_total\nwomen_repro_avg\nelderly_total\nelderly_avg\nland_total\nland_avg\nland_min\nland_max\nland_std\n\n\n\n\n0\n130000000\nNational Capital Region\nNCR\nMULTIPOLYGON (((121.03842 14.78525, 121.03815 ...\n1707\n12484535\n7313.728764\n6132828\n3592.752197\n6350844\n...\n1468.077329\n3699839\n2167.451084\n653306\n382.721734\n5.945955e+08\n3.483277e+05\n2890.947906\n2.750653e+07\n1.065422e+06\n\n\n1\n030000000\nRegion III\nCentral Luzon\nMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...\n3099\n10581859\n3414.604389\n5353309\n1727.431107\n5227035\n...\n655.773475\n2808810\n906.360116\n664150\n214.311068\n2.120885e+10\n6.843771e+06\n9589.467114\n3.496501e+08\n1.826504e+07\n\n\n2\n040000000\nRegion IV-A\nCalabarzon\nMULTIPOLYGON (((122.72165 13.36485, 122.72143 ...\n4010\n14081857\n3511.685037\n7035334\n1754.447382\n7044548\n...\n671.226185\n3857217\n961.899501\n780294\n194.587032\n1.551525e+10\n3.869139e+06\n3087.452331\n4.310269e+08\n1.119929e+07\n\n\n\n\n3 rows × 24 columns\n\n\n\n\n\n\nExample 2 : Grids over Population per Bgy Level\n\nInput:\n\naoi - region3 grids geometry (geom_type - polygon)\ndata - population data (geom_type - pois)\ndata_type: ‘individual_pois’\noverlap_method = ‘intersects’\naggregations:\n\nmetric_columns: ‘population’,‘men’, ‘land_area’\n\neach row in the data has a column population, men, women, including land with numeric value\naggregation_functions - ‘min’,‘max’, ‘mean’, ‘sum’, etc.\noutput_columns - ‘pop_min’, ‘pop_max’, for each\n\n\n\nOutput:\n\naoi with new columns pop_min, pop_max etc.\n\n\nLoad population and land POIs (Bgy level)\n\n\n\n\n\n\nNote\n\n\n\nThe dataset is similar to the previous one (Region 3, Region 4, and NCR) except that it has been filtered only data for the Region 3.\n\n\n\nregion3_pop_pois = gpd.read_file(\"../data/region3_population_pois.geojson\")\n\nCPU times: user 372 ms, sys: 0 ns, total: 372 ms\nWall time: 370 ms\n\n\n\nregion3_pop_pois.head()\n\n\n\n\n\n\n\n\n\nBARANGAY_CODE\npopulation\nmen\nwomen\nchildren_under_five\nyouth_15_24\nwomen_of_reproductive_age_15_49\nelderly_60_plus\nland_area\nReg_Name\ngeometry\n\n\n\n\n0\n31420020.0\n1807\n920\n887\n202\n359\n468\n77\n2.183518e+05\nRegion III\nPOINT (121.05919 14.85825)\n\n\n1\n34915006.0\n3093\n1594\n1499\n352\n658\n826\n159\n7.327482e+06\nRegion III\nPOINT (120.99590 15.62242)\n\n\n2\n35403009.0\n4241\n2158\n2083\n474\n821\n1112\n256\n8.740450e+05\nRegion III\nPOINT (120.77730 15.14580)\n\n\n3\n35409023.0\n3373\n1750\n1623\n296\n611\n895\n244\n6.484611e+05\nRegion III\nPOINT (120.58052 15.22315)\n\n\n4\n35413009.0\n20884\n10539\n10344\n2198\n3893\n5589\n1260\n2.015571e+06\nRegion III\nPOINT (120.68523 15.09163)\n\n\n\n\n\n\n\n\n\nregion3_pop_pois.columns.values\n\narray(['BARANGAY_CODE', 'population', 'men', 'women',\n       'children_under_five', 'youth_15_24',\n       'women_of_reproductive_age_15_49', 'elderly_60_plus', 'land_area',\n       'Reg_Name', 'geometry'], dtype=object)\n\n\nCreate zonal stats (same as previous example, but now for a more granular level for region 3 only)\n\ngrid_aoi = vzs.create_zonal_stats(\n    grid_aoi,\n    region3_pop_pois,\n    aggregations=[\n        {\"func\": \"count\", \"output\": \"bgy_count\"},\n        {\n            \"column\": \"population\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"pop_total\", \"pop_avg\"],\n        },\n        {\"column\": \"men\", \"func\": [\"sum\", \"mean\"], \"output\": [\"men_total\", \"men_avg\"]},\n        {\n            \"column\": \"women\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"women_total\", \"women_avg\"],\n        },\n        {\n            \"column\": \"children_under_five\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"under5_total\", \"under5_avg\"],\n        },\n        {\n            \"column\": \"youth_15_24\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"youth_total\", \"youth_avg\"],\n        },\n        {\n            \"column\": \"women_of_reproductive_age_15_49\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"women_repro_total\", \"women_repro_avg\"],\n        },\n        {\n            \"column\": \"elderly_60_plus\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"elderly_total\", \"elderly_avg\"],\n        },\n        {\n            \"column\": \"land_area\",\n            \"func\": [\"sum\", \"mean\", \"min\", \"max\", \"std\"],\n            \"output\": [\"land_total\", \"land_avg\", \"land_min\", \"land_max\", \"land_std\"],\n        },\n    ],\n    overlap_method=\"intersects\",\n)\n\nCPU times: user 115 ms, sys: 10.5 ms, total: 126 ms\nWall time: 121 ms\n\n\n\ngrid_aoi.head()\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\npois_count\nbgy_count\npop_total\npop_avg\nmen_total\nmen_avg\nwomen_total\n...\nyouth_avg\nwomen_repro_total\nwomen_repro_avg\nelderly_total\nelderly_avg\nland_total\nland_avg\nland_min\nland_max\nland_std\n\n\n\n\n0\n0\n30\nPOLYGON ((119.78583 15.70870, 119.83075 15.708...\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n...\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n\n\n1\n0\n31\nPOLYGON ((119.78583 15.75193, 119.83075 15.751...\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n...\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n\n\n2\n0\n32\nPOLYGON ((119.78583 15.79516, 119.83075 15.795...\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n...\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n\n\n3\n1\n30\nPOLYGON ((119.83075 15.70870, 119.87566 15.708...\nNaN\n1.0\n3415.0\n3415.0\n1744.0\n1744.0\n1670.0\n...\n648.0\n848.0\n848.0\n229.0\n229.0\n5.552401e+06\n5.552401e+06\n5.552401e+06\n5.552401e+06\nNaN\n\n\n4\n1\n32\nPOLYGON ((119.83075 15.79516, 119.87566 15.795...\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n...\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\nNaN\n\n\n\n\n5 rows × 24 columns\n\n\n\n\nShow grids with bgy_count > 0 and bgy_count == 0\n\nax = aoi[aoi.Reg_Name == \"Region III\"].plot(\n    ax=plt.axes(), facecolor=\"none\", edgecolor=\"black\"\n)\nax = grid_aoi[grid_aoi.bgy_count.notna()].plot(\n    ax=ax, facecolor=\"none\", edgecolor=\"blue\"\n)\nax = grid_aoi[grid_aoi.bgy_count.isna()].plot(ax=ax, facecolor=\"none\", edgecolor=\"red\")",
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+    "title": "Vector Zonal Stats Tutorial",
+    "section": "Generating Bing Tile Grid Zonal Stats",
+    "text": "Generating Bing Tile Grid Zonal Stats\nIf our areas of interest use Bing Tile Grids (with the associated quadkeys),\nwe can use a much faster way of generating zonal stats by pre-computing the quadkeys for our data.\n\n\n\n\n\n\nNote\n\n\n\nYou can use the geowrangler.grids modules BingTileGridGenerator to convert your AOI into a Bing Tile Grid AOI.\n\n\n\nExample 1: Count POIs for Region 3 Bing Tile Grid AOI\n\nInput:\n\naoi bing tile grids zoom level 13 - region3 (Admin Level 1) (Central Luzon) geometry (geom_type - polygon, multipolygon)\ndata - Region 3 pois (geom_type - points)\naggregations:\n\ncount - number of pois within aoi\n\n\nOutput\n\naoi with pois count (default output column: index_count)\n\n\nLoad our bing tile grid aoi\n\n# load region3_admin area in bing tile grid ('ADM level 1 - Philippines, region 3, zoom level13')\n\nregion3_bingtile_grid = gpd.read_file(\"../data/region3_bingtile_grid13.geojson\")\n\n\nregion3_bingtile_grid.head()\n\n\n\n\n\n\n\n\n\nquadkey\ngeometry\n\n\n\n\n0\n1323030303301\nPOLYGON ((120.10254 14.73239, 120.10254 14.774...\n\n\n1\n1323030303300\nPOLYGON ((120.05859 14.73239, 120.05859 14.774...\n\n\n2\n1323030303311\nPOLYGON ((120.19043 14.73239, 120.19043 14.774...\n\n\n3\n1323030303133\nPOLYGON ((120.19043 14.77488, 120.19043 14.817...\n\n\n4\n1323030303131\nPOLYGON ((120.19043 14.81737, 120.19043 14.859...\n\n\n\n\n\n\n\n\n\nax = plt.axes()\nax = aoi[aoi.Reg_Name == \"Region III\"].plot(ax=ax, facecolor=\"none\", edgecolor=\"blue\")\nax = region3_bingtile_grid.plot(ax=ax, facecolor=\"none\", edgecolor=\"red\")\n\n\n\n\n\n\n\n\nLets compute the quadkeys for our Region3 pois\n\nDATA_ZOOM_LEVEL = 23  # for pois, it can be as high as 24\n\n\nregion3_pois_quadkeys = vzs.compute_quadkey(region3_pois, DATA_ZOOM_LEVEL)\n\nCPU times: user 236 ms, sys: 320 µs, total: 236 ms\nWall time: 233 ms\n\n\n\nregion3_pois_quadkeys.head()\n\n\n\n\n\n\n\n\n\nosm_id\ncode\nfclass\nname\nBARANGAY_CODE\ngeometry\nquadkey\n\n\n\n\n0\n560410986\n2701\ntourist_info\nGawad Kalinga Office\n031420009\nPOINT (121.08516 14.83601)\n13230312020312101332220\n\n\n1\n1244301672\n2701\ntourist_info\nN 15deg 26' 11.1\", E 120deg 25' 50.2\", El...\n036918006\nPOINT (120.43045 15.43663)\n13230301323000331033200\n\n\n2\n1666684393\n2701\ntourist_info\nEco Park Tourist Information & DENR Environmen...\n036918006\nPOINT (120.44958 15.46446)\n13230301321223132232132\n\n\n3\n1679992929\n2701\ntourist_info\nLa Paz Tarlac - Zaragoza Nueva Ecija Boundary\n034932027\nPOINT (120.75832 15.44284)\n13230301332111310110220\n\n\n4\n1714645729\n2701\ntourist_info\nLucy Pineda\n035409019\nPOINT (120.61452 15.23359)\n13230303110021032011230\n\n\n\n\n\n\n\n\nLets compute the zonal stats for each grid as the pois_count. > Notice the computation time is pretty fast\n\nregion3_bingtile_pois = vzs.create_bingtile_zonal_stats(\n    region3_bingtile_grid,\n    region3_pois_quadkeys,\n    aggregations=[dict(func=\"count\", output=\"pois_count\", fillna=True)],\n)\n\nCPU times: user 71.6 ms, sys: 0 ns, total: 71.6 ms\nWall time: 69.3 ms\n\n\n\nregion3_bingtile_pois[region3_bingtile_pois.pois_count > 0].head()\n\n\n\n\n\n\n\n\n\nquadkey\ngeometry\npois_count\n\n\n\n\n5\n1323030312020\nPOLYGON ((120.23438 14.81737, 120.23438 14.859...\n8.0\n\n\n8\n1323030121200\nPOLYGON ((119.88281 15.74996, 119.88281 15.792...\n1.0\n\n\n11\n1323030121222\nPOLYGON ((119.88281 15.62304, 119.88281 15.665...\n1.0\n\n\n13\n1323030123002\nPOLYGON ((119.88281 15.53838, 119.88281 15.580...\n1.0\n\n\n22\n1323030123001\nPOLYGON ((119.92676 15.58071, 119.92676 15.623...\n1.0\n\n\n\n\n\n\n\n\n\nax = aoi[aoi.Reg_Name == \"Region III\"].plot(\n    ax=plt.axes(), facecolor=\"none\", edgecolor=\"black\"\n)\nax = region3_bingtile_pois[region3_bingtile_pois.pois_count > 0].plot(\n    column=\"pois_count\", ax=ax, edgecolor=\"blue\", alpha=0.4\n)\nax = region3_bingtile_pois[region3_bingtile_pois.pois_count == 0].plot(\n    ax=ax, facecolor=\"none\", edgecolor=\"red\", alpha=0.4\n)\n\n\n\n\n\n\n\n\n\n\nExample 2: Population stats for Region 3 Bing Tile Grid AOI\n\nInput:\n\naoi bing tile grids zoom level 13 - region3 (Admin Level 1) (Central Luzon) geometry (geom_type - polygon, multipolygon)\ndata - Region 3 pop data (geom_type - points)\naggregations:\n\npop totals, avg, men total and avg, etc , barangay counts, etc.\n\n\nOutput\n\naoi bing tile grids zoom level 13 with additional stats\n\n\nWe can also reuse the region34ncr population data to add more zonal statistics to our region3 bingtile pois\n\n# Add quadkeys to region34ncr population data\nregion34ncr_pop_data = vzs.compute_quadkey(\n    region34ncr_pop_data, DATA_ZOOM_LEVEL, inplace=True\n)  # inplace saves copying\n\nCPU times: user 1.5 s, sys: 0 ns, total: 1.5 s\nWall time: 1.5 s\n\n\n\nregion34ncr_pop_data.head()\n\n\n\n\n\n\n\n\n\nBARANGAY_CODE\npopulation\nmen\nwomen\nchildren_under_five\nyouth_15_24\nwomen_of_reproductive_age_15_49\nelderly_60_plus\nland_area\ngeometry\nquadkey\n\n\n\n\n0\n31420020.0\n1807\n920\n887\n202\n359\n468\n77\n2.183518e+05\nPOINT (121.05919 14.85825)\n13230312020301100210231\n\n\n1\n34915006.0\n3093\n1594\n1499\n352\n658\n826\n159\n7.327482e+06\nPOINT (120.99590 15.62242)\n13230310220010101012220\n\n\n2\n35403009.0\n4241\n2158\n2083\n474\n821\n1112\n256\n8.740450e+05\nPOINT (120.77730 15.14580)\n13230303111220301103213\n\n\n3\n35409023.0\n3373\n1750\n1623\n296\n611\n895\n244\n6.484611e+05\nPOINT (120.58052 15.22315)\n13230303101131332000021\n\n\n4\n35413009.0\n20884\n10539\n10344\n2198\n3893\n5589\n1260\n2.015571e+06\nPOINT (120.68523 15.09163)\n13230303112102100203003\n\n\n\n\n\n\n\n\n\nregion3_bingtile_pois_pop_data = vzs.create_bingtile_zonal_stats(\n    region3_bingtile_pois,  # reuse from previous example\n    region34ncr_pop_data,  # updated with quadkeys\n    aggregations=[\n        {\"func\": \"count\", \"output\": \"bgy_count\"},\n        {\n            \"column\": \"population\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"pop_total\", \"pop_avg\"],\n        },\n        {\"column\": \"men\", \"func\": [\"sum\", \"mean\"], \"output\": [\"men_total\", \"men_avg\"]},\n        {\n            \"column\": \"women\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"women_total\", \"women_avg\"],\n        },\n        {\n            \"column\": \"children_under_five\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"under5_total\", \"under5_avg\"],\n        },\n        {\n            \"column\": \"youth_15_24\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"youth_total\", \"youth_avg\"],\n        },\n        {\n            \"column\": \"women_of_reproductive_age_15_49\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"women_repro_total\", \"women_repro_avg\"],\n        },\n        {\n            \"column\": \"elderly_60_plus\",\n            \"func\": [\"sum\", \"mean\"],\n            \"output\": [\"elderly_total\", \"elderly_avg\"],\n        },\n        {\n            \"column\": \"land_area\",\n            \"func\": [\"sum\", \"mean\", \"min\", \"max\", \"std\"],\n            \"output\": [\"land_total\", \"land_avg\", \"land_min\", \"land_max\", \"land_std\"],\n        },\n    ],\n)\n\nCPU times: user 528 ms, sys: 0 ns, total: 528 ms\nWall time: 526 ms\n\n\n\nregion3_bingtile_pois_pop_data[region3_bingtile_pois_pop_data.bgy_count.notna()].head()\n\n\n\n\n\n\n\n\n\nquadkey\ngeometry\npois_count\nbgy_count\npop_total\npop_avg\nmen_total\nmen_avg\nwomen_total\nwomen_avg\n...\nyouth_avg\nwomen_repro_total\nwomen_repro_avg\nelderly_total\nelderly_avg\nland_total\nland_avg\nland_min\nland_max\nland_std\n\n\n\n\n5\n1323030312020\nPOLYGON ((120.23438 14.81737, 120.23438 14.859...\n8.0\n5.0\n40890.0\n8178.000000\n20398.0\n4079.600000\n20490.0\n4098.000000\n...\n1662.000000\n11493.0\n2298.600000\n2337.0\n467.4\n4.156777e+06\n8.313554e+05\n1.319242e+05\n2.503691e+06\n9.727404e+05\n\n\n6\n1323030120313\nPOLYGON ((119.83887 15.70766, 119.83887 15.749...\n0.0\n1.0\n3415.0\n3415.000000\n1744.0\n1744.000000\n1670.0\n1670.000000\n...\n648.000000\n848.0\n848.000000\n229.0\n229.0\n5.552401e+06\n5.552401e+06\n5.552401e+06\n5.552401e+06\nNaN\n\n\n7\n1323030121022\nPOLYGON ((119.88281 15.79225, 119.88281 15.834...\n0.0\n1.0\n4742.0\n4742.000000\n2438.0\n2438.000000\n2304.0\n2304.000000\n...\n828.000000\n1114.0\n1114.000000\n370.0\n370.0\n5.485826e+06\n5.485826e+06\n5.485826e+06\n5.485826e+06\nNaN\n\n\n8\n1323030121200\nPOLYGON ((119.88281 15.74996, 119.88281 15.792...\n1.0\n7.0\n21755.0\n3107.857143\n11047.0\n1578.142857\n10707.0\n1529.571429\n...\n587.857143\n5364.0\n766.285714\n1631.0\n233.0\n1.343751e+07\n1.919645e+06\n5.222719e+05\n4.416378e+06\n1.395113e+06\n\n\n9\n1323030121202\nPOLYGON ((119.88281 15.70766, 119.88281 15.749...\n0.0\n5.0\n10146.0\n2029.200000\n5257.0\n1051.400000\n4887.0\n977.400000\n...\n376.600000\n2393.0\n478.600000\n668.0\n133.6\n1.836666e+07\n3.673332e+06\n2.364678e+06\n5.247429e+06\n1.068957e+06\n\n\n\n\n5 rows × 23 columns\n\n\n\n\n\nax = aoi[aoi.Reg_Name == \"Region III\"].plot(\n    ax=plt.axes(), facecolor=\"none\", edgecolor=\"black\"\n)\nax = region3_bingtile_pois_pop_data[\n    region3_bingtile_pois_pop_data.bgy_count.notna()\n].plot(column=\"pop_total\", ax=ax, edgecolor=\"blue\", alpha=0.4)\nax = region3_bingtile_pois_pop_data[\n    region3_bingtile_pois_pop_data.bgy_count.isna()\n].plot(ax=ax, facecolor=\"none\", edgecolor=\"red\", alpha=0.4)",
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+    "text": "A basic introduction to using tile clustering",
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+    "text": "Summary\nClusters adjacent grid tiles together. Usage of this function assumes that the input is a grid dataset.",
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+    "text": "How does it work?\nTile clustering works by assigning the same ID to grid cells belonging to the same cluster. There are options to: (a) cluster adjacent cells by category, (b) cluster grid cells via adjacent edges or corners.\n\nTileClustering().cluster_tiles(df, category_col)\n\n\n\n\n\n\n\n\n\n\n\ntype\ndefault\noptional/required\ndetails\n\n\n\n\ncluster_type\nString\nfour_way\noptional\nLeave blank () if using four_way cluster_type (top, bottom, left, right sides of a cell). Put in eight_way if preference is to cluster via four sides and corners of a cell.\n\n\ndf\nGeoDataFrame\nnone\nrequired\ndataframe for clustering\n\n\ncategory_col\nString\nnone\noptional\ncolumn for category if need to cluster adjacent cells by category\n\n\n\nA technical step-by-step explanation of how TileClustering().cluster_tiles works is detailed in the cell blocks below. An example on how to use it with its arguments is shown in the sample use case section thereafter.\n\nDefine the class.\n\nclass TileClustering:\n    def __init__(\n        self,\n        cluster_type: str = \"four_way\",\n    ) -> None:\n\n        assert cluster_type in [\"four_way\", \"eight_way\"]\n        self.cluster_type = cluster_type\n        self.tile_cluster_col = \"tile_cluster\"\n\nDefine the function.\n\n@patch\ndef cluster_tiles(\n    self: TileClustering,\n    df: pd.DataFrame,\n    grid_x_col=\"x\",\n    grid_y_col=\"y\",\n    category_col: Optional[str] = None,\n) -> pd.DataFrame:\n    \"\"\"\n    Appends the cluster ID for each square grid cell\n    \"\"\"\n\n    if category_col is None:\n        cluster_df = self._cluster_tiles_single(df, grid_x_col, grid_y_col)\n    else:\n        assert (\n            not df[category_col].isnull().any()\n        ), f\"There shouldn't be null values for {category_col}\"\n        unique_categories = df[category_col].unique().tolist()\n\n        cluster_df_list = []\n        for i, category in enumerate(unique_categories, start=1):\n            bool_mask = df[category_col] == category\n            filtered_df = df.loc[bool_mask, :].copy()\n            cluster_filtered_df = self._cluster_tiles_single(\n                filtered_df, grid_x_col, grid_y_col\n            )\n            cluster_filtered_df[self.tile_cluster_col] = cluster_filtered_df[\n                self.tile_cluster_col\n            ].apply(lambda key: f\"{key}-{i}\")\n            cluster_df_list.append(cluster_filtered_df)\n        cluster_df = pd.concat(cluster_df_list, axis=0, ignore_index=True)\n\n    df = pd.merge(left=df, right=cluster_df, on=[grid_x_col, grid_y_col], how=\"left\")\n\n    return df\n\nCategorize commands depending on input of category_col. Append cluster ID for each grid cell.\n\n    # no entry in category_col - run the patched function _cluster_tiles_single\n    if category_col is None:\n        cluster_df = self._cluster_tiles_single(df, grid_x_col, grid_y_col)\n\n    # entry in category_col - make sure that all rows have values\n    else:\n        assert (\n            not df[category_col].isnull().any()\n        ), f\"There shouldn't be null values for {category_col}\"\n        unique_categories = df[category_col].unique().tolist()\n\n    # append cluster ID for each grid cell\n        cluster_df_list = []\n        for i, category in enumerate(unique_categories, start=1):\n            bool_mask = df[category_col] == category\n            filtered_df = df.loc[bool_mask, :].copy()\n            cluster_filtered_df = self._cluster_tiles_single(\n                filtered_df, grid_x_col, grid_y_col\n            )\n            cluster_filtered_df[self.tile_cluster_col] = cluster_filtered_df[\n                self.tile_cluster_col\n            ].apply(lambda key: f\"{key}-{i}\")\n            cluster_df_list.append(cluster_filtered_df)\n        cluster_df = pd.concat(cluster_df_list, axis=0, ignore_index=True)\n\nMerge grid cells with same cluster ID.\n\n    df = pd.merge(left=df, right=cluster_df, on=[grid_x_col, grid_y_col], how=\"left\")\n\nReturn output dataframe.\n\n    return df\n\nOther @patch functions\n\n_cluster_tiles_single - called when there is no entry in category_col\n@patch\ndef _cluster_tiles_single(\n    self: TileClustering,\n    df: pd.DataFrame,\n    grid_x_col=\"x\",\n    grid_y_col=\"y\",\n) -> pd.DataFrame:\n    \"\"\"\n    Performs tile clustering on a single category\n    \"\"\"\n\n    if self.tile_cluster_col in df.columns:\n        raise ValueError(\n            f\"{self.tile_cluster_col} already exists as a column. Please rename\"\n        )\n\n    grid_x = df[grid_x_col]\n    grid_y = df[grid_y_col]\n\n    self.grid_idx = set(zip(grid_x, grid_y))\n\n    self.tile_cluster_dict = {}\n    self.cluster_id = 0\n\n    for key in self.grid_idx:\n        if key not in self.tile_cluster_dict.keys():\n            self.cluster_id += 1\n\n            # reset the call stack per iteration\n            self.call_stack = deque()\n            self._dfs_connected_components(key)\n\n    cluster_df = pd.DataFrame.from_dict(\n        self.tile_cluster_dict, orient=\"index\", columns=[self.tile_cluster_col]\n    )\n    cluster_df = cluster_df.reset_index()\n    cluster_df[grid_x_col] = cluster_df[\"index\"].apply(lambda idx: idx[0])\n    cluster_df[grid_y_col] = cluster_df[\"index\"].apply(lambda idx: idx[1])\n    cluster_df = cluster_df.drop(columns=\"index\")\n\n    return cluster_df\n_get_adjacent_keys - defines how four_way and eight_way clustering works\n@patch\ndef _get_adjacent_keys(\n    self: TileClustering,\n    key: Tuple[int, int],\n) -> List[Tuple[int, int]]:\n\n    x_idx = key[0]\n    y_idx = key[1]\n\n    east_key = (x_idx + 1, y_idx)\n    west_key = (x_idx - 1, y_idx)\n    south_key = (x_idx, y_idx - 1)\n    north_key = (x_idx, y_idx + 1)\n\n    if self.cluster_type == \"four_way\":\n        adjacent_keys = [east_key, west_key, south_key, north_key]\n\n    if self.cluster_type == \"eight_way\":\n        northeast_key = (x_idx + 1, y_idx + 1)\n        northwest_key = (x_idx - 1, y_idx + 1)\n        southeast_key = (x_idx + 1, y_idx - 1)\n        southwest_key = (x_idx - 1, y_idx - 1)\n\n        adjacent_keys = [\n            east_key,\n            west_key,\n            south_key,\n            north_key,\n            northeast_key,\n            northwest_key,\n            southeast_key,\n            southwest_key,\n        ]\n\n    return adjacent_keys\n_dfs_connected_components - a non-recursive depth-first search implementation of connected components\n@patch\ndef _dfs_connected_components(\n    self: TileClustering,\n    key: Tuple[int, int],\n) -> None:\n\n    self.call_stack.append(key)\n    while self.call_stack:\n        ref_key = self.call_stack.pop()\n\n        # check if key exists in the first place\n        if ref_key in self.grid_idx:\n            # check if adjacent key has already been assigned\n            if ref_key not in self.tile_cluster_dict.keys():\n                self.tile_cluster_dict[ref_key] = self.cluster_id\n\n                adjacent_keys = self._get_adjacent_keys(ref_key)\n                for adjacent_key in adjacent_keys:\n                    self.call_stack.append(adjacent_key)",
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+    "section": "Sample use case- Clustering areas based on scores",
+    "text": "Sample use case- Clustering areas based on scores\nInput: - grid_gdf5k - GeoDataFrame of randomly scored 5km x 5km grid cells - class - category column for basis of of clustering\nOutput: - clustered grid based on class\n\nStep 1: Import packages\n\nimport geopandas as gpd\nimport numpy as np\n\nimport geowrangler.grids as grids\n\nimport geowrangler.tile_clustering as tile_clustering\n\n\n\nStep 2: Load GeoDataFrame and generate grid\n\ngrid_generator5k = grids.SquareGridGenerator(5_000)\ngrid_gdf5k = grid_generator5k.generate_grid(region3_gdf)\n\n\ngrid_gdf5k\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\n\n\n\n\n0\n7\n8\nPOLYGON ((120.10024 14.75528, 120.14516 14.755...\n\n\n1\n6\n8\nPOLYGON ((120.05533 14.75528, 120.10024 14.755...\n\n\n2\n9\n8\nPOLYGON ((120.19008 14.75528, 120.23499 14.755...\n\n\n3\n2\n24\nPOLYGON ((119.87566 15.44910, 119.92058 15.449...\n\n\n4\n2\n25\nPOLYGON ((119.87566 15.49239, 119.92058 15.492...\n\n\n...\n...\n...\n...\n\n\n1069\n54\n44\nPOLYGON ((122.21128 16.31312, 122.25620 16.313...\n\n\n1070\n54\n45\nPOLYGON ((122.21128 16.35623, 122.25620 16.356...\n\n\n1071\n54\n46\nPOLYGON ((122.21128 16.39932, 122.25620 16.399...\n\n\n1072\n54\n47\nPOLYGON ((122.21128 16.44240, 122.25620 16.442...\n\n\n1073\n54\n48\nPOLYGON ((122.21128 16.48548, 122.25620 16.485...\n\n\n\n\n1074 rows × 3 columns\n\n\n\n\n\ngrid_gdf5k.plot();\n\n\n\n\n\n\n\n\n\n\nStep 3: Assign scores\n\ngrid_gdf5k\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\nscore\nclass\n\n\n\n\n0\n7\n8\nPOLYGON ((120.10024 14.75528, 120.14516 14.755...\n0.154930\nFalse\n\n\n1\n6\n8\nPOLYGON ((120.05533 14.75528, 120.10024 14.755...\n0.953133\nTrue\n\n\n2\n9\n8\nPOLYGON ((120.19008 14.75528, 120.23499 14.755...\n0.719994\nTrue\n\n\n3\n2\n24\nPOLYGON ((119.87566 15.44910, 119.92058 15.449...\n0.283082\nFalse\n\n\n4\n2\n25\nPOLYGON ((119.87566 15.49239, 119.92058 15.492...\n0.626144\nFalse\n\n\n...\n...\n...\n...\n...\n...\n\n\n1069\n54\n44\nPOLYGON ((122.21128 16.31312, 122.25620 16.313...\n0.862839\nTrue\n\n\n1070\n54\n45\nPOLYGON ((122.21128 16.35623, 122.25620 16.356...\n0.659405\nFalse\n\n\n1071\n54\n46\nPOLYGON ((122.21128 16.39932, 122.25620 16.399...\n0.863707\nTrue\n\n\n1072\n54\n47\nPOLYGON ((122.21128 16.44240, 122.25620 16.442...\n0.246483\nFalse\n\n\n1073\n54\n48\nPOLYGON ((122.21128 16.48548, 122.25620 16.485...\n0.785704\nTrue\n\n\n\n\n1074 rows × 5 columns\n\n\n\n\n\n\nStep 4: Tile clustering\n\noutput = tile_clustering.TileClustering().cluster_tiles(\n    df=grid_gdf5k, category_col=\"class\"\n)\n\n\noutput\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\nscore\nclass\ntile_cluster\n\n\n\n\n0\n7\n8\nPOLYGON ((120.10024 14.75528, 120.14516 14.755...\n0.154930\nFalse\n1-1\n\n\n1\n6\n8\nPOLYGON ((120.05533 14.75528, 120.10024 14.755...\n0.953133\nTrue\n123-2\n\n\n2\n9\n8\nPOLYGON ((120.19008 14.75528, 120.23499 14.755...\n0.719994\nTrue\n2-2\n\n\n3\n2\n24\nPOLYGON ((119.87566 15.44910, 119.92058 15.449...\n0.283082\nFalse\n1-1\n\n\n4\n2\n25\nPOLYGON ((119.87566 15.49239, 119.92058 15.492...\n0.626144\nFalse\n1-1\n\n\n...\n...\n...\n...\n...\n...\n...\n\n\n1069\n54\n44\nPOLYGON ((122.21128 16.31312, 122.25620 16.313...\n0.862839\nTrue\n73-2\n\n\n1070\n54\n45\nPOLYGON ((122.21128 16.35623, 122.25620 16.356...\n0.659405\nFalse\n2-1\n\n\n1071\n54\n46\nPOLYGON ((122.21128 16.39932, 122.25620 16.399...\n0.863707\nTrue\n36-2\n\n\n1072\n54\n47\nPOLYGON ((122.21128 16.44240, 122.25620 16.442...\n0.246483\nFalse\n2-1\n\n\n1073\n54\n48\nPOLYGON ((122.21128 16.48548, 122.25620 16.485...\n0.785704\nTrue\n74-2\n\n\n\n\n1074 rows × 6 columns\n\n\n\n\n\noutput.plot(column=\"class\", categorical=True, cmap=\"Spectral\");",
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+    "text": "Test data\nCreate sample scores for square grid cells and cluster the cells\n\nimport geopandas as gpd\nimport numpy as np\n\nfrom geowrangler import grids\n\n\nnp.random.seed(1562)\n\nregion3_gdf = gpd.read_file(\"../data/region3_admin.geojson\")\n\n\ngrid_generator5k = grids.SquareGridGenerator(5_000)\ngrid_gdf5k = grid_generator5k.generate_grid(region3_gdf)\ngrid_gdf5k.head()\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\n\n\n\n\n0\n7\n8\nPOLYGON ((120.10024 14.75528, 120.14516 14.755...\n\n\n1\n6\n8\nPOLYGON ((120.05533 14.75528, 120.10024 14.755...\n\n\n2\n9\n8\nPOLYGON ((120.19008 14.75528, 120.23499 14.755...\n\n\n3\n9\n9\nPOLYGON ((120.19008 14.79871, 120.23499 14.798...\n\n\n4\n10\n9\nPOLYGON ((120.23499 14.79871, 120.27991 14.798...\n\n\n\n\n\n\n\n\n\ngrid_gdf5k.plot()\n\n\n\n\n\n\n\n\n\ngrid_gdf5k[\"score\"] = np.random.random(len(grid_gdf5k))\ngrid_gdf5k[\"class\"] = grid_gdf5k[\"score\"] > 0.7\ngrid_gdf5k.head()\n\nCPU times: user 3.15 ms, sys: 1.32 ms, total: 4.47 ms\nWall time: 10 ms\n\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\nscore\nclass\n\n\n\n\n0\n7\n8\nPOLYGON ((120.10024 14.75528, 120.14516 14.755...\n0.761806\nTrue\n\n\n1\n6\n8\nPOLYGON ((120.05533 14.75528, 120.10024 14.755...\n0.012455\nFalse\n\n\n2\n9\n8\nPOLYGON ((120.19008 14.75528, 120.23499 14.755...\n0.446552\nFalse\n\n\n3\n9\n9\nPOLYGON ((120.19008 14.79871, 120.23499 14.798...\n0.669020\nFalse\n\n\n4\n10\n9\nPOLYGON ((120.23499 14.79871, 120.27991 14.798...\n0.815914\nTrue\n\n\n\n\n\n\n\n\n\ntileclustering = TileClustering()\ngrid_gdf5k = tileclustering.cluster_tiles(grid_gdf5k, category_col=\"class\")\ngrid_gdf5k.head()\n\nCPU times: user 25.8 ms, sys: 0 ns, total: 25.8 ms\nWall time: 47.6 ms\n\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\nscore\nclass\ntile_cluster\n\n\n\n\n0\n7\n8\nPOLYGON ((120.10024 14.75528, 120.14516 14.755...\n0.761806\nTrue\n6-1\n\n\n1\n6\n8\nPOLYGON ((120.05533 14.75528, 120.10024 14.755...\n0.012455\nFalse\n7-2\n\n\n2\n9\n8\nPOLYGON ((120.19008 14.75528, 120.23499 14.755...\n0.446552\nFalse\n1-2\n\n\n3\n9\n9\nPOLYGON ((120.19008 14.79871, 120.23499 14.798...\n0.669020\nFalse\n1-2\n\n\n4\n10\n9\nPOLYGON ((120.23499 14.79871, 120.27991 14.798...\n0.815914\nTrue\n23-1\n\n\n\n\n\n\n\n\n\ngrid_gdf5k[\"tile_cluster\"].nunique()\n\n160\n\n\n\ngrid_gdf5k.plot(column=\"class\", categorical=True, cmap=\"Spectral\")",
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+    "text": "Basic Usage\nGenerate area zonal stats for a GeoDataframe containing areas of interest with a vector data source with the nearest distance.\n\n\n\n\n\n\nNote\n\n\n\nThe data geometries can be points, lines or areas with distance computed from the closest points on both the aoi and the data source. Both the aoi and the data source must be projected using a “planar” CRS.\n\n\n\nimport geopandas as gpd\nimport matplotlib.pyplot as plt\nfrom shapely.geometry import Point\n\nimport geowrangler.distance_zonal_stats as dzs\n\n\nSimple Grid AOIs and Nearby POI/Area Data Example\n\nsimple_aoi = gpd.read_file(\"../data/simple_planar_aoi.geojson\")\nsimple_data = gpd.read_file(\"../data/simple_planar_data.geojson\")\n\n\nsimple_point_data = make_point_df(3, 5, offset_x=0.5, offset_y=3.0)\n\nGiven an aoi (simple_aoi), a sample area data source (simple_data), and a sample point data source\n\nsimple_aoi\n\n\n\n\n\n\n\n\n\ngeometry\n\n\n\n\n0\nPOLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...\n\n\n1\nPOLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...\n\n\n2\nPOLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...\n\n\n\n\n\n\n\n\n\nsimple_data\n\n\n\n\n\n\n\n\n\npopulation\ninternet_speed\ngeometry\n\n\n\n\n0\n100\n20.0\nPOLYGON ((0.250 0.000, 0.250 1.000, 1.250 1.00...\n\n\n1\n200\n10.0\nPOLYGON ((1.250 0.000, 1.250 1.000, 2.250 1.00...\n\n\n2\n300\n5.0\nPOLYGON ((2.250 0.000, 2.250 1.000, 3.250 1.00...\n\n\n\n\n\n\n\n\n\nsimple_point_data\n\n\n\n\n\n\n\n\n\ngeometry\npopulation\ninternet_speed\n\n\n\n\n0\nPOINT (0.500 3.000)\n100\n20.0\n\n\n1\nPOINT (0.500 4.000)\n600\n120.0\n\n\n2\nPOINT (0.500 5.000)\n1100\n220.0\n\n\n3\nPOINT (0.500 6.000)\n1600\n320.0\n\n\n4\nPOINT (0.500 7.000)\n2100\n420.0\n\n\n5\nPOINT (1.500 3.000)\n200\n10.0\n\n\n6\nPOINT (1.500 4.000)\n700\n110.0\n\n\n7\nPOINT (1.500 5.000)\n1200\n210.0\n\n\n8\nPOINT (1.500 6.000)\n1700\n310.0\n\n\n9\nPOINT (1.500 7.000)\n2200\n410.0\n\n\n10\nPOINT (2.500 3.000)\n300\n5.0\n\n\n11\nPOINT (2.500 4.000)\n800\n105.0\n\n\n12\nPOINT (2.500 5.000)\n1300\n205.0\n\n\n13\nPOINT (2.500 6.000)\n1800\n305.0\n\n\n14\nPOINT (2.500 7.000)\n2300\n405.0\n\n\n\n\n\n\n\n\nIn order correctly compute distances from the aoi to the data sources, we need to make sure that the aoi, data and point data geodataframes are using a planar CRS (i.e. gdf.crs.is_geographic == False)\n\nsimple_aoi.crs\n\n<Projected CRS: EPSG:3857>\nName: WGS 84 / Pseudo-Mercator\nAxis Info [cartesian]:\n- X[east]: Easting (metre)\n- Y[north]: Northing (metre)\nArea of Use:\n- name: World between 85.06°S and 85.06°N.\n- bounds: (-180.0, -85.06, 180.0, 85.06)\nCoordinate Operation:\n- name: Popular Visualisation Pseudo-Mercator\n- method: Popular Visualisation Pseudo Mercator\nDatum: World Geodetic System 1984 ensemble\n- Ellipsoid: WGS 84\n- Prime Meridian: Greenwich\n\n\n\nsimple_data.crs\n\n<Projected CRS: EPSG:3857>\nName: WGS 84 / Pseudo-Mercator\nAxis Info [cartesian]:\n- X[east]: Easting (metre)\n- Y[north]: Northing (metre)\nArea of Use:\n- name: World between 85.06°S and 85.06°N.\n- bounds: (-180.0, -85.06, 180.0, 85.06)\nCoordinate Operation:\n- name: Popular Visualisation Pseudo-Mercator\n- method: Popular Visualisation Pseudo Mercator\nDatum: World Geodetic System 1984 ensemble\n- Ellipsoid: WGS 84\n- Prime Meridian: Greenwich\n\n\n\nsimple_point_data.crs\n\n<Projected CRS: EPSG:3857>\nName: WGS 84 / Pseudo-Mercator\nAxis Info [cartesian]:\n- X[east]: Easting (metre)\n- Y[north]: Northing (metre)\nArea of Use:\n- name: World between 85.06°S and 85.06°N.\n- bounds: (-180.0, -85.06, 180.0, 85.06)\nCoordinate Operation:\n- name: Popular Visualisation Pseudo-Mercator\n- method: Popular Visualisation Pseudo Mercator\nDatum: World Geodetic System 1984 ensemble\n- Ellipsoid: WGS 84\n- Prime Meridian: Greenwich\n\n\nWe have an aoi (simple_aoi) and geodataframe containing sample data (simple_data) that overlaps the aoi. We also have simple point data which do not intersect with our AOIs.\n\nax = plt.axes()\nax = simple_data.plot(\n    ax=ax, color=[\"orange\", \"brown\", \"purple\"], edgecolor=\"yellow\", alpha=0.4\n)\nax = simple_aoi.plot(ax=ax, facecolor=\"none\", edgecolor=[\"r\", \"g\", \"b\"])\nax = simple_point_data.plot(ax=ax)\n\n\n\n\n\n\n\n\nThe red,green,blue outlines are the 3 regions of interest (aoi) while the orange,brown, purple areas are the data areas.The blue dots are data which do not intersect our AOIs.\n\nresults = dzs.create_distance_zonal_stats(\n    simple_aoi,\n    simple_point_data,\n    max_distance=7,\n    aggregations=[\n        dict(func=\"count\"),\n        dict(func=\"sum\", column=\"population\"),\n        dict(func=\"mean\", column=\"internet_speed\"),\n    ],\n)\n\nCPU times: user 50.6 ms, sys: 0 ns, total: 50.6 ms\nWall time: 47.8 ms\n\n\nThe zonal stats computed for the point data only includes those points nearest to each aoi. The data geometries within nearest distance (within 7.0 m) are the only ones considered.\n\n\n\n\n\n\nNote\n\n\n\nSetting the max_distance to None or a large value can cause a possible slowdown for large datasets. See this Geopandas reference for more details.\n\n\n\nresults\n\n\n\n\n\n\n\n\n\ngeometry\nindex_count\npopulation_sum\ninternet_speed_mean\nnearest\n\n\n\n\n0\nPOLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...\n1\n100\n20.0\n2.0\n\n\n1\nPOLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...\n1\n200\n10.0\n2.0\n\n\n2\nPOLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...\n1\n300\n5.0\n2.0\n\n\n\n\n\n\n\n\nData areas and geometries which overlap the aoi areas have a distance of 0.0 and are always the nearest geometries.\n\nresults2 = dzs.create_distance_zonal_stats(\n    simple_aoi,\n    simple_data,\n    max_distance=1,\n    aggregations=[\n        dict(func=\"count\"),\n        dict(func=\"sum\", column=\"population\"),\n        dict(func=\"mean\", column=\"internet_speed\"),\n    ],\n)\n\nCPU times: user 33.2 ms, sys: 1.29 ms, total: 34.5 ms\nWall time: 29.5 ms\n\n\n\nresults2\n\n\n\n\n\n\n\n\n\ngeometry\nindex_count\npopulation_sum\ninternet_speed_mean\nnearest\n\n\n\n\n0\nPOLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...\n1\n100\n20.0\n0.0\n\n\n1\nPOLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...\n2\n300\n15.0\n0.0\n\n\n2\nPOLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...\n2\n500\n7.5\n0.0\n\n\n\n\n\n\n\n\n\n\nCustom Grids and POIs Example\n\nregion3_admin_grids = gpd.read_file(\"../data/region3_admin_grids.geojson\")\n\nCPU times: user 97.8 ms, sys: 29.2 ms, total: 127 ms\nWall time: 100 ms\n\n\n\nregion34ncr_osm_pois = gpd.read_file(\"../data/region34ncr_osm_pois.geojson\")\n\nCPU times: user 115 ms, sys: 365 µs, total: 115 ms\nWall time: 113 ms\n\n\n\nax = plt.axes()\nax = region34ncr_osm_pois.plot(ax=ax)\nax = region3_admin_grids.plot(ax=ax, facecolor=\"none\", edgecolor=\"blue\")\n\n\n\n\n\n\n\n\n\nregion3_admin_grids = region3_admin_grids.to_crs(\"EPSG:3857\")  # convert to planar\nregion34ncr_osm_pois = region34ncr_osm_pois.to_crs(\"EPSG:3857\")\n\n\nregion34ncr_osm_pois\n\n\n\n\n\n\n\n\n\nosm_id\ncode\nfclass\nname\nBARANGAY_CODE\ngeometry\n\n\n\n\n0\n311568428\n2701\ntourist_info\nManila American Cemetery and Memorial Visitor ...\n137602022\nPOINT (13475059.129 1636701.151)\n\n\n1\n672565496\n2701\ntourist_info\necopark paging and first aid station\n137404141\nPOINT (13477983.748 1656000.058)\n\n\n2\n672565498\n2701\ntourist_info\nEcopark ticket counter\n137404141\nPOINT (13477813.318 1656135.868)\n\n\n3\n1585389544\n2701\ntourist_info\nArea Formerly Occupied by Fort Bonifacio Museum\n137602021\nPOINT (13476156.594 1637474.593)\n\n\n4\n1834855424\n2701\ntourist_info\nLotto Booth\n137601020\nPOINT (13468786.053 1622805.175)\n\n\n...\n...\n...\n...\n...\n...\n...\n\n\n2829\n1282790636\n2723\nmonument\nRizal Monument\n030808004\nPOINT (13387876.843 1652143.344)\n\n\n2830\n1430752967\n2723\nmonument\nRizal Monument\n034903092\nPOINT (13464327.852 1743609.163)\n\n\n2831\n2280492117\n2723\nmonument\nRizal Monument\n031421037\nPOINT (13467118.142 1705384.453)\n\n\n2832\n4898774223\n2723\nmonument\nRizal Monument\n035414009\nPOINT (13434387.128 1685457.796)\n\n\n2833\n3087522557\n2724\nmemorial\nRizal Monument\n031410015\nPOINT (13448861.946 1672777.772)\n\n\n\n\n2834 rows × 6 columns\n\n\n\n\n\nresults3 = dzs.create_distance_zonal_stats(\n    region3_admin_grids,\n    region34ncr_osm_pois,\n    max_distance=10_000,  # within 10km\n    aggregations=[dict(func=\"count\", output=\"pois_count\", fillna=[True])],\n)\n\nCPU times: user 64.3 ms, sys: 6.26 ms, total: 70.5 ms\nWall time: 67.8 ms\n\n\n\nlen(results3[results3.pois_count == 0.0])\n\n165\n\n\n\nresults3\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\npois_count\nnearest\n\n\n\n\n0\n0\n30\nPOLYGON ((13334497.956 1771012.807, 13339497.9...\n1.0\n8849.591855\n\n\n1\n0\n31\nPOLYGON ((13334497.956 1776012.807, 13339497.9...\n1.0\n8844.448848\n\n\n2\n0\n32\nPOLYGON ((13334497.956 1781012.807, 13339497.9...\n0.0\nNaN\n\n\n3\n1\n30\nPOLYGON ((13339497.956 1771012.807, 13344497.9...\n1.0\n3856.266007\n\n\n4\n1\n32\nPOLYGON ((13339497.956 1781012.807, 13344497.9...\n1.0\n6070.762498\n\n\n...\n...\n...\n...\n...\n...\n\n\n1069\n54\n44\nPOLYGON ((13604497.956 1841012.807, 13609497.9...\n1.0\n674.134053\n\n\n1070\n54\n45\nPOLYGON ((13604497.956 1846012.807, 13609497.9...\n2.0\n0.000000\n\n\n1071\n54\n46\nPOLYGON ((13604497.956 1851012.807, 13609497.9...\n1.0\n4237.158313\n\n\n1072\n54\n47\nPOLYGON ((13604497.956 1856012.807, 13609497.9...\n1.0\n9237.158313\n\n\n1073\n54\n48\nPOLYGON ((13604497.956 1861012.807, 13609497.9...\n0.0\nNaN\n\n\n\n\n1074 rows × 5 columns",
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+    "text": "Basic Usage\nGenerate area zonal stats for a GeoDataframe containing areas of interest with a vector data source containing areas associated with statistics.\n\n\n\n\n\n\nNote\n\n\n\nThe assumption for the data is that the statistics are uniformly distributed over the entire area for each row in the data source.\n\n\n\nimport geopandas as gpd\nimport matplotlib.pyplot as plt\nimport geowrangler.area_zonal_stats as azs\n\n\nSimple Grid AOIs and Data\n\nsimple_aoi = gpd.read_file(\"../data/simple_planar_aoi.geojson\")\nsimple_data = gpd.read_file(\"../data/simple_planar_data.geojson\")\n\nGiven an AOI (simple_aoi) and geodataframe containing sample data (simple_data)\n\nsimple_aoi\n\n\n\n\n\n\n\n\n\ngeometry\n\n\n\n\n0\nPOLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...\n\n\n1\nPOLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...\n\n\n2\nPOLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...\n\n\n\n\n\n\n\n\n\nsimple_data\n\n\n\n\n\n\n\n\n\npopulation\ninternet_speed\ngeometry\n\n\n\n\n0\n100\n20.0\nPOLYGON ((0.250 0.000, 0.250 1.000, 1.250 1.00...\n\n\n1\n200\n10.0\nPOLYGON ((1.250 0.000, 1.250 1.000, 2.250 1.00...\n\n\n2\n300\n5.0\nPOLYGON ((2.250 0.000, 2.250 1.000, 3.250 1.00...\n\n\n\n\n\n\n\n\nIn order correctly apportion the statistic, we need to make sure that the AOI and data geodataframes are using a planar CRS (i.e., gdf.crs.is_geographic == False)\n\nsimple_aoi.crs\n\n<Projected CRS: EPSG:3857>\nName: WGS 84 / Pseudo-Mercator\nAxis Info [cartesian]:\n- X[east]: Easting (metre)\n- Y[north]: Northing (metre)\nArea of Use:\n- name: World between 85.06°S and 85.06°N.\n- bounds: (-180.0, -85.06, 180.0, 85.06)\nCoordinate Operation:\n- name: Popular Visualisation Pseudo-Mercator\n- method: Popular Visualisation Pseudo Mercator\nDatum: World Geodetic System 1984 ensemble\n- Ellipsoid: WGS 84\n- Prime Meridian: Greenwich\n\n\n\nsimple_data.crs\n\n<Projected CRS: EPSG:3857>\nName: WGS 84 / Pseudo-Mercator\nAxis Info [cartesian]:\n- X[east]: Easting (metre)\n- Y[north]: Northing (metre)\nArea of Use:\n- name: World between 85.06°S and 85.06°N.\n- bounds: (-180.0, -85.06, 180.0, 85.06)\nCoordinate Operation:\n- name: Popular Visualisation Pseudo-Mercator\n- method: Popular Visualisation Pseudo Mercator\nDatum: World Geodetic System 1984 ensemble\n- Ellipsoid: WGS 84\n- Prime Meridian: Greenwich\n\n\n\nax = plt.axes()\nax = simple_data.plot(\n    ax=ax, color=[\"orange\", \"brown\", \"purple\"], edgecolor=\"yellow\", alpha=0.4\n)\nax = simple_aoi.plot(ax=ax, facecolor=\"none\", edgecolor=[\"r\", \"g\", \"b\"])\n\n\n\n\n\n\n\n\nThe red, green, and blue outlines are the 3 regions of interest (AOI) while the orange, brown, and purple areas are the data areas.\n\nempty_aoi_results = azs.create_area_zonal_stats(simple_aoi, simple_data)\n\nIf no aggregations are specified, the include_intersect=True arg specifies that the sum of the data areas intersecting our AOI is computed in the column intersect_area_sum.\n\nempty_aoi_results\n\n\n\n\n\n\n\n\n\ngeometry\nintersect_area_sum\n\n\n\n\n0\nPOLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...\n0.75\n\n\n1\nPOLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...\n1.00\n\n\n2\nPOLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...\n1.00\n\n\n\n\n\n\n\n\n\nApportioning aggregated statistics\nTo aggregate statistics over the AOI areas intersecting with the data areas, the default behavior of “apportioning” the statistic over the intersection of the data area overlapping the AOI area depends on the statistic.\n\nFor sum it apportions the total value of the statistic over the proportion of the data area overlapping the AOI area divided by the total area of the data.\nFor mean it apportions the total value of the statistic over the proportion of the data area overlapping the AOI area divided by the total area of the aoi.\nFor other statistics, there is no apportioning done and uses the raw statistics from the data areas overlapping the AOI area.\n\n\n\n\n\n\n\nNote\n\n\n\nThis default behavior can be overridden by specifying a prefix (e.g., raw_, data_, and aoi_) to the statistic (i.e., func) – e.g. if you wish the apportion the sum as a proportion of the aoi area instead of the data area, specify the func as aoi_sum instead of sum.\n\n\n\nsimple_aoi_results = azs.create_area_zonal_stats(\n    simple_aoi,\n    simple_data,\n    [\n        dict(func=[\"sum\", \"count\"], column=\"population\"),\n        dict(func=[\"mean\", \"max\", \"min\", \"std\"], column=\"internet_speed\"),\n    ],\n)\n\nCPU times: user 53.2 ms, sys: 728 µs, total: 53.9 ms\nWall time: 51.6 ms\n\n\n\nsimple_aoi_results\n\n\n\n\n\n\n\n\n\ngeometry\nintersect_area_sum\npopulation_sum\npopulation_count\ninternet_speed_mean\ninternet_speed_max\ninternet_speed_min\ninternet_speed_std\n\n\n\n\n0\nPOLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...\n0.75\n75.0\n1\n15.000\n20.0\n0.0\nNaN\n\n\n1\nPOLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...\n1.00\n175.0\n2\n6.250\n20.0\n10.0\n7.071068\n\n\n2\nPOLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...\n1.00\n275.0\n2\n3.125\n10.0\n5.0\n3.535534\n\n\n\n\n\n\n\n\n\nsimple_aoi_results.population_sum.sum(axis=None)\n\n525.0\n\n\nNote the value of the mean of the internet speed for the 1st area above.\nWhile there is one data area overlapping with an internet speed of 20.0 the computed aggregate statistic for the mean is 15.0 due to the apportioning which only accounts for 0.75 (the total AOI area is 1.0) – meaning it assigns a value of 0.0 for the 0.25 area with no overlapping data area.\nIf you wish to override this behavior and impute the value of the statistic as the means of all the overlapping areas (in proportion to the overlap of their area over the total area of the AOI), you can additionally add a prefix of imputed_ as shown in the next example. Note that adding a prefix doesn’t change the default output column name, which is why we explicitly specified an output column name (internet_speed_imputed_mean in order to differentiate it from the original internet_speed_mean column.\nNotice as well that adding a raw_ prefix to the min,max,std statistics doesn’t change their values since by default these statistics don’t use any apportioning (so specifying raw_ is redundant in this case).\nLastly, check the effect of setting of the fix_min arg to False (the default value is True) – since it uses the raw column to compute the min, it is not aware that there are areas in the AOI that have no overlapping data areas and uses the min value from the overlapping areas (in this case, since there is only 1 partially overlapping area, it uses that as the min value). The fix_min arg “fixes” this by checking if the data areas completely overlap the AOI area and sets the minimum to 0 if there is a portion of the AOI area that is not completely intersected by a data area.\n\ncorrected_aoi_results = azs.create_area_zonal_stats(\n    simple_aoi,\n    simple_data,\n    [\n        dict(func=[\"sum\", \"count\"], column=\"population\"),\n        dict(\n            func=[\"mean\", \"imputed_mean\", \"raw_max\", \"raw_min\", \"raw_std\"],\n            column=\"internet_speed\",\n            output=[\n                \"internet_speed_mean\",\n                \"internet_speed_imputed_mean\",\n                \"internet_speed_max\",\n                \"internet_speed_min\",\n                \"internet_speed_std\",\n            ],\n        ),\n    ],\n    fix_min=False,\n)\n\nCPU times: user 103 ms, sys: 0 ns, total: 103 ms\nWall time: 101 ms\n\n\n\ncorrected_aoi_results\n\n\n\n\n\n\n\n\n\ngeometry\nintersect_area_sum\npopulation_sum\npopulation_count\ninternet_speed_mean\ninternet_speed_imputed_mean\ninternet_speed_max\ninternet_speed_min\ninternet_speed_std\n\n\n\n\n0\nPOLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...\n0.75\n75.0\n1\n15.000\n20.000\n20.0\n20.0\nNaN\n\n\n1\nPOLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...\n1.00\n175.0\n2\n6.250\n6.250\n20.0\n10.0\n7.071068\n\n\n2\nPOLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...\n1.00\n275.0\n2\n3.125\n3.125\n10.0\n5.0\n3.535534\n\n\n\n\n\n\n\n\n\n\n\nCustom Grids over admin area data\n\nAnother example using aggregated statistics over admin areas (barangay level) that are apportioned into grid areas by their overlap.\n\n\nNotice that since the grid areas does not completely overlap the admin areas (and vice versa) , the sum of their total populations and land areas are not equal.\n\n\nregion3_admin_grids = gpd.read_file(\"../data/region3_admin_grids.geojson\")\nregion3_admin_grids = region3_admin_grids.to_crs(\"EPSG:3857\")  # convert to planar\n\nCPU times: user 159 ms, sys: 0 ns, total: 159 ms\nWall time: 157 ms\n\n\n\nregion3_pop_bgy_level = gpd.read_file(\"../data/region3_population_bgy_level.geojson\")\n\nCPU times: user 376 ms, sys: 29.2 ms, total: 405 ms\nWall time: 402 ms\n\n\n\naoi_result = azs.create_area_zonal_stats(\n    region3_admin_grids,\n    region3_pop_bgy_level,\n    [\n        dict(func=[\"sum\", \"count\"], column=\"population\"),\n    ],\n)\n\nCPU times: user 764 ms, sys: 0 ns, total: 764 ms\nWall time: 763 ms\n\n\n\naoi_result\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\nintersect_area_sum\npopulation_sum\npopulation_count\n\n\n\n\n0\n0\n30\nPOLYGON ((13334497.956 1771012.807, 13339497.9...\n1.213773e+06\n687.832840\n1\n\n\n1\n0\n31\nPOLYGON ((13334497.956 1776012.807, 13339497.9...\n2.471924e+06\n986.890853\n1\n\n\n2\n0\n32\nPOLYGON ((13334497.956 1781012.807, 13339497.9...\n2.748813e+06\n1097.435840\n1\n\n\n3\n1\n30\nPOLYGON ((13339497.956 1771012.807, 13344497.9...\n1.081669e+06\n468.368614\n2\n\n\n4\n1\n32\nPOLYGON ((13339497.956 1781012.807, 13344497.9...\n8.941593e+04\n8.570604\n2\n\n\n...\n...\n...\n...\n...\n...\n...\n\n\n1069\n54\n44\nPOLYGON ((13604497.956 1841012.807, 13609497.9...\n1.976718e+05\n15.398162\n1\n\n\n1070\n54\n45\nPOLYGON ((13604497.956 1846012.807, 13609497.9...\n1.019141e+07\n1613.913393\n3\n\n\n1071\n54\n46\nPOLYGON ((13604497.956 1851012.807, 13609497.9...\n3.129991e+06\n1033.997979\n2\n\n\n1072\n54\n47\nPOLYGON ((13604497.956 1856012.807, 13609497.9...\n8.106461e+06\n250.893691\n2\n\n\n1073\n54\n48\nPOLYGON ((13604497.956 1861012.807, 13609497.9...\n1.928818e+07\n299.377987\n3\n\n\n\n\n1074 rows × 6 columns\n\n\n\n\n\n(\n    aoi_result.geometry.area.sum(),\n    aoi_result.intersect_area_sum.sum(),\n    aoi_result.population_sum.sum(),\n)\n\n(26850000000.000072, 24548892214.947075, 12136963.9717533)\n\n\n\n(region3_pop_bgy_level.geometry.area.sum(), region3_pop_bgy_level.population.sum())\n\n(27658006631.682587, 13139695)\n\n\n\ngdf = region3_admin_grids\ngdf2 = region3_pop_bgy_level\n\n\nax = plt.axes()\nax = gdf2.plot(ax=ax, column=\"population\", alpha=0.4)\nax = gdf.plot(ax=ax, facecolor=\"none\", edgecolor=\"blue\", alpha=0.4)\n\n\n\n\n\n\n\n\n\nax = plt.axes()\nax = gdf2.plot(ax=ax, facecolor=\"none\", alpha=0.4, edgecolor=\"red\")\nax = aoi_result.plot(column=\"population_sum\", ax=ax, alpha=0.4, edgecolor=\"blue\")\n\n\n\n\n\n\n\n\n\nax = plt.axes()\nax = gdf2.plot(ax=ax, column=\"population\", alpha=0.4, edgecolor=\"red\")\nax = aoi_result.plot(column=\"intersect_area_sum\", ax=ax, alpha=0.4, edgecolor=\"blue\")",
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+    "text": "Basic Usage\nGenerate zonal stats for a GeoDataframe containing areas of interest using raster data\nTerms:\n\naoi - (area of interest) a geodataframe which we are interested in generating zonal statistics for\nraster data - the source raster containing the features which we are interested in collecting zonal stats for our aoi.\n\n\nimport geopandas as gpd\nimport matplotlib.pyplot as plt\nimport geowrangler.raster_zonal_stats as rzs\n\n\nLoad AOI\nOur areas of interest (AOI) are three (3) Level 1 administration regions (ADM1) of the Philippines located in the island of Luzon.\n\n# area multipolygons for regions 3,4,ncr of the philippines\naoi = gpd.read_file(\"../data/region34ncr_admin.geojson\")\n\nCPU times: user 3.26 s, sys: 195 ms, total: 3.45 s\nWall time: 3.44 s\n\n\n\nax = aoi.plot(\n    ax=plt.axes(),\n    facecolor=\"none\",\n    edgecolor=[\n        \"#C62828\",\n        \"#283593\",\n        \"#FF9800\",\n    ],\n)\n\n\n\n\n\n\n\n\n\naoi\n\n\n\n\n\n\n\n\n\nReg_Code\nReg_Name\nReg_Alt_Name\ngeometry\n\n\n\n\n0\n130000000\nNational Capital Region\nNCR\nMULTIPOLYGON (((121.03842 14.78525, 121.03815 ...\n\n\n1\n030000000\nRegion III\nCentral Luzon\nMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...\n\n\n2\n040000000\nRegion IV-A\nCalabarzon\nMULTIPOLYGON (((122.72165 13.36485, 122.72143 ...\n\n\n\n\n\n\n\n\n\n\nDownload Philippine Population Data\n\nsee the Humanitarian Data Exchange World Population Counts - Philippines\n\nWe download our raster Data as GeoTiff files from the Humanitarian Data Exchange site.\n\n\n\n\n\n\nNote\n\n\n\nThis maybe slow as the file is about 180 Mb and depending on your internet download speed may take more than 5 minutes.\n\n\n\n# PHL population HDX links\nphil_pop_link = \"https://data.worldpop.org/GIS/Population/Global_2000_2020/2020/PHL/phl_ppp_2020.tif\"\nphil_pop_dset = \"phl_pop_2020.tif\"\n\n\n![ ! -e ../data/{phil_pop_dset} ] && curl -o ../data/{phil_pop_dset} {phil_pop_link}\n\n  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n                                 Dload  Upload   Total   Spent    Left  Speed\n100  180M  100  180M    0     0   576k      0  0:05:19  0:05:19 --:--:--  430k\nCPU times: user 12 s, sys: 3.57 s, total: 15.6 s\nWall time: 5min 19s\n\n\nTo create our raster zonal stats, we just need to set the aggregations, as well as some extra arguments, such as the nodata value in the raster.\n\nresults = rzs.create_raster_zonal_stats(\n    aoi,\n    f\"../data/{phil_pop_dset}\",\n    aggregation=dict(\n        func=[\"sum\", \"count\"],\n        column=\"population\",\n        output=[\"population_count\", \"samples\"],\n    ),\n    extra_args=dict(nodata=-99999),  # nodata value is -99999\n)\n\nCPU times: user 4.22 s, sys: 128 ms, total: 4.35 s\nWall time: 4.34 s\n\n\n\nresults\n\n\n\n\n\n\n\n\n\nReg_Code\nReg_Name\nReg_Alt_Name\ngeometry\nsamples\npopulation_count\n\n\n\n\n0\n130000000\nNational Capital Region\nNCR\nMULTIPOLYGON (((121.03842 14.78525, 121.03815 ...\n70786\n13165866.0\n\n\n1\n030000000\nRegion III\nCentral Luzon\nMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...\n2558377\n11493727.0\n\n\n2\n040000000\nRegion IV-A\nCalabarzon\nMULTIPOLYGON (((122.72165 13.36485, 122.72143 ...\n1876244\n15952383.0\n\n\n\n\n\n\n\n\n\n\nUsing Grid Tile AOIs\nWe can also use tile grids as our AOIs.\n\n# note that you don't need to load the aoi first\ngrid_aoi_file = \"../data/region3_admin_grids.geojson\"\n\n\ngrid_aoi_results = rzs.create_raster_zonal_stats(\n    grid_aoi_file,\n    f\"../data/{phil_pop_dset}\",\n    aggregation=dict(\n        func=[\"sum\", \"count\"],\n        column=\"population\",\n        output=[\"population_count\", \"samples\"],\n        fillna=[True, True],\n    ),\n    extra_args=dict(nodata=-99999),  # nodata value is -99999\n)\n\nCPU times: user 8.57 s, sys: 12.2 ms, total: 8.59 s\nWall time: 8.58 s\n\n\n\ngrid_aoi_results.head()\n\n\n\n\n\n\n\n\n\nx\ny\ngeometry\nsamples\npopulation_count\n\n\n\n\n0\n0\n30\nPOLYGON ((119.78583 15.70870, 119.83075 15.708...\n171\n1171.764038\n\n\n1\n0\n31\nPOLYGON ((119.78583 15.75193, 119.83075 15.751...\n329\n278.567200\n\n\n2\n0\n32\nPOLYGON ((119.78583 15.79516, 119.83075 15.795...\n345\n279.140198\n\n\n3\n1\n30\nPOLYGON ((119.83075 15.70870, 119.87566 15.708...\n158\n808.681152\n\n\n4\n1\n32\nPOLYGON ((119.83075 15.79516, 119.87566 15.795...\n20\n0.000000\n\n\n\n\n\n\n\n\n\nax = aoi[aoi.Reg_Name == \"Region III\"].plot(\n    ax=plt.axes(), facecolor=\"none\", edgecolor=\"black\"\n)\nax = grid_aoi_results.plot(\n    ax=ax, column=\"population_count\", edgecolor=\"blue\", alpha=0.5\n)\n\n\n\n\n\n\n\n\n\n\nUsing Bing Tile Grid Tile AOIs\nWe can also use pre-existing Bing tile grids as our AOIs.\n\n# note that you don't need to load the aoi first\nbingtile_grid_aoi_file = \"../data/region3_bingtile_grid13.geojson\"\n\n\nbingtile_grid_aoi_results = rzs.create_raster_zonal_stats(\n    bingtile_grid_aoi_file,\n    f\"../data/{phil_pop_dset}\",\n    aggregation=dict(\n        func=[\"sum\", \"count\"],\n        column=\"population\",\n        output=[\"population_count\", \"samples\"],\n        fillna=[True, True],\n    ),\n    extra_args=dict(nodata=-99999),  # nodata value is -99999\n)\n\nCPU times: user 10.6 s, sys: 124 ms, total: 10.7 s\nWall time: 10.7 s\n\n\n\nbingtile_grid_aoi_results.head()\n\n\n\n\n\n\n\n\n\nquadkey\ngeometry\nsamples\npopulation_count\n\n\n\n\n0\n1323030303301\nPOLYGON ((120.10254 14.73239, 120.10254 14.774...\n737\n196.756744\n\n\n1\n1323030303300\nPOLYGON ((120.05859 14.73239, 120.05859 14.774...\n50\n35.732861\n\n\n2\n1323030303311\nPOLYGON ((120.19043 14.73239, 120.19043 14.774...\n248\n383.338013\n\n\n3\n1323030303133\nPOLYGON ((120.19043 14.77488, 120.19043 14.817...\n901\n5621.879395\n\n\n4\n1323030303131\nPOLYGON ((120.19043 14.81737, 120.19043 14.859...\n1328\n6584.988770\n\n\n\n\n\n\n\n\n\nax = aoi[aoi.Reg_Name == \"Region III\"].plot(\n    ax=plt.axes(), facecolor=\"none\", edgecolor=\"black\"\n)\nax = bingtile_grid_aoi_results.plot(\n    ax=ax, column=\"population_count\", edgecolor=\"blue\", alpha=0.5\n)",
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+    "text": "Basic Usage\nGenerate a raster mask from a reference TIF image based on the labels assigned to certain regions of the area. The generated raster mask can also be used to convert the raster to a dataframe with respect to their assigned labels.\nTerms: * raster mask - processed image wherein some parts of it are set to no value or in other words hidden * labels - parts of an area within the satellite image are categorized as such. Examples are mining, agriculture, etc.\n\nimport geopandas as gpd\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nimport geowrangler.raster_to_dataframe as rdf\n\n\nfrom rasterio import features\nfrom rasterio.plot import show\nfrom rasterio.windows import Window, transform\n\n\nLoad reference raster image and shape file\nThe shape file that should be loaded should contain a column that consists of labels.\n\ntiff_file = \"../data/vector_to_raster_mask_sample/cabanglasan.tif\"\nshape_file = \"../data/vector_to_raster_mask_sample/labels_20220816.gpkg\"\ntarget_file = shape_file.replace(\"gpkg\", \"tiff\")\n\nWe create a dictionary of our target labels that we want to create a raster mask for.\n\nlabels = {\n    \"mining\": 1,\n    \"neg\": 2,\n    \"agriculture\": 3,\n    \"product_extraction\": 4,\n    \"kaingin\": 5,\n    \"biophysical\": 6,\n}\n\n\n\nGenerate raster mask\nTo create the mask we input the reference raster image, shape file with labels, and the dictionary with our target labels into the function.\n\n# Generate masks for a file\nmasks, grids, values = rdf.generate_mask(\n    tiff_file=tiff_file,\n    shape_file=shape_file,\n    output_file=target_file,\n    labels_column=\"label\",\n    labels_dict=labels,\n    plot=True,\n)\n\n/home/abbymoreno/geowrangler/geowrangler/raster_to_dataframe.py:29: FutureWarning: Currently, index_parts defaults to True, but in the future, it will default to False to be consistent with Pandas. Use `index_parts=True` to keep the current behavior and True/False to silence the warning.\n  gs = gdf.explode()\n\n\n\n\n\n\n\n\n\n\n\nLoad rasters and the outputed raster mask\n\ntiff_files = [\n    \"../data/vector_to_raster_mask_sample/cabanglasan.tif\",\n]\nmask_file = \"../data/vector_to_raster_mask_sample/labels_20220816.tiff\"\n\n\n\nConvert raster to dataframe\nTo convert raster/s to a dataframe, we just need to input the images into the function and add the mask file. The mask file will be used to create the last column label in the dataframe. The result is a tabular dataset that contains the Band values per raster image and their labels.\n\ndata = rdf.read_bands(tiff_files, mask_file)\n\n\ndata\n\n\n\n\n\n\n\n\n\nB1_0\nB2_0\nB3_0\nB4_0\nB5_0\nB6_0\nB7_0\nB8_0\nB9_0\nB10_0\nB11_0\nB12_0\nlabel\n\n\n\n\n0\n0.1198\n0.09635\n0.09330\n0.0698\n0.10665\n0.20250\n0.2490\n0.23525\n0.28125\n0.0377\n0.19925\n0.1002\n0\n\n\n1\n0.1198\n0.09580\n0.09245\n0.0708\n0.10665\n0.20250\n0.2490\n0.23925\n0.28125\n0.0377\n0.19925\n0.1002\n0\n\n\n2\n0.1148\n0.09420\n0.09460\n0.0707\n0.10380\n0.20395\n0.2478\n0.23150\n0.27165\n0.0385\n0.18240\n0.0902\n0\n\n\n3\n0.1148\n0.09190\n0.08850\n0.0631\n0.10380\n0.20395\n0.2478\n0.23300\n0.27165\n0.0385\n0.18240\n0.0902\n0\n\n\n4\n0.1148\n0.09350\n0.09080\n0.0643\n0.10565\n0.20830\n0.2466\n0.24205\n0.26990\n0.0385\n0.18050\n0.0894\n0\n\n\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n...\n\n\n775824\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.0000\n0\n\n\n775825\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.0000\n0\n\n\n775826\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.0000\n0\n\n\n775827\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.0000\n0\n\n\n775828\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.00000\n0.0000\n0.00000\n0.0000\n0\n\n\n\n\n775829 rows × 13 columns\n\n\n\n\nChecking the label column we can see that the TIF image that we converted does not contain the desired labels.\n\ndata[\"label\"].unique()\n\narray([0], dtype=uint16)",
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+    "text": "Overview\nGeowrangler is a python package for geodata wrangling. It helps you build data transformation workflows that have no out-of-the-box solutions from other geospatial libraries.\nWe surveyed our past geospatial projects to extract these solutions for our work and hope that these will be useful for others as well.\nOur audience are researchers, analysts and engineers delivering geospatial projects.\nWe welcome your comments, suggestions, bug reports and code contributions to make Geowrangler better.\n\n\nContext\nGeowrangler was borne out of our efforts to reduce the amount of boilerplate code in wrangling geospatial data. It builds on top of existing geospatial libraries such as geopandas, rasterio, rasterstats, morecantile and others. Our goals are centered on the following tasks:\n\nExtracting area of interest zonal statistics from vector and raster data\nGridding areas of interest\nValidating geospatial datasets\nDownloading of publically available geospatial datasets (e.g. OSM, Ookla, Nightlights)\nOther geospatial vector and raster data processing tasks\n\nTo make it easy to document, maintain and extend the package, we opted to maintain the source code, tests and documentation on Jupyter notebooks. We use nbdev to generate the python package and documentation from the notebooks. See this document to learn more about our development workflow.\nBy doing this, we hope to make it easy for geospatial analysts, scientists and engineers to learn, explore and extend this package for their geospatial processing needs.\nAside from providing reference documentation for each module, we have included extensive tutorials and use case examples in order to make it easy to learn and use.\n\n\nModules\n\nGrid Tile Generation\nGeometry Validation\nVector Zonal Stats\nRaster Zonal Stats\nArea Zonal Stats\nDistance Zonal Stats\nDemographic and Health Survey (DHS) Processing Utils\nGeofabrik (OSM) Data Download\nOokla Data Download\n\nCheck this page for more details about our Roadmap.\n\n\nInstallation\npip install geowrangler\n\n\nExploring the Documentation\nWe develop the package modules alongside their documentation. Each page comes with an Open in Colab button that will open the jupyter notebook in Colab for exploration (including this page).\nClick on the Open in Colab button below to open this page as a Google Colab notebook.\n\n\n# view the source of a grid component\ngdf = gpd.GeoDataFrame()\ngrid = geowrangler.grids.SquareGridGenerator(gdf, 1)\ngrid??\n\n\nType:        SquareGridGenerator\nString form: <geowrangler.grids.SquareGridGenerator object at 0x7fc284380880>\nFile:        ~/work/unicef-ai4d/geowrangler-1/geowrangler/grids.py\nSource:     \nclass SquareGridGenerator:\n    def __init__(\n        self,\n        cell_size: float,  # height and width of a square cell in meters\n        grid_projection: str = \"EPSG:3857\",  # projection of grid output\n        boundary: Union[SquareGridBoundary, List[float]] = None,  # original boundary\n    ):\n        self.cell_size = cell_size\n        self.grid_projection = grid_projection\n        self.boundary = boundary\n\n\n\n\n\nTutorials\n\nGrids Generation\nGeometry Validation\nVector Zonal Stats\nRaster Zonal Stats\nArea Zonal Stats\nDistance Zonal Stats\nDHS Processing Utils\nDataset Downloads\n\n\n\nReference\n\nGrids Generation\nGeometry Validation\nVector Zonal Stats\nRaster Zonal Stats\nArea Zonal Stats\nDistance Zonal Stats\nDHS Processing Utils\nDataset Geofabrik (OSM)\nDataset Ookla\n\n\n\n\n\n\n\nNote\n\n\n\nAll the documentation pages (including the references) are executable Jupyter notebooks.",
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+    "text": "Basic Usage\nLoading a geojson with invalid geometries\n\nimport geopandas as gpd\nimport pandas as pd\n\n\ngdf = gpd.read_file(\"../data/broken.geojson\")\ngdf = pd.concat([gdf, gpd.GeoDataFrame({\"geometry\": [None], \"id\": \"null geometry\"})])\ngdf\n\n/home/jt/.cache/pypoetry/virtualenvs/geowrangler-U9oiUrW5-py3.9/lib/python3.9/site-packages/geopandas/_compat.py:111: UserWarning: The Shapely GEOS version (3.10.2-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.\n  warnings.warn(\n\n\n\n\n\n\n\n\n\n\nid\ngeometry\n\n\n\n\n0\nvalid\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 0....\n\n\n1\nout_of_crs_bounds\nPOLYGON ((200.00000 0.00000, 0.00000 1.00000, ...\n\n\n2\nmisoriented\nPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....\n\n\n3\nself_intersecting\nPOLYGON ((0.00000 0.00000, 1.00000 1.00000, 2....\n\n\n4\ncounterclockwise\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\n\n\n5\nsliver\nPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 0....\n\n\n6\nholes\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\n\n\n7\nnon-closed polygon\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\n\n\n8\nmultipolygon\nMULTIPOLYGON (((0.00000 0.00000, 1.00000 0.000...\n\n\n9\npolygon z\nPOLYGON Z ((0.00000 0.00000 1.00000, 1.00000 0...\n\n\n10\ncomplex self-intersecting polygon\nPOLYGON ((0.00000 0.00000, 5.00000 0.00000, 5....\n\n\n0\nnull geometry\nNone\n\n\n\n\n\n\n\n\nWe then run Geometry Validation. By default, these append a new column if the validation fails, applies a fix if possible, and raises a warning if no fix is available.\n\nfrom geowrangler.validation import GeometryValidation\n\n\nGeometryValidation(gdf)\n\nvalidated_gdf = GeometryValidation(gdf).validate_all()\nvalidated_gdf\n\n/home/jt/repos/geowrangler/geowrangler/validation.py:107: UserWarning: Found null geometries\n  warnings.warn(self.warning_message)\n/home/jt/repos/geowrangler/geowrangler/validation.py:107: UserWarning: Found geometries out of bounds from crs\n  warnings.warn(self.warning_message)\n/home/jt/repos/geowrangler/geowrangler/validation.py:107: UserWarning: Found geometries with area equals or less than zero\n  warnings.warn(self.warning_message)\n\n\n\n\n\n\n\n\n\n\nid\ngeometry\nis_not_null\nis_not_self_intersecting\nis_oriented_properly\nis_within_crs_bounds\narea_is_not_zero\n\n\n\n\n0\nvalid\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 0....\nTrue\nTrue\nTrue\nTrue\nTrue\n\n\n1\nout_of_crs_bounds\nPOLYGON ((200.00000 0.00000, 0.00000 1.00000, ...\nTrue\nTrue\nTrue\nFalse\nTrue\n\n\n2\nmisoriented\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\nTrue\nTrue\nFalse\nTrue\nTrue\n\n\n3\nself_intersecting\nMULTIPOLYGON (((0.00000 2.00000, 0.00000 0.000...\nTrue\nFalse\nFalse\nTrue\nTrue\n\n\n4\ncounterclockwise\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\nTrue\nTrue\nTrue\nTrue\nTrue\n\n\n5\nsliver\nPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 0....\nTrue\nTrue\nTrue\nTrue\nFalse\n\n\n6\nholes\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\nTrue\nTrue\nTrue\nTrue\nTrue\n\n\n7\nnon-closed polygon\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\nTrue\nTrue\nTrue\nTrue\nTrue\n\n\n8\nmultipolygon\nMULTIPOLYGON (((0.00000 0.00000, 1.00000 0.000...\nTrue\nTrue\nTrue\nTrue\nTrue\n\n\n9\npolygon z\nPOLYGON Z ((0.00000 0.00000 1.00000, 1.00000 0...\nTrue\nTrue\nTrue\nTrue\nTrue\n\n\n10\ncomplex self-intersecting polygon\nPOLYGON ((0.00000 5.00000, 0.00000 2.50000, 0....\nTrue\nFalse\nFalse\nTrue\nTrue\n\n\n0\nnull geometry\nNone\nFalse\nTrue\nTrue\nTrue\nTrue\n\n\n\n\n\n\n\n\n\ngdf.iloc[5].geometry.area\n\n0.0\n\n\nRunning the validation again shows that validation applies some fixes\n\nGeometryValidation(validated_gdf[[\"id\", \"geometry\"]]).validate_all()\n\n/home/jt/repos/geowrangler/geowrangler/validation.py:107: UserWarning: Found null geometries\n  warnings.warn(self.warning_message)\n/home/jt/repos/geowrangler/geowrangler/validation.py:107: UserWarning: Found geometries out of bounds from crs\n  warnings.warn(self.warning_message)\n/home/jt/repos/geowrangler/geowrangler/validation.py:107: UserWarning: Found geometries with area equals or less than zero\n  warnings.warn(self.warning_message)\n\n\n\n\n\n\n\n\n\n\nid\ngeometry\nis_not_null\nis_not_self_intersecting\nis_oriented_properly\nis_within_crs_bounds\narea_is_not_zero\n\n\n\n\n0\nvalid\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 0....\nTrue\nTrue\nTrue\nTrue\nTrue\n\n\n1\nout_of_crs_bounds\nPOLYGON ((200.00000 0.00000, 0.00000 1.00000, ...\nTrue\nTrue\nTrue\nFalse\nTrue\n\n\n2\nmisoriented\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\nTrue\nTrue\nTrue\nTrue\nTrue\n\n\n3\nself_intersecting\nMULTIPOLYGON (((0.00000 2.00000, 0.00000 0.000...\nTrue\nTrue\nTrue\nTrue\nTrue\n\n\n4\ncounterclockwise\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\nTrue\nTrue\nTrue\nTrue\nTrue\n\n\n5\nsliver\nPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 0....\nTrue\nTrue\nTrue\nTrue\nFalse\n\n\n6\nholes\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\nTrue\nTrue\nTrue\nTrue\nTrue\n\n\n7\nnon-closed polygon\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\nTrue\nTrue\nTrue\nTrue\nTrue\n\n\n8\nmultipolygon\nMULTIPOLYGON (((0.00000 0.00000, 1.00000 0.000...\nTrue\nTrue\nTrue\nTrue\nTrue\n\n\n9\npolygon z\nPOLYGON Z ((0.00000 0.00000 1.00000, 1.00000 0...\nTrue\nTrue\nTrue\nTrue\nTrue\n\n\n10\ncomplex self-intersecting polygon\nPOLYGON ((0.00000 5.00000, 0.00000 2.50000, 0....\nTrue\nTrue\nTrue\nTrue\nTrue\n\n\n0\nnull geometry\nNone\nFalse\nTrue\nTrue\nTrue\nTrue",
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+    "title": "Geometry Validation Tutorial",
+    "section": "Passing Validators",
+    "text": "Passing Validators\nYou can pass a list of Validators to selective run validators, the default uses the following - NullValidator - Checks if geometry is null. No fix - OrientationValidator - Check the orientation of the outer most ring of each polygon is counter clockwise. Converts it to counter-clockwise if as the fix - SelfIntersectingValidator - Checks if the polygons is self intersecting. Runs shapely.validation.make_valid as the fix. - CrsBoundsValidator - Checks if bounds of each geometry are within the CRS. No fix - AreaValidator - Checks if polygons or multipolygon have an area greater than zero\n\nfrom geowrangler.validation import NullValidator, SelfIntersectingValidator\n\n\nvalidated_gdf = GeometryValidation(\n    gdf, validators=[NullValidator, SelfIntersectingValidator]\n).validate_all()\nvalidated_gdf\n\n/home/jt/repos/geowrangler/geowrangler/validation.py:107: UserWarning: Found null geometries\n  warnings.warn(self.warning_message)\n\n\n\n\n\n\n\n\n\n\nid\ngeometry\nis_not_null\nis_not_self_intersecting\n\n\n\n\n0\nvalid\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 0....\nTrue\nTrue\n\n\n1\nout_of_crs_bounds\nPOLYGON ((200.00000 0.00000, 0.00000 1.00000, ...\nTrue\nTrue\n\n\n2\nmisoriented\nPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....\nTrue\nTrue\n\n\n3\nself_intersecting\nMULTIPOLYGON (((0.00000 2.00000, 1.00000 1.000...\nTrue\nFalse\n\n\n4\ncounterclockwise\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\nTrue\nTrue\n\n\n5\nsliver\nPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 0....\nTrue\nTrue\n\n\n6\nholes\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\nTrue\nTrue\n\n\n7\nnon-closed polygon\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\nTrue\nTrue\n\n\n8\nmultipolygon\nMULTIPOLYGON (((0.00000 0.00000, 1.00000 0.000...\nTrue\nTrue\n\n\n9\npolygon z\nPOLYGON Z ((0.00000 0.00000 1.00000, 1.00000 0...\nTrue\nTrue\n\n\n10\ncomplex self-intersecting polygon\nPOLYGON ((0.00000 5.00000, 5.00000 5.00000, 5....\nTrue\nFalse\n\n\n0\nnull geometry\nNone\nFalse\nTrue\n\n\n\n\n\n\n\n\nYou can also use a single validator at a time\n\nSelfIntersectingValidator().validate(gdf)\n\n\n\n\n\n\n\n\n\nid\ngeometry\nis_not_self_intersecting\n\n\n\n\n0\nvalid\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 0....\nTrue\n\n\n1\nout_of_crs_bounds\nPOLYGON ((200.00000 0.00000, 0.00000 1.00000, ...\nTrue\n\n\n2\nmisoriented\nPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....\nTrue\n\n\n3\nself_intersecting\nMULTIPOLYGON (((0.00000 2.00000, 1.00000 1.000...\nFalse\n\n\n4\ncounterclockwise\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\nTrue\n\n\n5\nsliver\nPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 0....\nTrue\n\n\n6\nholes\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\nTrue\n\n\n7\nnon-closed polygon\nPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....\nTrue\n\n\n8\nmultipolygon\nMULTIPOLYGON (((0.00000 0.00000, 1.00000 0.000...\nTrue\n\n\n9\npolygon z\nPOLYGON Z ((0.00000 0.00000 1.00000, 1.00000 0...\nTrue\n\n\n10\ncomplex self-intersecting polygon\nPOLYGON ((0.00000 5.00000, 5.00000 5.00000, 5....\nFalse\n\n\n0\nnull geometry\nNone\nTrue",
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+    "title": "Geometry Validation Tutorial",
+    "section": "Building your own validator",
+    "text": "Building your own validator\nLet’s build a validator that check if the is point below 0 in the x axis, if that is the case we set it to 0\n\nfrom shapely.geometry.point import Point\nfrom shapely.geometry.polygon import Polygon\n\nfrom geowrangler.validation import BaseValidator\n\n\nclass PointValidator(BaseValidator):\n    validator_column_name = \"is_not_point\"\n    geometry_types = [\"Point\"]  # What kind of geometies to validate and fix\n\n    def check(self, geometry):\n        # Checks if the geometry is valid. If False, applies the fix\n        return geometry.x > 0\n\n    def fix(self, geometry):\n        return Point(0, geometry.y)\n\n\ngdf = gpd.GeoDataFrame(\n    geometry=[Point(-0.1, 0), Polygon([(-0.1, 0.1), (-0.1, 1), (1, 1)])]\n)\nvalidated_gdf = PointValidator().validate(gdf)\n\nax = gdf.plot()\nax = validated_gdf.plot()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are several cases where no fix is available or you want to fix them manualy, we can create a validator that warns the users.\n\nfrom shapely.geometry.point import Point\nfrom shapely.geometry.polygon import Polygon\n\nfrom geowrangler.validation import BaseValidator\n\n\nclass PointValidator(BaseValidator):\n    validator_column_name = \"is_not_point\"\n    fix_available = False  # Telling the validator that there is no available fixes\n    warning_message = \"Found geometries that are points below 0\"  # warning message\n    geometry_types = [\"Point\"]  # What kind of geometies to validate and fix\n\n    def check(self, geometry):\n        # Checks if the geometry is valid. If False, warn the user\n        return geometry.x > 0\n\n\ngdf = gpd.GeoDataFrame(geometry=[Point(-0.1, 0), Polygon([(0, 0.0), (0, 1), (1, 1)])])\nvalidated_gdf = PointValidator().validate(gdf)\nvalidated_gdf\n\n/home/jt/repos/geowrangler/geowrangler/validation.py:107: UserWarning: Found geometries that are points below 0\n  warnings.warn(self.warning_message)\n\n\n\n\n\n\n\n\n\n\ngeometry\nis_not_point\n\n\n\n\n0\nPOINT (-0.10000 0.00000)\nFalse\n\n\n1\nPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....\nTrue",
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+    "text": "source\n\nlist_geofabrik_regions\n\n list_geofabrik_regions ()\n\nGet list of regions from geofabrik index\n\nsource\n\n\nget_osm_download_url\n\n get_osm_download_url (region, year=None)\n\n\nsource\n\n\nget_download_filepath\n\n get_download_filepath (url, directory)\n\n\nsource\n\n\ndownload_geofabrik_region\n\n download_geofabrik_region (region:str, directory:str='data/',\n                            overwrite=False, year=None,\n                            show_progress=True, chunksize=8192)\n\nDownload geofabrik region to path\n\nsource\n\n\ndownload_osm_region_data\n\n download_osm_region_data (region, year=None,\n                           cache_dir='~/.cache/geowrangler',\n                           use_cache=True, chunksize=8192,\n                           show_progress=True)\n\n\nsource\n\n\nOsmDataManager\n\n OsmDataManager (cache_dir='~/.cache/geowrangler')\n\nAn instance of this class provides convenience functions for loading and caching OSM data\n\nsource\n\n\nOsmDataManager.load_pois\n\n OsmDataManager.load_pois (region, year=None, use_cache=True,\n                           chunksize=1048576, show_progress=True)\n\n\nsource\n\n\nOsmDataManager.load_roads\n\n OsmDataManager.load_roads (region, year=None, use_cache=True,\n                            chunksize=1048576, show_progress=True)",
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diff --git a/site_libs/bootstrap/bootstrap.min.css b/site_libs/bootstrap/bootstrap.min.css
new file mode 100644
index 0000000..93687d0
--- /dev/null
+++ b/site_libs/bootstrap/bootstrap.min.css
@@ -0,0 +1,12 @@
+/*!
+ * Bootstrap  v5.3.1 (https://getbootstrap.com/)
+ * Copyright 2011-2023 The Bootstrap Authors
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+ */@import"https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@300;400;700&display=swap";:root,[data-bs-theme=light]{--bs-blue: #2780e3;--bs-indigo: #6610f2;--bs-purple: #613d7c;--bs-pink: #e83e8c;--bs-red: #ff0039;--bs-orange: #f0ad4e;--bs-yellow: #ff7518;--bs-green: #3fb618;--bs-teal: #20c997;--bs-cyan: #9954bb;--bs-black: #000;--bs-white: #fff;--bs-gray: #6c757d;--bs-gray-dark: #343a40;--bs-gray-100: #f8f9fa;--bs-gray-200: #e9ecef;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #adb5bd;--bs-gray-600: #6c757d;--bs-gray-700: #495057;--bs-gray-800: #343a40;--bs-gray-900: #212529;--bs-default: #343a40;--bs-primary: #2780e3;--bs-secondary: #343a40;--bs-success: #3fb618;--bs-info: #9954bb;--bs-warning: #ff7518;--bs-danger: #ff0039;--bs-light: #f8f9fa;--bs-dark: #343a40;--bs-default-rgb: 52, 58, 64;--bs-primary-rgb: 39, 128, 227;--bs-secondary-rgb: 52, 58, 64;--bs-success-rgb: 63, 182, 24;--bs-info-rgb: 153, 84, 187;--bs-warning-rgb: 255, 117, 24;--bs-danger-rgb: 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0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow{bottom:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow{left:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-end .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow::before{right:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-right-color:var(--bs-tooltip-bg)}.bs-tooltip-bottom .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow{top:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-bottom .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow::before{bottom:-1px;border-width:0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-start .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow{right:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-start .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) 0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:0.875rem;--bs-popover-bg: #fff;--bs-popover-border-width: 1px;--bs-popover-border-color: rgba(0, 0, 0, 0.175);--bs-popover-border-radius: 0.5rem;--bs-popover-inner-border-radius: calc(0.5rem - 1px);--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-popover-header-padding-x: 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i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt,Nt),this._element.style[e]="",N.trigger(this._element,St)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(N.trigger(this._element,Dt).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,d(this._element),this._element.classList.add(Mt),this._element.classList.remove(Pt,Nt);for(const t of this._triggerArray){const e=z.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt),N.trigger(this._element,$t)}),this._element,!0)}_isShown(t=this._element){return 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s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new 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+//# sourceMappingURL=bootstrap.bundle.min.js.map
\ No newline at end of file
diff --git a/site_libs/clipboard/clipboard.min.js b/site_libs/clipboard/clipboard.min.js
new file mode 100644
index 0000000..1103f81
--- /dev/null
+++ b/site_libs/clipboard/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.11
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1.anchorjs-link,.anchorjs-link:focus{opacity:1}",A.sheet.cssRules.length),A.sheet.insertRule("[data-anchorjs-icon]::after{content:attr(data-anchorjs-icon)}",A.sheet.cssRules.length),A.sheet.insertRule('@font-face{font-family:anchorjs-icons;src:url(data:n/a;base64,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) format("truetype")}',A.sheet.cssRules.length)),h=document.querySelectorAll("[id]"),t=[].map.call(h,function(A){return A.id}),i=0;i\]./()*\\\n\t\b\v\u00A0]/g,"-").replace(/-{2,}/g,"-").substring(0,this.options.truncate).replace(/^-+|-+$/gm,"").toLowerCase()},this.hasAnchorJSLink=function(A){var e=A.firstChild&&-1<(" "+A.firstChild.className+" ").indexOf(" anchorjs-link "),A=A.lastChild&&-1<(" "+A.lastChild.className+" ").indexOf(" anchorjs-link ");return e||A||!1}}});
+// @license-end
\ No newline at end of file
diff --git a/site_libs/quarto-html/popper.min.js b/site_libs/quarto-html/popper.min.js
new file mode 100644
index 0000000..e3726d7
--- /dev/null
+++ b/site_libs/quarto-html/popper.min.js
@@ -0,0 +1,6 @@
+/**
+ * @popperjs/core v2.11.7 - MIT License
+ */
+
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self).Popper={})}(this,(function(e){"use strict";function t(e){if(null==e)return window;if("[object Window]"!==e.toString()){var t=e.ownerDocument;return t&&t.defaultView||window}return e}function n(e){return e instanceof t(e).Element||e instanceof Element}function r(e){return e instanceof t(e).HTMLElement||e instanceof HTMLElement}function o(e){return"undefined"!=typeof ShadowRoot&&(e instanceof t(e).ShadowRoot||e instanceof ShadowRoot)}var i=Math.max,a=Math.min,s=Math.round;function f(){var e=navigator.userAgentData;return null!=e&&e.brands&&Array.isArray(e.brands)?e.brands.map((function(e){return e.brand+"/"+e.version})).join(" "):navigator.userAgent}function c(){return!/^((?!chrome|android).)*safari/i.test(f())}function p(e,o,i){void 0===o&&(o=!1),void 0===i&&(i=!1);var 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+
diff --git a/site_libs/quarto-html/quarto-syntax-highlighting.css b/site_libs/quarto-html/quarto-syntax-highlighting.css
new file mode 100644
index 0000000..d9fd98f
--- /dev/null
+++ b/site_libs/quarto-html/quarto-syntax-highlighting.css
@@ -0,0 +1,203 @@
+/* quarto syntax highlight colors */
+:root {
+  --quarto-hl-ot-color: #003B4F;
+  --quarto-hl-at-color: #657422;
+  --quarto-hl-ss-color: #20794D;
+  --quarto-hl-an-color: #5E5E5E;
+  --quarto-hl-fu-color: #4758AB;
+  --quarto-hl-st-color: #20794D;
+  --quarto-hl-cf-color: #003B4F;
+  --quarto-hl-op-color: #5E5E5E;
+  --quarto-hl-er-color: #AD0000;
+  --quarto-hl-bn-color: #AD0000;
+  --quarto-hl-al-color: #AD0000;
+  --quarto-hl-va-color: #111111;
+  --quarto-hl-bu-color: inherit;
+  --quarto-hl-ex-color: inherit;
+  --quarto-hl-pp-color: #AD0000;
+  --quarto-hl-in-color: #5E5E5E;
+  --quarto-hl-vs-color: #20794D;
+  --quarto-hl-wa-color: #5E5E5E;
+  --quarto-hl-do-color: #5E5E5E;
+  --quarto-hl-im-color: #00769E;
+  --quarto-hl-ch-color: #20794D;
+  --quarto-hl-dt-color: #AD0000;
+  --quarto-hl-fl-color: #AD0000;
+  --quarto-hl-co-color: #5E5E5E;
+  --quarto-hl-cv-color: #5E5E5E;
+  --quarto-hl-cn-color: #8f5902;
+  --quarto-hl-sc-color: #5E5E5E;
+  --quarto-hl-dv-color: #AD0000;
+  --quarto-hl-kw-color: #003B4F;
+}
+
+/* other quarto variables */
+:root {
+  --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
+}
+
+pre > code.sourceCode > span {
+  color: #003B4F;
+}
+
+code span {
+  color: #003B4F;
+}
+
+code.sourceCode > span {
+  color: #003B4F;
+}
+
+div.sourceCode,
+div.sourceCode pre.sourceCode {
+  color: #003B4F;
+}
+
+code span.ot {
+  color: #003B4F;
+  font-style: inherit;
+}
+
+code span.at {
+  color: #657422;
+  font-style: inherit;
+}
+
+code span.ss {
+  color: #20794D;
+  font-style: inherit;
+}
+
+code span.an {
+  color: #5E5E5E;
+  font-style: inherit;
+}
+
+code span.fu {
+  color: #4758AB;
+  font-style: inherit;
+}
+
+code span.st {
+  color: #20794D;
+  font-style: inherit;
+}
+
+code span.cf {
+  color: #003B4F;
+  font-style: inherit;
+}
+
+code span.op {
+  color: #5E5E5E;
+  font-style: inherit;
+}
+
+code span.er {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.bn {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.al {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.va {
+  color: #111111;
+  font-style: inherit;
+}
+
+code span.bu {
+  font-style: inherit;
+}
+
+code span.ex {
+  font-style: inherit;
+}
+
+code span.pp {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.in {
+  color: #5E5E5E;
+  font-style: inherit;
+}
+
+code span.vs {
+  color: #20794D;
+  font-style: inherit;
+}
+
+code span.wa {
+  color: #5E5E5E;
+  font-style: italic;
+}
+
+code span.do {
+  color: #5E5E5E;
+  font-style: italic;
+}
+
+code span.im {
+  color: #00769E;
+  font-style: inherit;
+}
+
+code span.ch {
+  color: #20794D;
+  font-style: inherit;
+}
+
+code span.dt {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.fl {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.co {
+  color: #5E5E5E;
+  font-style: inherit;
+}
+
+code span.cv {
+  color: #5E5E5E;
+  font-style: italic;
+}
+
+code span.cn {
+  color: #8f5902;
+  font-style: inherit;
+}
+
+code span.sc {
+  color: #5E5E5E;
+  font-style: inherit;
+}
+
+code span.dv {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.kw {
+  color: #003B4F;
+  font-style: inherit;
+}
+
+.prevent-inlining {
+  content: " {
+  // Find any conflicting margin elements and add margins to the
+  // top to prevent overlap
+  const marginChildren = window.document.querySelectorAll(
+    ".column-margin.column-container > *, .margin-caption, .aside"
+  );
+
+  let lastBottom = 0;
+  for (const marginChild of marginChildren) {
+    if (marginChild.offsetParent !== null) {
+      // clear the top margin so we recompute it
+      marginChild.style.marginTop = null;
+      const top = marginChild.getBoundingClientRect().top + window.scrollY;
+      if (top < lastBottom) {
+        const marginChildStyle = window.getComputedStyle(marginChild);
+        const marginBottom = parseFloat(marginChildStyle["marginBottom"]);
+        const margin = lastBottom - top + marginBottom;
+        marginChild.style.marginTop = `${margin}px`;
+      }
+      const styles = window.getComputedStyle(marginChild);
+      const marginTop = parseFloat(styles["marginTop"]);
+      lastBottom = top + marginChild.getBoundingClientRect().height + marginTop;
+    }
+  }
+};
+
+window.document.addEventListener("DOMContentLoaded", function (_event) {
+  // Recompute the position of margin elements anytime the body size changes
+  if (window.ResizeObserver) {
+    const resizeObserver = new window.ResizeObserver(
+      throttle(() => {
+        layoutMarginEls();
+        if (
+          window.document.body.getBoundingClientRect().width < 990 &&
+          isReaderMode()
+        ) {
+          quartoToggleReader();
+        }
+      }, 50)
+    );
+    resizeObserver.observe(window.document.body);
+  }
+
+  const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]');
+  const sidebarEl = window.document.getElementById("quarto-sidebar");
+  const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left");
+  const marginSidebarEl = window.document.getElementById(
+    "quarto-margin-sidebar"
+  );
+  // function to determine whether the element has a previous sibling that is active
+  const prevSiblingIsActiveLink = (el) => {
+    const sibling = el.previousElementSibling;
+    if (sibling && sibling.tagName === "A") {
+      return sibling.classList.contains("active");
+    } else {
+      return false;
+    }
+  };
+
+  // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior)
+  function fireSlideEnter(e) {
+    const event = window.document.createEvent("Event");
+    event.initEvent("slideenter", true, true);
+    window.document.dispatchEvent(event);
+  }
+  const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]');
+  tabs.forEach((tab) => {
+    tab.addEventListener("shown.bs.tab", fireSlideEnter);
+  });
+
+  // fire slideEnter for tabby tab activations (for htmlwidget resize behavior)
+  document.addEventListener("tabby", fireSlideEnter, false);
+
+  // Track scrolling and mark TOC links as active
+  // get table of contents and sidebar (bail if we don't have at least one)
+  const tocLinks = tocEl
+    ? [...tocEl.querySelectorAll("a[data-scroll-target]")]
+    : [];
+  const makeActive = (link) => tocLinks[link].classList.add("active");
+  const removeActive = (link) => tocLinks[link].classList.remove("active");
+  const removeAllActive = () =>
+    [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link));
+
+  // activate the anchor for a section associated with this TOC entry
+  tocLinks.forEach((link) => {
+    link.addEventListener("click", () => {
+      if (link.href.indexOf("#") !== -1) {
+        const anchor = link.href.split("#")[1];
+        const heading = window.document.querySelector(
+          `[data-anchor-id=${anchor}]`
+        );
+        if (heading) {
+          // Add the class
+          heading.classList.add("reveal-anchorjs-link");
+
+          // function to show the anchor
+          const handleMouseout = () => {
+            heading.classList.remove("reveal-anchorjs-link");
+            heading.removeEventListener("mouseout", handleMouseout);
+          };
+
+          // add a function to clear the anchor when the user mouses out of it
+          heading.addEventListener("mouseout", handleMouseout);
+        }
+      }
+    });
+  });
+
+  const sections = tocLinks.map((link) => {
+    const target = link.getAttribute("data-scroll-target");
+    if (target.startsWith("#")) {
+      return window.document.getElementById(decodeURI(`${target.slice(1)}`));
+    } else {
+      return window.document.querySelector(decodeURI(`${target}`));
+    }
+  });
+
+  const sectionMargin = 200;
+  let currentActive = 0;
+  // track whether we've initialized state the first time
+  let init = false;
+
+  const updateActiveLink = () => {
+    // The index from bottom to top (e.g. reversed list)
+    let sectionIndex = -1;
+    if (
+      window.innerHeight + window.pageYOffset >=
+      window.document.body.offsetHeight
+    ) {
+      sectionIndex = 0;
+    } else {
+      sectionIndex = [...sections].reverse().findIndex((section) => {
+        if (section) {
+          return window.pageYOffset >= section.offsetTop - sectionMargin;
+        } else {
+          return false;
+        }
+      });
+    }
+    if (sectionIndex > -1) {
+      const current = sections.length - sectionIndex - 1;
+      if (current !== currentActive) {
+        removeAllActive();
+        currentActive = current;
+        makeActive(current);
+        if (init) {
+          window.dispatchEvent(sectionChanged);
+        }
+        init = true;
+      }
+    }
+  };
+
+  const inHiddenRegion = (top, bottom, hiddenRegions) => {
+    for (const region of hiddenRegions) {
+      if (top <= region.bottom && bottom >= region.top) {
+        return true;
+      }
+    }
+    return false;
+  };
+
+  const categorySelector = "header.quarto-title-block .quarto-category";
+  const activateCategories = (href) => {
+    // Find any categories
+    // Surround them with a link pointing back to:
+    // #category=Authoring
+    try {
+      const categoryEls = window.document.querySelectorAll(categorySelector);
+      for (const categoryEl of categoryEls) {
+        const categoryText = categoryEl.textContent;
+        if (categoryText) {
+          const link = `${href}#category=${encodeURIComponent(categoryText)}`;
+          const linkEl = window.document.createElement("a");
+          linkEl.setAttribute("href", link);
+          for (const child of categoryEl.childNodes) {
+            linkEl.append(child);
+          }
+          categoryEl.appendChild(linkEl);
+        }
+      }
+    } catch {
+      // Ignore errors
+    }
+  };
+  function hasTitleCategories() {
+    return window.document.querySelector(categorySelector) !== null;
+  }
+
+  function offsetRelativeUrl(url) {
+    const offset = getMeta("quarto:offset");
+    return offset ? offset + url : url;
+  }
+
+  function offsetAbsoluteUrl(url) {
+    const offset = getMeta("quarto:offset");
+    const baseUrl = new URL(offset, window.location);
+
+    const projRelativeUrl = url.replace(baseUrl, "");
+    if (projRelativeUrl.startsWith("/")) {
+      return projRelativeUrl;
+    } else {
+      return "/" + projRelativeUrl;
+    }
+  }
+
+  // read a meta tag value
+  function getMeta(metaName) {
+    const metas = window.document.getElementsByTagName("meta");
+    for (let i = 0; i < metas.length; i++) {
+      if (metas[i].getAttribute("name") === metaName) {
+        return metas[i].getAttribute("content");
+      }
+    }
+    return "";
+  }
+
+  async function findAndActivateCategories() {
+    const currentPagePath = offsetAbsoluteUrl(window.location.href);
+    const response = await fetch(offsetRelativeUrl("listings.json"));
+    if (response.status == 200) {
+      return response.json().then(function (listingPaths) {
+        const listingHrefs = [];
+        for (const listingPath of listingPaths) {
+          const pathWithoutLeadingSlash = listingPath.listing.substring(1);
+          for (const item of listingPath.items) {
+            if (
+              item === currentPagePath ||
+              item === currentPagePath + "index.html"
+            ) {
+              // Resolve this path against the offset to be sure
+              // we already are using the correct path to the listing
+              // (this adjusts the listing urls to be rooted against
+              // whatever root the page is actually running against)
+              const relative = offsetRelativeUrl(pathWithoutLeadingSlash);
+              const baseUrl = window.location;
+              const resolvedPath = new URL(relative, baseUrl);
+              listingHrefs.push(resolvedPath.pathname);
+              break;
+            }
+          }
+        }
+
+        // Look up the tree for a nearby linting and use that if we find one
+        const nearestListing = findNearestParentListing(
+          offsetAbsoluteUrl(window.location.pathname),
+          listingHrefs
+        );
+        if (nearestListing) {
+          activateCategories(nearestListing);
+        } else {
+          // See if the referrer is a listing page for this item
+          const referredRelativePath = offsetAbsoluteUrl(document.referrer);
+          const referrerListing = listingHrefs.find((listingHref) => {
+            const isListingReferrer =
+              listingHref === referredRelativePath ||
+              listingHref === referredRelativePath + "index.html";
+            return isListingReferrer;
+          });
+
+          if (referrerListing) {
+            // Try to use the referrer if possible
+            activateCategories(referrerListing);
+          } else if (listingHrefs.length > 0) {
+            // Otherwise, just fall back to the first listing
+            activateCategories(listingHrefs[0]);
+          }
+        }
+      });
+    }
+  }
+  if (hasTitleCategories()) {
+    findAndActivateCategories();
+  }
+
+  const findNearestParentListing = (href, listingHrefs) => {
+    if (!href || !listingHrefs) {
+      return undefined;
+    }
+    // Look up the tree for a nearby linting and use that if we find one
+    const relativeParts = href.substring(1).split("/");
+    while (relativeParts.length > 0) {
+      const path = relativeParts.join("/");
+      for (const listingHref of listingHrefs) {
+        if (listingHref.startsWith(path)) {
+          return listingHref;
+        }
+      }
+      relativeParts.pop();
+    }
+
+    return undefined;
+  };
+
+  const manageSidebarVisiblity = (el, placeholderDescriptor) => {
+    let isVisible = true;
+    let elRect;
+
+    return (hiddenRegions) => {
+      if (el === null) {
+        return;
+      }
+
+      // Find the last element of the TOC
+      const lastChildEl = el.lastElementChild;
+
+      if (lastChildEl) {
+        // Converts the sidebar to a menu
+        const convertToMenu = () => {
+          for (const child of el.children) {
+            child.style.opacity = 0;
+            child.style.overflow = "hidden";
+          }
+
+          nexttick(() => {
+            const toggleContainer = window.document.createElement("div");
+            toggleContainer.style.width = "100%";
+            toggleContainer.classList.add("zindex-over-content");
+            toggleContainer.classList.add("quarto-sidebar-toggle");
+            toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
+            toggleContainer.id = placeholderDescriptor.id;
+            toggleContainer.style.position = "fixed";
+
+            const toggleIcon = window.document.createElement("i");
+            toggleIcon.classList.add("quarto-sidebar-toggle-icon");
+            toggleIcon.classList.add("bi");
+            toggleIcon.classList.add("bi-caret-down-fill");
+
+            const toggleTitle = window.document.createElement("div");
+            const titleEl = window.document.body.querySelector(
+              placeholderDescriptor.titleSelector
+            );
+            if (titleEl) {
+              toggleTitle.append(
+                titleEl.textContent || titleEl.innerText,
+                toggleIcon
+              );
+            }
+            toggleTitle.classList.add("zindex-over-content");
+            toggleTitle.classList.add("quarto-sidebar-toggle-title");
+            toggleContainer.append(toggleTitle);
+
+            const toggleContents = window.document.createElement("div");
+            toggleContents.classList = el.classList;
+            toggleContents.classList.add("zindex-over-content");
+            toggleContents.classList.add("quarto-sidebar-toggle-contents");
+            for (const child of el.children) {
+              if (child.id === "toc-title") {
+                continue;
+              }
+
+              const clone = child.cloneNode(true);
+              clone.style.opacity = 1;
+              clone.style.display = null;
+              toggleContents.append(clone);
+            }
+            toggleContents.style.height = "0px";
+            const positionToggle = () => {
+              // position the element (top left of parent, same width as parent)
+              if (!elRect) {
+                elRect = el.getBoundingClientRect();
+              }
+              toggleContainer.style.left = `${elRect.left}px`;
+              toggleContainer.style.top = `${elRect.top}px`;
+              toggleContainer.style.width = `${elRect.width}px`;
+            };
+            positionToggle();
+
+            toggleContainer.append(toggleContents);
+            el.parentElement.prepend(toggleContainer);
+
+            // Process clicks
+            let tocShowing = false;
+            // Allow the caller to control whether this is dismissed
+            // when it is clicked (e.g. sidebar navigation supports
+            // opening and closing the nav tree, so don't dismiss on click)
+            const clickEl = placeholderDescriptor.dismissOnClick
+              ? toggleContainer
+              : toggleTitle;
+
+            const closeToggle = () => {
+              if (tocShowing) {
+                toggleContainer.classList.remove("expanded");
+                toggleContents.style.height = "0px";
+                tocShowing = false;
+              }
+            };
+
+            // Get rid of any expanded toggle if the user scrolls
+            window.document.addEventListener(
+              "scroll",
+              throttle(() => {
+                closeToggle();
+              }, 50)
+            );
+
+            // Handle positioning of the toggle
+            window.addEventListener(
+              "resize",
+              throttle(() => {
+                elRect = undefined;
+                positionToggle();
+              }, 50)
+            );
+
+            window.addEventListener("quarto-hrChanged", () => {
+              elRect = undefined;
+            });
+
+            // Process the click
+            clickEl.onclick = () => {
+              if (!tocShowing) {
+                toggleContainer.classList.add("expanded");
+                toggleContents.style.height = null;
+                tocShowing = true;
+              } else {
+                closeToggle();
+              }
+            };
+          });
+        };
+
+        // Converts a sidebar from a menu back to a sidebar
+        const convertToSidebar = () => {
+          for (const child of el.children) {
+            child.style.opacity = 1;
+            child.style.overflow = null;
+          }
+
+          const placeholderEl = window.document.getElementById(
+            placeholderDescriptor.id
+          );
+          if (placeholderEl) {
+            placeholderEl.remove();
+          }
+
+          el.classList.remove("rollup");
+        };
+
+        if (isReaderMode()) {
+          convertToMenu();
+          isVisible = false;
+        } else {
+          // Find the top and bottom o the element that is being managed
+          const elTop = el.offsetTop;
+          const elBottom =
+            elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;
+
+          if (!isVisible) {
+            // If the element is current not visible reveal if there are
+            // no conflicts with overlay regions
+            if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
+              convertToSidebar();
+              isVisible = true;
+            }
+          } else {
+            // If the element is visible, hide it if it conflicts with overlay regions
+            // and insert a placeholder toggle (or if we're in reader mode)
+            if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
+              convertToMenu();
+              isVisible = false;
+            }
+          }
+        }
+      }
+    };
+  };
+
+  const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]');
+  for (const tabEl of tabEls) {
+    const id = tabEl.getAttribute("data-bs-target");
+    if (id) {
+      const columnEl = document.querySelector(
+        `${id} .column-margin, .tabset-margin-content`
+      );
+      if (columnEl)
+        tabEl.addEventListener("shown.bs.tab", function (event) {
+          const el = event.srcElement;
+          if (el) {
+            const visibleCls = `${el.id}-margin-content`;
+            // walk up until we find a parent tabset
+            let panelTabsetEl = el.parentElement;
+            while (panelTabsetEl) {
+              if (panelTabsetEl.classList.contains("panel-tabset")) {
+                break;
+              }
+              panelTabsetEl = panelTabsetEl.parentElement;
+            }
+
+            if (panelTabsetEl) {
+              const prevSib = panelTabsetEl.previousElementSibling;
+              if (
+                prevSib &&
+                prevSib.classList.contains("tabset-margin-container")
+              ) {
+                const childNodes = prevSib.querySelectorAll(
+                  ".tabset-margin-content"
+                );
+                for (const childEl of childNodes) {
+                  if (childEl.classList.contains(visibleCls)) {
+                    childEl.classList.remove("collapse");
+                  } else {
+                    childEl.classList.add("collapse");
+                  }
+                }
+              }
+            }
+          }
+
+          layoutMarginEls();
+        });
+    }
+  }
+
+  // Manage the visibility of the toc and the sidebar
+  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
+    id: "quarto-toc-toggle",
+    titleSelector: "#toc-title",
+    dismissOnClick: true,
+  });
+  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
+    id: "quarto-sidebarnav-toggle",
+    titleSelector: ".title",
+    dismissOnClick: false,
+  });
+  let tocLeftScrollVisibility;
+  if (leftTocEl) {
+    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
+      id: "quarto-lefttoc-toggle",
+      titleSelector: "#toc-title",
+      dismissOnClick: true,
+    });
+  }
+
+  // Find the first element that uses formatting in special columns
+  const conflictingEls = window.document.body.querySelectorAll(
+    '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
+  );
+
+  // Filter all the possibly conflicting elements into ones
+  // the do conflict on the left or ride side
+  const arrConflictingEls = Array.from(conflictingEls);
+  const leftSideConflictEls = arrConflictingEls.filter((el) => {
+    if (el.tagName === "ASIDE") {
+      return false;
+    }
+    return Array.from(el.classList).find((className) => {
+      return (
+        className !== "column-body" &&
+        className.startsWith("column-") &&
+        !className.endsWith("right") &&
+        !className.endsWith("container") &&
+        className !== "column-margin"
+      );
+    });
+  });
+  const rightSideConflictEls = arrConflictingEls.filter((el) => {
+    if (el.tagName === "ASIDE") {
+      return true;
+    }
+
+    const hasMarginCaption = Array.from(el.classList).find((className) => {
+      return className == "margin-caption";
+    });
+    if (hasMarginCaption) {
+      return true;
+    }
+
+    return Array.from(el.classList).find((className) => {
+      return (
+        className !== "column-body" &&
+        !className.endsWith("container") &&
+        className.startsWith("column-") &&
+        !className.endsWith("left")
+      );
+    });
+  });
+
+  const kOverlapPaddingSize = 10;
+  function toRegions(els) {
+    return els.map((el) => {
+      const boundRect = el.getBoundingClientRect();
+      const top =
+        boundRect.top +
+        document.documentElement.scrollTop -
+        kOverlapPaddingSize;
+      return {
+        top,
+        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
+      };
+    });
+  }
+
+  let hasObserved = false;
+  const visibleItemObserver = (els) => {
+    let visibleElements = [...els];
+    const intersectionObserver = new IntersectionObserver(
+      (entries, _observer) => {
+        entries.forEach((entry) => {
+          if (entry.isIntersecting) {
+            if (visibleElements.indexOf(entry.target) === -1) {
+              visibleElements.push(entry.target);
+            }
+          } else {
+            visibleElements = visibleElements.filter((visibleEntry) => {
+              return visibleEntry !== entry;
+            });
+          }
+        });
+
+        if (!hasObserved) {
+          hideOverlappedSidebars();
+        }
+        hasObserved = true;
+      },
+      {}
+    );
+    els.forEach((el) => {
+      intersectionObserver.observe(el);
+    });
+
+    return {
+      getVisibleEntries: () => {
+        return visibleElements;
+      },
+    };
+  };
+
+  const rightElementObserver = visibleItemObserver(rightSideConflictEls);
+  const leftElementObserver = visibleItemObserver(leftSideConflictEls);
+
+  const hideOverlappedSidebars = () => {
+    marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
+    sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
+    if (tocLeftScrollVisibility) {
+      tocLeftScrollVisibility(
+        toRegions(leftElementObserver.getVisibleEntries())
+      );
+    }
+  };
+
+  window.quartoToggleReader = () => {
+    // Applies a slow class (or removes it)
+    // to update the transition speed
+    const slowTransition = (slow) => {
+      const manageTransition = (id, slow) => {
+        const el = document.getElementById(id);
+        if (el) {
+          if (slow) {
+            el.classList.add("slow");
+          } else {
+            el.classList.remove("slow");
+          }
+        }
+      };
+
+      manageTransition("TOC", slow);
+      manageTransition("quarto-sidebar", slow);
+    };
+    const readerMode = !isReaderMode();
+    setReaderModeValue(readerMode);
+
+    // If we're entering reader mode, slow the transition
+    if (readerMode) {
+      slowTransition(readerMode);
+    }
+    highlightReaderToggle(readerMode);
+    hideOverlappedSidebars();
+
+    // If we're exiting reader mode, restore the non-slow transition
+    if (!readerMode) {
+      slowTransition(!readerMode);
+    }
+  };
+
+  const highlightReaderToggle = (readerMode) => {
+    const els = document.querySelectorAll(".quarto-reader-toggle");
+    if (els) {
+      els.forEach((el) => {
+        if (readerMode) {
+          el.classList.add("reader");
+        } else {
+          el.classList.remove("reader");
+        }
+      });
+    }
+  };
+
+  const setReaderModeValue = (val) => {
+    if (window.location.protocol !== "file:") {
+      window.localStorage.setItem("quarto-reader-mode", val);
+    } else {
+      localReaderMode = val;
+    }
+  };
+
+  const isReaderMode = () => {
+    if (window.location.protocol !== "file:") {
+      return window.localStorage.getItem("quarto-reader-mode") === "true";
+    } else {
+      return localReaderMode;
+    }
+  };
+  let localReaderMode = null;
+
+  const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded");
+  const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;
+
+  // Walk the TOC and collapse/expand nodes
+  // Nodes are expanded if:
+  // - they are top level
+  // - they have children that are 'active' links
+  // - they are directly below an link that is 'active'
+  const walk = (el, depth) => {
+    // Tick depth when we enter a UL
+    if (el.tagName === "UL") {
+      depth = depth + 1;
+    }
+
+    // It this is active link
+    let isActiveNode = false;
+    if (el.tagName === "A" && el.classList.contains("active")) {
+      isActiveNode = true;
+    }
+
+    // See if there is an active child to this element
+    let hasActiveChild = false;
+    for (child of el.children) {
+      hasActiveChild = walk(child, depth) || hasActiveChild;
+    }
+
+    // Process the collapse state if this is an UL
+    if (el.tagName === "UL") {
+      if (tocOpenDepth === -1 && depth > 1) {
+        el.classList.add("collapse");
+      } else if (
+        depth <= tocOpenDepth ||
+        hasActiveChild ||
+        prevSiblingIsActiveLink(el)
+      ) {
+        el.classList.remove("collapse");
+      } else {
+        el.classList.add("collapse");
+      }
+
+      // untick depth when we leave a UL
+      depth = depth - 1;
+    }
+    return hasActiveChild || isActiveNode;
+  };
+
+  // walk the TOC and expand / collapse any items that should be shown
+
+  if (tocEl) {
+    walk(tocEl, 0);
+    updateActiveLink();
+  }
+
+  // Throttle the scroll event and walk peridiocally
+  window.document.addEventListener(
+    "scroll",
+    throttle(() => {
+      if (tocEl) {
+        updateActiveLink();
+        walk(tocEl, 0);
+      }
+      if (!isReaderMode()) {
+        hideOverlappedSidebars();
+      }
+    }, 5)
+  );
+  window.addEventListener(
+    "resize",
+    throttle(() => {
+      if (!isReaderMode()) {
+        hideOverlappedSidebars();
+      }
+    }, 10)
+  );
+  hideOverlappedSidebars();
+  highlightReaderToggle(isReaderMode());
+});
+
+// grouped tabsets
+window.addEventListener("pageshow", (_event) => {
+  function getTabSettings() {
+    const data = localStorage.getItem("quarto-persistent-tabsets-data");
+    if (!data) {
+      localStorage.setItem("quarto-persistent-tabsets-data", "{}");
+      return {};
+    }
+    if (data) {
+      return JSON.parse(data);
+    }
+  }
+
+  function setTabSettings(data) {
+    localStorage.setItem(
+      "quarto-persistent-tabsets-data",
+      JSON.stringify(data)
+    );
+  }
+
+  function setTabState(groupName, groupValue) {
+    const data = getTabSettings();
+    data[groupName] = groupValue;
+    setTabSettings(data);
+  }
+
+  function toggleTab(tab, active) {
+    const tabPanelId = tab.getAttribute("aria-controls");
+    const tabPanel = document.getElementById(tabPanelId);
+    if (active) {
+      tab.classList.add("active");
+      tabPanel.classList.add("active");
+    } else {
+      tab.classList.remove("active");
+      tabPanel.classList.remove("active");
+    }
+  }
+
+  function toggleAll(selectedGroup, selectorsToSync) {
+    for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
+      const active = selectedGroup === thisGroup;
+      for (const tab of tabs) {
+        toggleTab(tab, active);
+      }
+    }
+  }
+
+  function findSelectorsToSyncByLanguage() {
+    const result = {};
+    const tabs = Array.from(
+      document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
+    );
+    for (const item of tabs) {
+      const div = item.parentElement.parentElement.parentElement;
+      const group = div.getAttribute("data-group");
+      if (!result[group]) {
+        result[group] = {};
+      }
+      const selectorsToSync = result[group];
+      const value = item.innerHTML;
+      if (!selectorsToSync[value]) {
+        selectorsToSync[value] = [];
+      }
+      selectorsToSync[value].push(item);
+    }
+    return result;
+  }
+
+  function setupSelectorSync() {
+    const selectorsToSync = findSelectorsToSyncByLanguage();
+    Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
+      Object.entries(tabSetsByValue).forEach(([value, items]) => {
+        items.forEach((item) => {
+          item.addEventListener("click", (_event) => {
+            setTabState(group, value);
+            toggleAll(value, selectorsToSync[group]);
+          });
+        });
+      });
+    });
+    return selectorsToSync;
+  }
+
+  const selectorsToSync = setupSelectorSync();
+  for (const [group, selectedName] of Object.entries(getTabSettings())) {
+    const selectors = selectorsToSync[group];
+    // it's possible that stale state gives us empty selections, so we explicitly check here.
+    if (selectors) {
+      toggleAll(selectedName, selectors);
+    }
+  }
+});
+
+function throttle(func, wait) {
+  let waiting = false;
+  return function () {
+    if (!waiting) {
+      func.apply(this, arguments);
+      waiting = true;
+      setTimeout(function () {
+        waiting = false;
+      }, wait);
+    }
+  };
+}
+
+function nexttick(func) {
+  return setTimeout(func, 0);
+}
diff --git a/site_libs/quarto-html/tippy.css b/site_libs/quarto-html/tippy.css
new file mode 100644
index 0000000..e6ae635
--- /dev/null
+++ b/site_libs/quarto-html/tippy.css
@@ -0,0 +1 @@
+.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 8px;border-left-color:initial;right:-7px;transform-origin:center left}.tippy-box[data-placement^=right]>.tippy-arrow{left:0}.tippy-box[data-placement^=right]>.tippy-arrow:before{left:-7px;border-width:8px 8px 8px 0;border-right-color:initial;transform-origin:center right}.tippy-box[data-inertia][data-state=visible]{transition-timing-function:cubic-bezier(.54,1.5,.38,1.11)}.tippy-arrow{width:16px;height:16px;color:#333}.tippy-arrow:before{content:"";position:absolute;border-color:transparent;border-style:solid}.tippy-content{position:relative;padding:5px 9px;z-index:1}
\ No newline at end of file
diff --git a/site_libs/quarto-html/tippy.umd.min.js b/site_libs/quarto-html/tippy.umd.min.js
new file mode 100644
index 0000000..ca292be
--- /dev/null
+++ b/site_libs/quarto-html/tippy.umd.min.js
@@ -0,0 +1,2 @@
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s=r("reference")?(e.popperInstance?e.popperInstance.state.elements.reference:t).getBoundingClientRect():null,u=r("popper")?n.getBoundingClientRect():null;(s&&Q(o,s)||u&&Q(i,u))&&e.popperInstance&&e.popperInstance.update(),o=s,i=u,e.state.isMounted&&requestAnimationFrame(a)}return{onMount:function(){e.props.sticky&&a()}}}};function Q(e,t){return!e||!t||(e.top!==t.top||e.right!==t.right||e.bottom!==t.bottom||e.left!==t.left)}return F.setDefaultProps({plugins:[Y,J,G,K],render:N}),F.createSingleton=function(e,t){var n;void 0===t&&(t={});var r,o=e,i=[],a=[],c=t.overrides,p=[],f=!1;function l(){a=o.map((function(e){return u(e.props.triggerTarget||e.reference)})).reduce((function(e,t){return e.concat(t)}),[])}function v(){i=o.map((function(e){return e.reference}))}function m(e){o.forEach((function(t){e?t.enable():t.disable()}))}function g(e){return o.map((function(t){var n=t.setProps;return t.setProps=function(o){n(o),t.reference===r&&e.setProps(o)},function(){t.setProps=n}}))}function h(e,t){var n=a.indexOf(t);if(t!==r){r=t;var s=(c||[]).concat("content").reduce((function(e,t){return e[t]=o[n].props[t],e}),{});e.setProps(Object.assign({},s,{getReferenceClientRect:"function"==typeof s.getReferenceClientRect?s.getReferenceClientRect:function(){var e;return null==(e=i[n])?void 0:e.getBoundingClientRect()}}))}}m(!1),v(),l();var b={fn:function(){return{onDestroy:function(){m(!0)},onHidden:function(){r=null},onClickOutside:function(e){e.props.showOnCreate&&!f&&(f=!0,r=null)},onShow:function(e){e.props.showOnCreate&&!f&&(f=!0,h(e,i[0]))},onTrigger:function(e,t){h(e,t.currentTarget)}}}},y=F(d(),Object.assign({},s(t,["overrides"]),{plugins:[b].concat(t.plugins||[]),triggerTarget:a,popperOptions:Object.assign({},t.popperOptions,{modifiers:[].concat((null==(n=t.popperOptions)?void 0:n.modifiers)||[],[W])})})),w=y.show;y.show=function(e){if(w(),!r&&null==e)return h(y,i[0]);if(!r||null!=e){if("number"==typeof e)return i[e]&&h(y,i[e]);if(o.indexOf(e)>=0){var t=e.reference;return h(y,t)}return i.indexOf(e)>=0?h(y,e):void 0}},y.showNext=function(){var e=i[0];if(!r)return y.show(0);var t=i.indexOf(r);y.show(i[t+1]||e)},y.showPrevious=function(){var e=i[i.length-1];if(!r)return y.show(e);var t=i.indexOf(r),n=i[t-1]||e;y.show(n)};var E=y.setProps;return y.setProps=function(e){c=e.overrides||c,E(e)},y.setInstances=function(e){m(!0),p.forEach((function(e){return e()})),o=e,m(!1),v(),l(),p=g(y),y.setProps({triggerTarget:a})},p=g(y),y},F.delegate=function(e,n){var r=[],o=[],i=!1,a=n.target,c=s(n,["target"]),p=Object.assign({},c,{trigger:"manual",touch:!1}),f=Object.assign({touch:R.touch},c,{showOnCreate:!0}),l=F(e,p);function d(e){if(e.target&&!i){var t=e.target.closest(a);if(t){var r=t.getAttribute("data-tippy-trigger")||n.trigger||R.trigger;if(!t._tippy&&!("touchstart"===e.type&&"boolean"==typeof f.touch||"touchstart"!==e.type&&r.indexOf(X[e.type])<0)){var s=F(t,f);s&&(o=o.concat(s))}}}}function v(e,t,n,o){void 0===o&&(o=!1),e.addEventListener(t,n,o),r.push({node:e,eventType:t,handler:n,options:o})}return u(l).forEach((function(e){var n=e.destroy,a=e.enable,s=e.disable;e.destroy=function(e){void 0===e&&(e=!0),e&&o.forEach((function(e){e.destroy()})),o=[],r.forEach((function(e){var t=e.node,n=e.eventType,r=e.handler,o=e.options;t.removeEventListener(n,r,o)})),r=[],n()},e.enable=function(){a(),o.forEach((function(e){return e.enable()})),i=!1},e.disable=function(){s(),o.forEach((function(e){return e.disable()})),i=!0},function(e){var n=e.reference;v(n,"touchstart",d,t),v(n,"mouseover",d),v(n,"focusin",d),v(n,"click",d)}(e)})),l},F.hideAll=function(e){var t=void 0===e?{}:e,n=t.exclude,r=t.duration;U.forEach((function(e){var t=!1;if(n&&(t=g(n)?e.reference===n:e.popper===n.popper),!t){var o=e.props.duration;e.setProps({duration:r}),e.hide(),e.state.isDestroyed||e.setProps({duration:o})}}))},F.roundArrow='',F}));
+
diff --git a/site_libs/quarto-nav/headroom.min.js b/site_libs/quarto-nav/headroom.min.js
new file mode 100644
index 0000000..b08f1df
--- /dev/null
+++ b/site_libs/quarto-nav/headroom.min.js
@@ -0,0 +1,7 @@
+/*!
+ * headroom.js v0.12.0 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return Math.max(t.offsetHeight,t.clientHeight)},scrollY:function(){return t.scrollTop}}}(t)}function n(t,s,e){var n,o=function(){var n=!1;try{var t={get passive(){n=!0}};window.addEventListener("test",t,t),window.removeEventListener("test",t,t)}catch(t){n=!1}return n}(),i=!1,r=d(t),l=r.scrollY(),a={};function c(){var t=Math.round(r.scrollY()),n=r.height(),o=r.scrollHeight();a.scrollY=t,a.lastScrollY=l,a.direction=ls.tolerance[a.direction],e(a),l=t,i=!1}function h(){i||(i=!0,n=requestAnimationFrame(c))}var u=!!o&&{passive:!0,capture:!1};return t.addEventListener("scroll",h,u),c(),{destroy:function(){cancelAnimationFrame(n),t.removeEventListener("scroll",h,u)}}}function o(t){return t===Object(t)?t:{down:t,up:t}}function s(t,n){n=n||{},Object.assign(this,s.options,n),this.classes=Object.assign({},s.options.classes,n.classes),this.elem=t,this.tolerance=o(this.tolerance),this.offset=o(this.offset),this.initialised=!1,this.frozen=!1}return s.prototype={constructor:s,init:function(){return s.cutsTheMustard&&!this.initialised&&(this.addClass("initial"),this.initialised=!0,setTimeout(function(t){t.scrollTracker=n(t.scroller,{offset:t.offset,tolerance:t.tolerance},t.update.bind(t))},100,this)),this},destroy:function(){this.initialised=!1,Object.keys(this.classes).forEach(this.removeClass,this),this.scrollTracker.destroy()},unpin:function(){!this.hasClass("pinned")&&this.hasClass("unpinned")||(this.addClass("unpinned"),this.removeClass("pinned"),this.onUnpin&&this.onUnpin.call(this))},pin:function(){this.hasClass("unpinned")&&(this.addClass("pinned"),this.removeClass("unpinned"),this.onPin&&this.onPin.call(this))},freeze:function(){this.frozen=!0,this.addClass("frozen")},unfreeze:function(){this.frozen=!1,this.removeClass("frozen")},top:function(){this.hasClass("top")||(this.addClass("top"),this.removeClass("notTop"),this.onTop&&this.onTop.call(this))},notTop:function(){this.hasClass("notTop")||(this.addClass("notTop"),this.removeClass("top"),this.onNotTop&&this.onNotTop.call(this))},bottom:function(){this.hasClass("bottom")||(this.addClass("bottom"),this.removeClass("notBottom"),this.onBottom&&this.onBottom.call(this))},notBottom:function(){this.hasClass("notBottom")||(this.addClass("notBottom"),this.removeClass("bottom"),this.onNotBottom&&this.onNotBottom.call(this))},shouldUnpin:function(t){return"down"===t.direction&&!t.top&&t.toleranceExceeded},shouldPin:function(t){return"up"===t.direction&&t.toleranceExceeded||t.top},addClass:function(t){this.elem.classList.add.apply(this.elem.classList,this.classes[t].split(" "))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},s.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},s.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),s});
diff --git a/site_libs/quarto-nav/quarto-nav.js b/site_libs/quarto-nav/quarto-nav.js
new file mode 100644
index 0000000..f6a53b1
--- /dev/null
+++ b/site_libs/quarto-nav/quarto-nav.js
@@ -0,0 +1,289 @@
+const headroomChanged = new CustomEvent("quarto-hrChanged", {
+  detail: {},
+  bubbles: true,
+  cancelable: false,
+  composed: false,
+});
+
+window.document.addEventListener("DOMContentLoaded", function () {
+  let init = false;
+
+  // Manage the back to top button, if one is present.
+  let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;
+  const scrollDownBuffer = 5;
+  const scrollUpBuffer = 35;
+  const btn = document.getElementById("quarto-back-to-top");
+  const hideBackToTop = () => {
+    btn.style.display = "none";
+  };
+  const showBackToTop = () => {
+    btn.style.display = "inline-block";
+  };
+  if (btn) {
+    window.document.addEventListener(
+      "scroll",
+      function () {
+        const currentScrollTop =
+          window.pageYOffset || document.documentElement.scrollTop;
+
+        // Shows and hides the button 'intelligently' as the user scrolls
+        if (currentScrollTop - scrollDownBuffer > lastScrollTop) {
+          hideBackToTop();
+          lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop;
+        } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) {
+          showBackToTop();
+          lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop;
+        }
+
+        // Show the button at the bottom, hides it at the top
+        if (currentScrollTop <= 0) {
+          hideBackToTop();
+        } else if (
+          window.innerHeight + currentScrollTop >=
+          document.body.offsetHeight
+        ) {
+          showBackToTop();
+        }
+      },
+      false
+    );
+  }
+
+  function throttle(func, wait) {
+    var timeout;
+    return function () {
+      const context = this;
+      const args = arguments;
+      const later = function () {
+        clearTimeout(timeout);
+        timeout = null;
+        func.apply(context, args);
+      };
+
+      if (!timeout) {
+        timeout = setTimeout(later, wait);
+      }
+    };
+  }
+
+  function headerOffset() {
+    // Set an offset if there is are fixed top navbar
+    const headerEl = window.document.querySelector("header.fixed-top");
+    if (headerEl) {
+      return headerEl.clientHeight;
+    } else {
+      return 0;
+    }
+  }
+
+  function footerOffset() {
+    const footerEl = window.document.querySelector("footer.footer");
+    if (footerEl) {
+      return footerEl.clientHeight;
+    } else {
+      return 0;
+    }
+  }
+
+  function dashboardOffset() {
+    const dashboardNavEl = window.document.getElementById(
+      "quarto-dashboard-header"
+    );
+    if (dashboardNavEl !== null) {
+      return dashboardNavEl.clientHeight;
+    } else {
+      return 0;
+    }
+  }
+
+  function updateDocumentOffsetWithoutAnimation() {
+    updateDocumentOffset(false);
+  }
+
+  function updateDocumentOffset(animated) {
+    // set body offset
+    const topOffset = headerOffset();
+    const bodyOffset = topOffset + footerOffset() + dashboardOffset();
+    const bodyEl = window.document.body;
+    bodyEl.setAttribute("data-bs-offset", topOffset);
+    bodyEl.style.paddingTop = topOffset + "px";
+
+    // deal with sidebar offsets
+    const sidebars = window.document.querySelectorAll(
+      ".sidebar, .headroom-target"
+    );
+    sidebars.forEach((sidebar) => {
+      if (!animated) {
+        sidebar.classList.add("notransition");
+        // Remove the no transition class after the animation has time to complete
+        setTimeout(function () {
+          sidebar.classList.remove("notransition");
+        }, 201);
+      }
+
+      if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) {
+        sidebar.style.top = "0";
+        sidebar.style.maxHeight = "100vh";
+      } else {
+        sidebar.style.top = topOffset + "px";
+        sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)";
+      }
+    });
+
+    // allow space for footer
+    const mainContainer = window.document.querySelector(".quarto-container");
+    if (mainContainer) {
+      mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)";
+    }
+
+    // link offset
+    let linkStyle = window.document.querySelector("#quarto-target-style");
+    if (!linkStyle) {
+      linkStyle = window.document.createElement("style");
+      linkStyle.setAttribute("id", "quarto-target-style");
+      window.document.head.appendChild(linkStyle);
+    }
+    while (linkStyle.firstChild) {
+      linkStyle.removeChild(linkStyle.firstChild);
+    }
+    if (topOffset > 0) {
+      linkStyle.appendChild(
+        window.document.createTextNode(`
+      section:target::before {
+        content: "";
+        display: block;
+        height: ${topOffset}px;
+        margin: -${topOffset}px 0 0;
+      }`)
+      );
+    }
+    if (init) {
+      window.dispatchEvent(headroomChanged);
+    }
+    init = true;
+  }
+
+  // initialize headroom
+  var header = window.document.querySelector("#quarto-header");
+  if (header && window.Headroom) {
+    const headroom = new window.Headroom(header, {
+      tolerance: 5,
+      onPin: function () {
+        const sidebars = window.document.querySelectorAll(
+          ".sidebar, .headroom-target"
+        );
+        sidebars.forEach((sidebar) => {
+          sidebar.classList.remove("sidebar-unpinned");
+        });
+        updateDocumentOffset();
+      },
+      onUnpin: function () {
+        const sidebars = window.document.querySelectorAll(
+          ".sidebar, .headroom-target"
+        );
+        sidebars.forEach((sidebar) => {
+          sidebar.classList.add("sidebar-unpinned");
+        });
+        updateDocumentOffset();
+      },
+    });
+    headroom.init();
+
+    let frozen = false;
+    window.quartoToggleHeadroom = function () {
+      if (frozen) {
+        headroom.unfreeze();
+        frozen = false;
+      } else {
+        headroom.freeze();
+        frozen = true;
+      }
+    };
+  }
+
+  window.addEventListener(
+    "hashchange",
+    function (e) {
+      if (
+        getComputedStyle(document.documentElement).scrollBehavior !== "smooth"
+      ) {
+        window.scrollTo(0, window.pageYOffset - headerOffset());
+      }
+    },
+    false
+  );
+
+  // Observe size changed for the header
+  const headerEl = window.document.querySelector("header.fixed-top");
+  if (headerEl && window.ResizeObserver) {
+    const observer = new window.ResizeObserver(() => {
+      setTimeout(updateDocumentOffsetWithoutAnimation, 0);
+    });
+    observer.observe(headerEl, {
+      attributes: true,
+      childList: true,
+      characterData: true,
+    });
+  } else {
+    window.addEventListener(
+      "resize",
+      throttle(updateDocumentOffsetWithoutAnimation, 50)
+    );
+  }
+  setTimeout(updateDocumentOffsetWithoutAnimation, 250);
+
+  // fixup index.html links if we aren't on the filesystem
+  if (window.location.protocol !== "file:") {
+    const links = window.document.querySelectorAll("a");
+    for (let i = 0; i < links.length; i++) {
+      if (links[i].href) {
+        links[i].dataset.originalHref = links[i].href;
+        links[i].href = links[i].href.replace(/\/index\.html/, "/");
+      }
+    }
+
+    // Fixup any sharing links that require urls
+    // Append url to any sharing urls
+    const sharingLinks = window.document.querySelectorAll(
+      "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item"
+    );
+    for (let i = 0; i < sharingLinks.length; i++) {
+      const sharingLink = sharingLinks[i];
+      const href = sharingLink.getAttribute("href");
+      if (href) {
+        sharingLink.setAttribute(
+          "href",
+          href.replace("|url|", window.location.href)
+        );
+      }
+    }
+
+    // Scroll the active navigation item into view, if necessary
+    const navSidebar = window.document.querySelector("nav#quarto-sidebar");
+    if (navSidebar) {
+      // Find the active item
+      const activeItem = navSidebar.querySelector("li.sidebar-item a.active");
+      if (activeItem) {
+        // Wait for the scroll height and height to resolve by observing size changes on the
+        // nav element that is scrollable
+        const resizeObserver = new ResizeObserver((_entries) => {
+          // The bottom of the element
+          const elBottom = activeItem.offsetTop;
+          const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight;
+
+          // The element height and scroll height are the same, then we are still loading
+          if (viewBottom !== navSidebar.scrollHeight) {
+            // Determine if the item isn't visible and scroll to it
+            if (elBottom >= viewBottom) {
+              navSidebar.scrollTop = elBottom;
+            }
+
+            // stop observing now since we've completed the scroll
+            resizeObserver.unobserve(navSidebar);
+          }
+        });
+        resizeObserver.observe(navSidebar);
+      }
+    }
+  }
+});
diff --git a/site_libs/quarto-search/autocomplete.umd.js b/site_libs/quarto-search/autocomplete.umd.js
new file mode 100644
index 0000000..ae0063a
--- /dev/null
+++ b/site_libs/quarto-search/autocomplete.umd.js
@@ -0,0 +1,3 @@
+/*! @algolia/autocomplete-js 1.11.1 | MIT License | © Algolia, Inc. and contributors | https://github.com/algolia/autocomplete */
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self)["@algolia/autocomplete-js"]={})}(this,(function(e){"use strict";function t(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function n(e){for(var n=1;n=0||(o[n]=e[n]);return o}(e,t);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(e);for(r=0;r=0||Object.prototype.propertyIsEnumerable.call(e,n)&&(o[n]=e[n])}return o}function a(e,t){return function(e){if(Array.isArray(e))return e}(e)||function(e,t){var n=null==e?null:"undefined"!=typeof Symbol&&e[Symbol.iterator]||e["@@iterator"];if(null!=n){var r,o,i,u,a=[],l=!0,c=!1;try{if(i=(n=n.call(e)).next,0===t){if(Object(n)!==n)return;l=!1}else 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diff --git a/site_libs/quarto-search/fuse.min.js b/site_libs/quarto-search/fuse.min.js
new file mode 100644
index 0000000..adc2835
--- /dev/null
+++ b/site_libs/quarto-search/fuse.min.js
@@ -0,0 +1,9 @@
+/**
+ * Fuse.js v6.6.2 - Lightweight fuzzy-search (http://fusejs.io)
+ *
+ * Copyright (c) 2022 Kiro Risk (http://kiro.me)
+ * All Rights Reserved. Apache Software License 2.0
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ */
+var e,t;e=this,t=function(){"use strict";function e(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function t(t){for(var n=1;ne.length)&&(t=e.length);for(var n=0,r=new Array(t);n0&&void 0!==arguments[0]?arguments[0]:1,t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3,n=new Map,r=Math.pow(10,t);return{get:function(t){var i=t.match(C).length;if(n.has(i))return n.get(i);var o=1/Math.pow(i,.5*e),c=parseFloat(Math.round(o*r)/r);return n.set(i,c),c},clear:function(){n.clear()}}}var $=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},n=t.getFn,i=void 0===n?I.getFn:n,o=t.fieldNormWeight,c=void 0===o?I.fieldNormWeight:o;r(this,e),this.norm=E(c,3),this.getFn=i,this.isCreated=!1,this.setIndexRecords()}return o(e,[{key:"setSources",value:function(){var e=arguments.length>0&&void 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R(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},n=t.errors,r=void 0===n?0:n,i=t.currentLocation,o=void 0===i?0:i,c=t.expectedLocation,a=void 0===c?0:c,s=t.distance,u=void 0===s?I.distance:s,h=t.ignoreLocation,l=void 0===h?I.ignoreLocation:h,f=r/e.length;if(l)return f;var d=Math.abs(a-o);return u?f+d/u:d?1:f}function N(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:I.minMatchCharLength,n=[],r=-1,i=-1,o=0,c=e.length;o=t&&n.push([r,i]),r=-1)}return e[o-1]&&o-r>=t&&n.push([r,o-1]),n}var P=32;function W(e){for(var t={},n=0,r=e.length;n1&&void 0!==arguments[1]?arguments[1]:{},o=i.location,c=void 0===o?I.location:o,a=i.threshold,s=void 0===a?I.threshold:a,u=i.distance,h=void 0===u?I.distance:u,l=i.includeMatches,f=void 0===l?I.includeMatches:l,d=i.findAllMatches,v=void 0===d?I.findAllMatches:d,g=i.minMatchCharLength,y=void 0===g?I.minMatchCharLength:g,p=i.isCaseSensitive,m=void 0===p?I.isCaseSensitive:p,k=i.ignoreLocation,M=void 0===k?I.ignoreLocation:k;if(r(this,e),this.options={location:c,threshold:s,distance:h,includeMatches:f,findAllMatches:v,minMatchCharLength:y,isCaseSensitive:m,ignoreLocation:M},this.pattern=m?t:t.toLowerCase(),this.chunks=[],this.pattern.length){var b=function(e,t){n.chunks.push({pattern:e,alphabet:W(e),startIndex:t})},x=this.pattern.length;if(x>P){for(var w=0,L=x%P,S=x-L;w3&&void 0!==arguments[3]?arguments[3]:{},i=r.location,o=void 0===i?I.location:i,c=r.distance,a=void 0===c?I.distance:c,s=r.threshold,u=void 0===s?I.threshold:s,h=r.findAllMatches,l=void 0===h?I.findAllMatches:h,f=r.minMatchCharLength,d=void 0===f?I.minMatchCharLength:f,v=r.includeMatches,g=void 0===v?I.includeMatches:v,y=r.ignoreLocation,p=void 0===y?I.ignoreLocation:y;if(t.length>P)throw new Error(w(P));for(var m,k=t.length,M=e.length,b=Math.max(0,Math.min(o,M)),x=u,L=b,S=d>1||g,_=S?Array(M):[];(m=e.indexOf(t,L))>-1;){var O=R(t,{currentLocation:m,expectedLocation:b,distance:a,ignoreLocation:p});if(x=Math.min(O,x),L=m+k,S)for(var j=0;j=z;q-=1){var B=q-1,J=n[e.charAt(B)];if(S&&(_[B]=+!!J),K[q]=(K[q+1]<<1|1)&J,F&&(K[q]|=(A[q+1]|A[q])<<1|1|A[q+1]),K[q]&$&&(C=R(t,{errors:F,currentLocation:B,expectedLocation:b,distance:a,ignoreLocation:p}))<=x){if(x=C,(L=B)<=b)break;z=Math.max(1,2*b-L)}}if(R(t,{errors:F+1,currentLocation:b,expectedLocation:b,distance:a,ignoreLocation:p})>x)break;A=K}var U={isMatch:L>=0,score:Math.max(.001,C)};if(S){var V=N(_,d);V.length?g&&(U.indices=V):U.isMatch=!1}return U}(e,n,i,{location:c+o,distance:a,threshold:s,findAllMatches:u,minMatchCharLength:h,includeMatches:r,ignoreLocation:l}),p=y.isMatch,m=y.score,k=y.indices;p&&(g=!0),v+=m,p&&k&&(d=[].concat(f(d),f(k)))}));var y={isMatch:g,score:g?v/this.chunks.length:1};return g&&r&&(y.indices=d),y}}]),e}(),z=function(){function e(t){r(this,e),this.pattern=t}return o(e,[{key:"search",value:function(){}}],[{key:"isMultiMatch",value:function(e){return D(e,this.multiRegex)}},{key:"isSingleMatch",value:function(e){return D(e,this.singleRegex)}}]),e}();function D(e,t){var n=e.match(t);return n?n[1]:null}var K=function(e){a(n,e);var t=l(n);function n(e){return r(this,n),t.call(this,e)}return o(n,[{key:"search",value:function(e){var t=e===this.pattern;return{isMatch:t,score:t?0:1,indices:[0,this.pattern.length-1]}}}],[{key:"type",get:function(){return"exact"}},{key:"multiRegex",get:function(){return/^="(.*)"$/}},{key:"singleRegex",get:function(){return/^=(.*)$/}}]),n}(z),q=function(e){a(n,e);var t=l(n);function n(e){return r(this,n),t.call(this,e)}return o(n,[{key:"search",value:function(e){var t=-1===e.indexOf(this.pattern);return{isMatch:t,score:t?0:1,indices:[0,e.length-1]}}}],[{key:"type",get:function(){return"inverse-exact"}},{key:"multiRegex",get:function(){return/^!"(.*)"$/}},{key:"singleRegex",get:function(){return/^!(.*)$/}}]),n}(z),B=function(e){a(n,e);var t=l(n);function n(e){return r(this,n),t.call(this,e)}return o(n,[{key:"search",value:function(e){var t=e.startsWith(this.pattern);return{isMatch:t,score:t?0:1,indices:[0,this.pattern.length-1]}}}],[{key:"type",get:function(){return"prefix-exact"}},{key:"multiRegex",get:function(){return/^\^"(.*)"$/}},{key:"singleRegex",get:function(){return/^\^(.*)$/}}]),n}(z),J=function(e){a(n,e);var t=l(n);function n(e){return r(this,n),t.call(this,e)}return o(n,[{key:"search",value:function(e){var t=!e.startsWith(this.pattern);return{isMatch:t,score:t?0:1,indices:[0,e.length-1]}}}],[{key:"type",get:function(){return"inverse-prefix-exact"}},{key:"multiRegex",get:function(){return/^!\^"(.*)"$/}},{key:"singleRegex",get:function(){return/^!\^(.*)$/}}]),n}(z),U=function(e){a(n,e);var t=l(n);function n(e){return r(this,n),t.call(this,e)}return o(n,[{key:"search",value:function(e){var t=e.endsWith(this.pattern);return{isMatch:t,score:t?0:1,indices:[e.length-this.pattern.length,e.length-1]}}}],[{key:"type",get:function(){return"suffix-exact"}},{key:"multiRegex",get:function(){return/^"(.*)"\$$/}},{key:"singleRegex",get:function(){return/^(.*)\$$/}}]),n}(z),V=function(e){a(n,e);var t=l(n);function n(e){return r(this,n),t.call(this,e)}return o(n,[{key:"search",value:function(e){var t=!e.endsWith(this.pattern);return{isMatch:t,score:t?0:1,indices:[0,e.length-1]}}}],[{key:"type",get:function(){return"inverse-suffix-exact"}},{key:"multiRegex",get:function(){return/^!"(.*)"\$$/}},{key:"singleRegex",get:function(){return/^!(.*)\$$/}}]),n}(z),G=function(e){a(n,e);var t=l(n);function n(e){var i,o=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},c=o.location,a=void 0===c?I.location:c,s=o.threshold,u=void 0===s?I.threshold:s,h=o.distance,l=void 0===h?I.distance:h,f=o.includeMatches,d=void 0===f?I.includeMatches:f,v=o.findAllMatches,g=void 0===v?I.findAllMatches:v,y=o.minMatchCharLength,p=void 0===y?I.minMatchCharLength:y,m=o.isCaseSensitive,k=void 0===m?I.isCaseSensitive:m,M=o.ignoreLocation,b=void 0===M?I.ignoreLocation:M;return r(this,n),(i=t.call(this,e))._bitapSearch=new T(e,{location:a,threshold:u,distance:l,includeMatches:d,findAllMatches:g,minMatchCharLength:p,isCaseSensitive:k,ignoreLocation:b}),i}return o(n,[{key:"search",value:function(e){return this._bitapSearch.searchIn(e)}}],[{key:"type",get:function(){return"fuzzy"}},{key:"multiRegex",get:function(){return/^"(.*)"$/}},{key:"singleRegex",get:function(){return/^(.*)$/}}]),n}(z),H=function(e){a(n,e);var t=l(n);function n(e){return r(this,n),t.call(this,e)}return o(n,[{key:"search",value:function(e){for(var t,n=0,r=[],i=this.pattern.length;(t=e.indexOf(this.pattern,n))>-1;)n=t+i,r.push([t,n-1]);var o=!!r.length;return{isMatch:o,score:o?0:1,indices:r}}}],[{key:"type",get:function(){return"include"}},{key:"multiRegex",get:function(){return/^'"(.*)"$/}},{key:"singleRegex",get:function(){return/^'(.*)$/}}]),n}(z),Q=[K,H,B,J,V,U,q,G],X=Q.length,Y=/ +(?=(?:[^\"]*\"[^\"]*\")*[^\"]*$)/;function Z(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return e.split("|").map((function(e){for(var n=e.trim().split(Y).filter((function(e){return e&&!!e.trim()})),r=[],i=0,o=n.length;i1&&void 0!==arguments[1]?arguments[1]:{},i=n.isCaseSensitive,o=void 0===i?I.isCaseSensitive:i,c=n.includeMatches,a=void 0===c?I.includeMatches:c,s=n.minMatchCharLength,u=void 0===s?I.minMatchCharLength:s,h=n.ignoreLocation,l=void 0===h?I.ignoreLocation:h,f=n.findAllMatches,d=void 0===f?I.findAllMatches:f,v=n.location,g=void 0===v?I.location:v,y=n.threshold,p=void 0===y?I.threshold:y,m=n.distance,k=void 0===m?I.distance:m;r(this,e),this.query=null,this.options={isCaseSensitive:o,includeMatches:a,minMatchCharLength:u,findAllMatches:d,ignoreLocation:l,location:g,threshold:p,distance:k},this.pattern=o?t:t.toLowerCase(),this.query=Z(this.pattern,this.options)}return o(e,[{key:"searchIn",value:function(e){var t=this.query;if(!t)return{isMatch:!1,score:1};var n=this.options,r=n.includeMatches;e=n.isCaseSensitive?e:e.toLowerCase();for(var i=0,o=[],c=0,a=0,s=t.length;a-1&&(n.refIndex=e.idx),t.matches.push(n)}}))}function ve(e,t){t.score=e.score}function ge(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{},r=n.includeMatches,i=void 0===r?I.includeMatches:r,o=n.includeScore,c=void 0===o?I.includeScore:o,a=[];return i&&a.push(de),c&&a.push(ve),e.map((function(e){var n=e.idx,r={item:t[n],refIndex:n};return a.length&&a.forEach((function(t){t(e,r)})),r}))}var ye=function(){function e(n){var i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},o=arguments.length>2?arguments[2]:void 0;r(this,e),this.options=t(t({},I),i),this.options.useExtendedSearch,this._keyStore=new S(this.options.keys),this.setCollection(n,o)}return o(e,[{key:"setCollection",value:function(e,t){if(this._docs=e,t&&!(t instanceof $))throw new Error("Incorrect 'index' type");this._myIndex=t||F(this.options.keys,this._docs,{getFn:this.options.getFn,fieldNormWeight:this.options.fieldNormWeight})}},{key:"add",value:function(e){k(e)&&(this._docs.push(e),this._myIndex.add(e))}},{key:"remove",value:function(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:function(){return!1},t=[],n=0,r=this._docs.length;n1&&void 0!==arguments[1]?arguments[1]:{},n=t.limit,r=void 0===n?-1:n,i=this.options,o=i.includeMatches,c=i.includeScore,a=i.shouldSort,s=i.sortFn,u=i.ignoreFieldNorm,h=g(e)?g(this._docs[0])?this._searchStringList(e):this._searchObjectList(e):this._searchLogical(e);return fe(h,{ignoreFieldNorm:u}),a&&h.sort(s),y(r)&&r>-1&&(h=h.slice(0,r)),ge(h,this._docs,{includeMatches:o,includeScore:c})}},{key:"_searchStringList",value:function(e){var t=re(e,this.options),n=this._myIndex.records,r=[];return n.forEach((function(e){var n=e.v,i=e.i,o=e.n;if(k(n)){var c=t.searchIn(n),a=c.isMatch,s=c.score,u=c.indices;a&&r.push({item:n,idx:i,matches:[{score:s,value:n,norm:o,indices:u}]})}})),r}},{key:"_searchLogical",value:function(e){var t=this,n=function(e,t){var n=(arguments.length>2&&void 0!==arguments[2]?arguments[2]:{}).auto,r=void 0===n||n,i=function e(n){var i=Object.keys(n),o=ue(n);if(!o&&i.length>1&&!se(n))return e(le(n));if(he(n)){var c=o?n[ce]:i[0],a=o?n[ae]:n[c];if(!g(a))throw new Error(x(c));var s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t();
\ No newline at end of file
diff --git a/site_libs/quarto-search/quarto-search.js b/site_libs/quarto-search/quarto-search.js
new file mode 100644
index 0000000..5f723d7
--- /dev/null
+++ b/site_libs/quarto-search/quarto-search.js
@@ -0,0 +1,1286 @@
+const kQueryArg = "q";
+const kResultsArg = "show-results";
+
+// If items don't provide a URL, then both the navigator and the onSelect
+// function aren't called (and therefore, the default implementation is used)
+//
+// We're using this sentinel URL to signal to those handlers that this
+// item is a more item (along with the type) and can be handled appropriately
+const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05";
+
+window.document.addEventListener("DOMContentLoaded", function (_event) {
+  // Ensure that search is available on this page. If it isn't,
+  // should return early and not do anything
+  var searchEl = window.document.getElementById("quarto-search");
+  if (!searchEl) return;
+
+  const { autocomplete } = window["@algolia/autocomplete-js"];
+
+  let quartoSearchOptions = {};
+  let language = {};
+  const searchOptionEl = window.document.getElementById(
+    "quarto-search-options"
+  );
+  if (searchOptionEl) {
+    const jsonStr = searchOptionEl.textContent;
+    quartoSearchOptions = JSON.parse(jsonStr);
+    language = quartoSearchOptions.language;
+  }
+
+  // note the search mode
+  if (quartoSearchOptions.type === "overlay") {
+    searchEl.classList.add("type-overlay");
+  } else {
+    searchEl.classList.add("type-textbox");
+  }
+
+  // Used to determine highlighting behavior for this page
+  // A `q` query param is expected when the user follows a search
+  // to this page
+  const currentUrl = new URL(window.location);
+  const query = currentUrl.searchParams.get(kQueryArg);
+  const showSearchResults = currentUrl.searchParams.get(kResultsArg);
+  const mainEl = window.document.querySelector("main");
+
+  // highlight matches on the page
+  if (query && mainEl) {
+    // perform any highlighting
+    highlight(escapeRegExp(query), mainEl);
+
+    // fix up the URL to remove the q query param
+    const replacementUrl = new URL(window.location);
+    replacementUrl.searchParams.delete(kQueryArg);
+    window.history.replaceState({}, "", replacementUrl);
+  }
+
+  // function to clear highlighting on the page when the search query changes
+  // (e.g. if the user edits the query or clears it)
+  let highlighting = true;
+  const resetHighlighting = (searchTerm) => {
+    if (mainEl && highlighting && query && searchTerm !== query) {
+      clearHighlight(query, mainEl);
+      highlighting = false;
+    }
+  };
+
+  // Clear search highlighting when the user scrolls sufficiently
+  const resetFn = () => {
+    resetHighlighting("");
+    window.removeEventListener("quarto-hrChanged", resetFn);
+    window.removeEventListener("quarto-sectionChanged", resetFn);
+  };
+
+  // Register this event after the initial scrolling and settling of events
+  // on the page
+  window.addEventListener("quarto-hrChanged", resetFn);
+  window.addEventListener("quarto-sectionChanged", resetFn);
+
+  // Responsively switch to overlay mode if the search is present on the navbar
+  // Note that switching the sidebar to overlay mode requires more coordinate (not just
+  // the media query since we generate different HTML for sidebar overlays than we do
+  // for sidebar input UI)
+  const detachedMediaQuery =
+    quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)";
+
+  // If configured, include the analytics client to send insights
+  const plugins = configurePlugins(quartoSearchOptions);
+
+  let lastState = null;
+  const { setIsOpen, setQuery, setCollections } = autocomplete({
+    container: searchEl,
+    detachedMediaQuery: detachedMediaQuery,
+    defaultActiveItemId: 0,
+    panelContainer: "#quarto-search-results",
+    panelPlacement: quartoSearchOptions["panel-placement"],
+    debug: false,
+    openOnFocus: true,
+    plugins,
+    classNames: {
+      form: "d-flex",
+    },
+    placeholder: language["search-text-placeholder"],
+    translations: {
+      clearButtonTitle: language["search-clear-button-title"],
+      detachedCancelButtonText: language["search-detached-cancel-button-title"],
+      submitButtonTitle: language["search-submit-button-title"],
+    },
+    initialState: {
+      query,
+    },
+    getItemUrl({ item }) {
+      return item.href;
+    },
+    onStateChange({ state }) {
+      // If this is a file URL, note that
+
+      // Perhaps reset highlighting
+      resetHighlighting(state.query);
+
+      // If the panel just opened, ensure the panel is positioned properly
+      if (state.isOpen) {
+        if (lastState && !lastState.isOpen) {
+          setTimeout(() => {
+            positionPanel(quartoSearchOptions["panel-placement"]);
+          }, 150);
+        }
+      }
+
+      // Perhaps show the copy link
+      showCopyLink(state.query, quartoSearchOptions);
+
+      lastState = state;
+    },
+    reshape({ sources, state }) {
+      return sources.map((source) => {
+        try {
+          const items = source.getItems();
+
+          // Validate the items
+          validateItems(items);
+
+          // group the items by document
+          const groupedItems = new Map();
+          items.forEach((item) => {
+            const hrefParts = item.href.split("#");
+            const baseHref = hrefParts[0];
+            const isDocumentItem = hrefParts.length === 1;
+
+            const items = groupedItems.get(baseHref);
+            if (!items) {
+              groupedItems.set(baseHref, [item]);
+            } else {
+              // If the href for this item matches the document
+              // exactly, place this item first as it is the item that represents
+              // the document itself
+              if (isDocumentItem) {
+                items.unshift(item);
+              } else {
+                items.push(item);
+              }
+              groupedItems.set(baseHref, items);
+            }
+          });
+
+          const reshapedItems = [];
+          let count = 1;
+          for (const [_key, value] of groupedItems) {
+            const firstItem = value[0];
+            reshapedItems.push({
+              ...firstItem,
+              type: kItemTypeDoc,
+            });
+
+            const collapseMatches = quartoSearchOptions["collapse-after"];
+            const collapseCount =
+              typeof collapseMatches === "number" ? collapseMatches : 1;
+
+            if (value.length > 1) {
+              const target = `search-more-${count}`;
+              const isExpanded =
+                state.context.expanded &&
+                state.context.expanded.includes(target);
+
+              const remainingCount = value.length - collapseCount;
+
+              for (let i = 1; i < value.length; i++) {
+                if (collapseMatches && i === collapseCount) {
+                  reshapedItems.push({
+                    target,
+                    title: isExpanded
+                      ? language["search-hide-matches-text"]
+                      : remainingCount === 1
+                      ? `${remainingCount} ${language["search-more-match-text"]}`
+                      : `${remainingCount} ${language["search-more-matches-text"]}`,
+                    type: kItemTypeMore,
+                    href: kItemTypeMoreHref,
+                  });
+                }
+
+                if (isExpanded || !collapseMatches || i < collapseCount) {
+                  reshapedItems.push({
+                    ...value[i],
+                    type: kItemTypeItem,
+                    target,
+                  });
+                }
+              }
+            }
+            count += 1;
+          }
+
+          return {
+            ...source,
+            getItems() {
+              return reshapedItems;
+            },
+          };
+        } catch (error) {
+          // Some form of error occurred
+          return {
+            ...source,
+            getItems() {
+              return [
+                {
+                  title: error.name || "An Error Occurred While Searching",
+                  text:
+                    error.message ||
+                    "An unknown error occurred while attempting to perform the requested search.",
+                  type: kItemTypeError,
+                },
+              ];
+            },
+          };
+        }
+      });
+    },
+    navigator: {
+      navigate({ itemUrl }) {
+        if (itemUrl !== offsetURL(kItemTypeMoreHref)) {
+          window.location.assign(itemUrl);
+        }
+      },
+      navigateNewTab({ itemUrl }) {
+        if (itemUrl !== offsetURL(kItemTypeMoreHref)) {
+          const windowReference = window.open(itemUrl, "_blank", "noopener");
+          if (windowReference) {
+            windowReference.focus();
+          }
+        }
+      },
+      navigateNewWindow({ itemUrl }) {
+        if (itemUrl !== offsetURL(kItemTypeMoreHref)) {
+          window.open(itemUrl, "_blank", "noopener");
+        }
+      },
+    },
+    getSources({ state, setContext, setActiveItemId, refresh }) {
+      return [
+        {
+          sourceId: "documents",
+          getItemUrl({ item }) {
+            if (item.href) {
+              return offsetURL(item.href);
+            } else {
+              return undefined;
+            }
+          },
+          onSelect({
+            item,
+            state,
+            setContext,
+            setIsOpen,
+            setActiveItemId,
+            refresh,
+          }) {
+            if (item.type === kItemTypeMore) {
+              toggleExpanded(item, state, setContext, setActiveItemId, refresh);
+
+              // Toggle more
+              setIsOpen(true);
+            }
+          },
+          getItems({ query }) {
+            if (query === null || query === "") {
+              return [];
+            }
+
+            const limit = quartoSearchOptions.limit;
+            if (quartoSearchOptions.algolia) {
+              return algoliaSearch(query, limit, quartoSearchOptions.algolia);
+            } else {
+              // Fuse search options
+              const fuseSearchOptions = {
+                isCaseSensitive: false,
+                shouldSort: true,
+                minMatchCharLength: 2,
+                limit: limit,
+              };
+
+              return readSearchData().then(function (fuse) {
+                return fuseSearch(query, fuse, fuseSearchOptions);
+              });
+            }
+          },
+          templates: {
+            noResults({ createElement }) {
+              const hasQuery = lastState.query;
+
+              return createElement(
+                "div",
+                {
+                  class: `quarto-search-no-results${
+                    hasQuery ? "" : " no-query"
+                  }`,
+                },
+                language["search-no-results-text"]
+              );
+            },
+            header({ items, createElement }) {
+              // count the documents
+              const count = items.filter((item) => {
+                return item.type === kItemTypeDoc;
+              }).length;
+
+              if (count > 0) {
+                return createElement(
+                  "div",
+                  { class: "search-result-header" },
+                  `${count} ${language["search-matching-documents-text"]}`
+                );
+              } else {
+                return createElement(
+                  "div",
+                  { class: "search-result-header-no-results" },
+                  ``
+                );
+              }
+            },
+            footer({ _items, createElement }) {
+              if (
+                quartoSearchOptions.algolia &&
+                quartoSearchOptions.algolia["show-logo"]
+              ) {
+                const libDir = quartoSearchOptions.algolia["libDir"];
+                const logo = createElement("img", {
+                  src: offsetURL(
+                    `${libDir}/quarto-search/search-by-algolia.svg`
+                  ),
+                  class: "algolia-search-logo",
+                });
+                return createElement(
+                  "a",
+                  { href: "http://www.algolia.com/" },
+                  logo
+                );
+              }
+            },
+
+            item({ item, createElement }) {
+              return renderItem(
+                item,
+                createElement,
+                state,
+                setActiveItemId,
+                setContext,
+                refresh,
+                quartoSearchOptions
+              );
+            },
+          },
+        },
+      ];
+    },
+  });
+
+  window.quartoOpenSearch = () => {
+    setIsOpen(false);
+    setIsOpen(true);
+    focusSearchInput();
+  };
+
+  document.addEventListener("keyup", (event) => {
+    const { key } = event;
+    const kbds = quartoSearchOptions["keyboard-shortcut"];
+    const focusedEl = document.activeElement;
+
+    const isFormElFocused = [
+      "input",
+      "select",
+      "textarea",
+      "button",
+      "option",
+    ].find((tag) => {
+      return focusedEl.tagName.toLowerCase() === tag;
+    });
+
+    if (
+      kbds &&
+      kbds.includes(key) &&
+      !isFormElFocused &&
+      !document.activeElement.isContentEditable
+    ) {
+      event.preventDefault();
+      window.quartoOpenSearch();
+    }
+  });
+
+  // Remove the labeleledby attribute since it is pointing
+  // to a non-existent label
+  if (quartoSearchOptions.type === "overlay") {
+    const inputEl = window.document.querySelector(
+      "#quarto-search .aa-Autocomplete"
+    );
+    if (inputEl) {
+      inputEl.removeAttribute("aria-labelledby");
+    }
+  }
+
+  function throttle(func, wait) {
+    let waiting = false;
+    return function () {
+      if (!waiting) {
+        func.apply(this, arguments);
+        waiting = true;
+        setTimeout(function () {
+          waiting = false;
+        }, wait);
+      }
+    };
+  }
+
+  // If the main document scrolls dismiss the search results
+  // (otherwise, since they're floating in the document they can scroll with the document)
+  window.document.body.onscroll = throttle(() => {
+    // Only do this if we're not detached
+    // Bug #7117
+    // This will happen when the keyboard is shown on ios (resulting in a scroll)
+    // which then closed the search UI
+    if (!window.matchMedia(detachedMediaQuery).matches) {
+      setIsOpen(false);
+    }
+  }, 50);
+
+  if (showSearchResults) {
+    setIsOpen(true);
+    focusSearchInput();
+  }
+});
+
+function configurePlugins(quartoSearchOptions) {
+  const autocompletePlugins = [];
+  const algoliaOptions = quartoSearchOptions.algolia;
+  if (
+    algoliaOptions &&
+    algoliaOptions["analytics-events"] &&
+    algoliaOptions["search-only-api-key"] &&
+    algoliaOptions["application-id"]
+  ) {
+    const apiKey = algoliaOptions["search-only-api-key"];
+    const appId = algoliaOptions["application-id"];
+
+    // Aloglia insights may not be loaded because they require cookie consent
+    // Use deferred loading so events will start being recorded when/if consent
+    // is granted.
+    const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => {
+      if (
+        window.aa &&
+        window["@algolia/autocomplete-plugin-algolia-insights"]
+      ) {
+        window.aa("init", {
+          appId,
+          apiKey,
+          useCookie: true,
+        });
+
+        const { createAlgoliaInsightsPlugin } =
+          window["@algolia/autocomplete-plugin-algolia-insights"];
+        // Register the insights client
+        const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({
+          insightsClient: window.aa,
+          onItemsChange({ insights, insightsEvents }) {
+            const events = insightsEvents.flatMap((event) => {
+              // This API limits the number of items per event to 20
+              const chunkSize = 20;
+              const itemChunks = [];
+              const eventItems = event.items;
+              for (let i = 0; i < eventItems.length; i += chunkSize) {
+                itemChunks.push(eventItems.slice(i, i + chunkSize));
+              }
+              // Split the items into multiple events that can be sent
+              const events = itemChunks.map((items) => {
+                return {
+                  ...event,
+                  items,
+                };
+              });
+              return events;
+            });
+
+            for (const event of events) {
+              insights.viewedObjectIDs(event);
+            }
+          },
+        });
+        return algoliaInsightsPlugin;
+      }
+    });
+
+    // Add the plugin
+    autocompletePlugins.push(algoliaInsightsDeferredPlugin);
+    return autocompletePlugins;
+  }
+}
+
+// For plugins that may not load immediately, create a wrapper
+// plugin and forward events and plugin data once the plugin
+// is initialized. This is useful for cases like cookie consent
+// which may prevent the analytics insights event plugin from initializing
+// immediately.
+function deferredLoadPlugin(createPlugin) {
+  let plugin = undefined;
+  let subscribeObj = undefined;
+  const wrappedPlugin = () => {
+    if (!plugin && subscribeObj) {
+      plugin = createPlugin();
+      if (plugin && plugin.subscribe) {
+        plugin.subscribe(subscribeObj);
+      }
+    }
+    return plugin;
+  };
+
+  return {
+    subscribe: (obj) => {
+      subscribeObj = obj;
+    },
+    onStateChange: (obj) => {
+      const plugin = wrappedPlugin();
+      if (plugin && plugin.onStateChange) {
+        plugin.onStateChange(obj);
+      }
+    },
+    onSubmit: (obj) => {
+      const plugin = wrappedPlugin();
+      if (plugin && plugin.onSubmit) {
+        plugin.onSubmit(obj);
+      }
+    },
+    onReset: (obj) => {
+      const plugin = wrappedPlugin();
+      if (plugin && plugin.onReset) {
+        plugin.onReset(obj);
+      }
+    },
+    getSources: (obj) => {
+      const plugin = wrappedPlugin();
+      if (plugin && plugin.getSources) {
+        return plugin.getSources(obj);
+      } else {
+        return Promise.resolve([]);
+      }
+    },
+    data: (obj) => {
+      const plugin = wrappedPlugin();
+      if (plugin && plugin.data) {
+        plugin.data(obj);
+      }
+    },
+  };
+}
+
+function validateItems(items) {
+  // Validate the first item
+  if (items.length > 0) {
+    const item = items[0];
+    const missingFields = [];
+    if (item.href == undefined) {
+      missingFields.push("href");
+    }
+    if (!item.title == undefined) {
+      missingFields.push("title");
+    }
+    if (!item.text == undefined) {
+      missingFields.push("text");
+    }
+
+    if (missingFields.length === 1) {
+      throw {
+        name: `Error: Search index is missing the ${missingFields[0]} field.`,
+        message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`,
+      };
+    } else if (missingFields.length > 1) {
+      const missingFieldList = missingFields
+        .map((field) => {
+          return `${field}`;
+        })
+        .join(", ");
+
+      throw {
+        name: `Error: Search index is missing the following fields: ${missingFieldList}.`,
+        message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`,
+      };
+    }
+  }
+}
+
+let lastQuery = null;
+function showCopyLink(query, options) {
+  const language = options.language;
+  lastQuery = query;
+  // Insert share icon
+  const inputSuffixEl = window.document.body.querySelector(
+    ".aa-Form .aa-InputWrapperSuffix"
+  );
+
+  if (inputSuffixEl) {
+    let copyButtonEl = window.document.body.querySelector(
+      ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton"
+    );
+
+    if (copyButtonEl === null) {
+      copyButtonEl = window.document.createElement("button");
+      copyButtonEl.setAttribute("class", "aa-CopyButton");
+      copyButtonEl.setAttribute("type", "button");
+      copyButtonEl.setAttribute("title", language["search-copy-link-title"]);
+      copyButtonEl.onmousedown = (e) => {
+        e.preventDefault();
+        e.stopPropagation();
+      };
+
+      const linkIcon = "bi-clipboard";
+      const checkIcon = "bi-check2";
+
+      const shareIconEl = window.document.createElement("i");
+      shareIconEl.setAttribute("class", `bi ${linkIcon}`);
+      copyButtonEl.appendChild(shareIconEl);
+      inputSuffixEl.prepend(copyButtonEl);
+
+      const clipboard = new window.ClipboardJS(".aa-CopyButton", {
+        text: function (_trigger) {
+          const copyUrl = new URL(window.location);
+          copyUrl.searchParams.set(kQueryArg, lastQuery);
+          copyUrl.searchParams.set(kResultsArg, "1");
+          return copyUrl.toString();
+        },
+      });
+      clipboard.on("success", function (e) {
+        // Focus the input
+
+        // button target
+        const button = e.trigger;
+        const icon = button.querySelector("i.bi");
+
+        // flash "checked"
+        icon.classList.add(checkIcon);
+        icon.classList.remove(linkIcon);
+        setTimeout(function () {
+          icon.classList.remove(checkIcon);
+          icon.classList.add(linkIcon);
+        }, 1000);
+      });
+    }
+
+    // If there is a query, show the link icon
+    if (copyButtonEl) {
+      if (lastQuery && options["copy-button"]) {
+        copyButtonEl.style.display = "flex";
+      } else {
+        copyButtonEl.style.display = "none";
+      }
+    }
+  }
+}
+
+/* Search Index Handling */
+// create the index
+var fuseIndex = undefined;
+var shownWarning = false;
+
+// fuse index options
+const kFuseIndexOptions = {
+  keys: [
+    { name: "title", weight: 20 },
+    { name: "section", weight: 20 },
+    { name: "text", weight: 10 },
+  ],
+  ignoreLocation: true,
+  threshold: 0.1,
+};
+
+async function readSearchData() {
+  // Initialize the search index on demand
+  if (fuseIndex === undefined) {
+    if (window.location.protocol === "file:" && !shownWarning) {
+      window.alert(
+        "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server."
+      );
+      shownWarning = true;
+      return;
+    }
+    const fuse = new window.Fuse([], kFuseIndexOptions);
+
+    // fetch the main search.json
+    const response = await fetch(offsetURL("search.json"));
+    if (response.status == 200) {
+      return response.json().then(function (searchDocs) {
+        searchDocs.forEach(function (searchDoc) {
+          fuse.add(searchDoc);
+        });
+        fuseIndex = fuse;
+        return fuseIndex;
+      });
+    } else {
+      return Promise.reject(
+        new Error(
+          "Unexpected status from search index request: " + response.status
+        )
+      );
+    }
+  }
+
+  return fuseIndex;
+}
+
+function inputElement() {
+  return window.document.body.querySelector(".aa-Form .aa-Input");
+}
+
+function focusSearchInput() {
+  setTimeout(() => {
+    const inputEl = inputElement();
+    if (inputEl) {
+      inputEl.focus();
+    }
+  }, 50);
+}
+
+/* Panels */
+const kItemTypeDoc = "document";
+const kItemTypeMore = "document-more";
+const kItemTypeItem = "document-item";
+const kItemTypeError = "error";
+
+function renderItem(
+  item,
+  createElement,
+  state,
+  setActiveItemId,
+  setContext,
+  refresh,
+  quartoSearchOptions
+) {
+  switch (item.type) {
+    case kItemTypeDoc:
+      return createDocumentCard(
+        createElement,
+        "file-richtext",
+        item.title,
+        item.section,
+        item.text,
+        item.href,
+        item.crumbs,
+        quartoSearchOptions
+      );
+    case kItemTypeMore:
+      return createMoreCard(
+        createElement,
+        item,
+        state,
+        setActiveItemId,
+        setContext,
+        refresh
+      );
+    case kItemTypeItem:
+      return createSectionCard(
+        createElement,
+        item.section,
+        item.text,
+        item.href
+      );
+    case kItemTypeError:
+      return createErrorCard(createElement, item.title, item.text);
+    default:
+      return undefined;
+  }
+}
+
+function createDocumentCard(
+  createElement,
+  icon,
+  title,
+  section,
+  text,
+  href,
+  crumbs,
+  quartoSearchOptions
+) {
+  const iconEl = createElement("i", {
+    class: `bi bi-${icon} search-result-icon`,
+  });
+  const titleEl = createElement("p", { class: "search-result-title" }, title);
+  const titleContents = [iconEl, titleEl];
+  const showParent = quartoSearchOptions["show-item-context"];
+  if (crumbs && showParent) {
+    let crumbsOut = undefined;
+    const crumbClz = ["search-result-crumbs"];
+    if (showParent === "root") {
+      crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined;
+    } else if (showParent === "parent") {
+      crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined;
+    } else {
+      crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined;
+      crumbClz.push("search-result-crumbs-wrap");
+    }
+
+    const crumbEl = createElement(
+      "p",
+      { class: crumbClz.join(" ") },
+      crumbsOut
+    );
+    titleContents.push(crumbEl);
+  }
+
+  const titleContainerEl = createElement(
+    "div",
+    { class: "search-result-title-container" },
+    titleContents
+  );
+
+  const textEls = [];
+  if (section) {
+    const sectionEl = createElement(
+      "p",
+      { class: "search-result-section" },
+      section
+    );
+    textEls.push(sectionEl);
+  }
+  const descEl = createElement("p", {
+    class: "search-result-text",
+    dangerouslySetInnerHTML: {
+      __html: text,
+    },
+  });
+  textEls.push(descEl);
+
+  const textContainerEl = createElement(
+    "div",
+    { class: "search-result-text-container" },
+    textEls
+  );
+
+  const containerEl = createElement(
+    "div",
+    {
+      class: "search-result-container",
+    },
+    [titleContainerEl, textContainerEl]
+  );
+
+  const linkEl = createElement(
+    "a",
+    {
+      href: offsetURL(href),
+      class: "search-result-link",
+    },
+    containerEl
+  );
+
+  const classes = ["search-result-doc", "search-item"];
+  if (!section) {
+    classes.push("document-selectable");
+  }
+
+  return createElement(
+    "div",
+    {
+      class: classes.join(" "),
+    },
+    linkEl
+  );
+}
+
+function createMoreCard(
+  createElement,
+  item,
+  state,
+  setActiveItemId,
+  setContext,
+  refresh
+) {
+  const moreCardEl = createElement(
+    "div",
+    {
+      class: "search-result-more search-item",
+      onClick: (e) => {
+        // Handle expanding the sections by adding the expanded
+        // section to the list of expanded sections
+        toggleExpanded(item, state, setContext, setActiveItemId, refresh);
+        e.stopPropagation();
+      },
+    },
+    item.title
+  );
+
+  return moreCardEl;
+}
+
+function toggleExpanded(item, state, setContext, setActiveItemId, refresh) {
+  const expanded = state.context.expanded || [];
+  if (expanded.includes(item.target)) {
+    setContext({
+      expanded: expanded.filter((target) => target !== item.target),
+    });
+  } else {
+    setContext({ expanded: [...expanded, item.target] });
+  }
+
+  refresh();
+  setActiveItemId(item.__autocomplete_id);
+}
+
+function createSectionCard(createElement, section, text, href) {
+  const sectionEl = createSection(createElement, section, text, href);
+  return createElement(
+    "div",
+    {
+      class: "search-result-doc-section search-item",
+    },
+    sectionEl
+  );
+}
+
+function createSection(createElement, title, text, href) {
+  const descEl = createElement("p", {
+    class: "search-result-text",
+    dangerouslySetInnerHTML: {
+      __html: text,
+    },
+  });
+
+  const titleEl = createElement("p", { class: "search-result-section" }, title);
+  const linkEl = createElement(
+    "a",
+    {
+      href: offsetURL(href),
+      class: "search-result-link",
+    },
+    [titleEl, descEl]
+  );
+  return linkEl;
+}
+
+function createErrorCard(createElement, title, text) {
+  const descEl = createElement("p", {
+    class: "search-error-text",
+    dangerouslySetInnerHTML: {
+      __html: text,
+    },
+  });
+
+  const titleEl = createElement("p", {
+    class: "search-error-title",
+    dangerouslySetInnerHTML: {
+      __html: ` ${title}`,
+    },
+  });
+  const errorEl = createElement("div", { class: "search-error" }, [
+    titleEl,
+    descEl,
+  ]);
+  return errorEl;
+}
+
+function positionPanel(pos) {
+  const panelEl = window.document.querySelector(
+    "#quarto-search-results .aa-Panel"
+  );
+  const inputEl = window.document.querySelector(
+    "#quarto-search .aa-Autocomplete"
+  );
+
+  if (panelEl && inputEl) {
+    panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`;
+    if (pos === "start") {
+      panelEl.style.left = `${Math.round(inputEl.left)}px`;
+    } else {
+      panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`;
+    }
+  }
+}
+
+/* Highlighting */
+// highlighting functions
+function highlightMatch(query, text) {
+  if (text) {
+    const start = text.toLowerCase().indexOf(query.toLowerCase());
+    if (start !== -1) {
+      const startMark = "";
+      const endMark = "";
+
+      const end = start + query.length;
+      text =
+        text.slice(0, start) +
+        startMark +
+        text.slice(start, end) +
+        endMark +
+        text.slice(end);
+      const startInfo = clipStart(text, start);
+      const endInfo = clipEnd(
+        text,
+        startInfo.position + startMark.length + endMark.length
+      );
+      text =
+        startInfo.prefix +
+        text.slice(startInfo.position, endInfo.position) +
+        endInfo.suffix;
+
+      return text;
+    } else {
+      return text;
+    }
+  } else {
+    return text;
+  }
+}
+
+function clipStart(text, pos) {
+  const clipStart = pos - 50;
+  if (clipStart < 0) {
+    // This will just return the start of the string
+    return {
+      position: 0,
+      prefix: "",
+    };
+  } else {
+    // We're clipping before the start of the string, walk backwards to the first space.
+    const spacePos = findSpace(text, pos, -1);
+    return {
+      position: spacePos.position,
+      prefix: "",
+    };
+  }
+}
+
+function clipEnd(text, pos) {
+  const clipEnd = pos + 200;
+  if (clipEnd > text.length) {
+    return {
+      position: text.length,
+      suffix: "",
+    };
+  } else {
+    const spacePos = findSpace(text, clipEnd, 1);
+    return {
+      position: spacePos.position,
+      suffix: spacePos.clipped ? "…" : "",
+    };
+  }
+}
+
+function findSpace(text, start, step) {
+  let stepPos = start;
+  while (stepPos > -1 && stepPos < text.length) {
+    const char = text[stepPos];
+    if (char === " " || char === "," || char === ":") {
+      return {
+        position: step === 1 ? stepPos : stepPos - step,
+        clipped: stepPos > 1 && stepPos < text.length,
+      };
+    }
+    stepPos = stepPos + step;
+  }
+
+  return {
+    position: stepPos - step,
+    clipped: false,
+  };
+}
+
+// removes highlighting as implemented by the mark tag
+function clearHighlight(searchterm, el) {
+  const childNodes = el.childNodes;
+  for (let i = childNodes.length - 1; i >= 0; i--) {
+    const node = childNodes[i];
+    if (node.nodeType === Node.ELEMENT_NODE) {
+      if (
+        node.tagName === "MARK" &&
+        node.innerText.toLowerCase() === searchterm.toLowerCase()
+      ) {
+        el.replaceChild(document.createTextNode(node.innerText), node);
+      } else {
+        clearHighlight(searchterm, node);
+      }
+    }
+  }
+}
+
+function escapeRegExp(string) {
+  return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+}
+
+// highlight matches
+function highlight(term, el) {
+  const termRegex = new RegExp(term, "ig");
+  const childNodes = el.childNodes;
+
+  // walk back to front avoid mutating elements in front of us
+  for (let i = childNodes.length - 1; i >= 0; i--) {
+    const node = childNodes[i];
+
+    if (node.nodeType === Node.TEXT_NODE) {
+      // Search text nodes for text to highlight
+      const text = node.nodeValue;
+
+      let startIndex = 0;
+      let matchIndex = text.search(termRegex);
+      if (matchIndex > -1) {
+        const markFragment = document.createDocumentFragment();
+        while (matchIndex > -1) {
+          const prefix = text.slice(startIndex, matchIndex);
+          markFragment.appendChild(document.createTextNode(prefix));
+
+          const mark = document.createElement("mark");
+          mark.appendChild(
+            document.createTextNode(
+              text.slice(matchIndex, matchIndex + term.length)
+            )
+          );
+          markFragment.appendChild(mark);
+
+          startIndex = matchIndex + term.length;
+          matchIndex = text.slice(startIndex).search(new RegExp(term, "ig"));
+          if (matchIndex > -1) {
+            matchIndex = startIndex + matchIndex;
+          }
+        }
+        if (startIndex < text.length) {
+          markFragment.appendChild(
+            document.createTextNode(text.slice(startIndex, text.length))
+          );
+        }
+
+        el.replaceChild(markFragment, node);
+      }
+    } else if (node.nodeType === Node.ELEMENT_NODE) {
+      // recurse through elements
+      highlight(term, node);
+    }
+  }
+}
+
+/* Link Handling */
+// get the offset from this page for a given site root relative url
+function offsetURL(url) {
+  var offset = getMeta("quarto:offset");
+  return offset ? offset + url : url;
+}
+
+// read a meta tag value
+function getMeta(metaName) {
+  var metas = window.document.getElementsByTagName("meta");
+  for (let i = 0; i < metas.length; i++) {
+    if (metas[i].getAttribute("name") === metaName) {
+      return metas[i].getAttribute("content");
+    }
+  }
+  return "";
+}
+
+function algoliaSearch(query, limit, algoliaOptions) {
+  const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"];
+
+  const applicationId = algoliaOptions["application-id"];
+  const searchOnlyApiKey = algoliaOptions["search-only-api-key"];
+  const indexName = algoliaOptions["index-name"];
+  const indexFields = algoliaOptions["index-fields"];
+  const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey);
+  const searchParams = algoliaOptions["params"];
+  const searchAnalytics = !!algoliaOptions["analytics-events"];
+
+  return getAlgoliaResults({
+    searchClient,
+    queries: [
+      {
+        indexName: indexName,
+        query,
+        params: {
+          hitsPerPage: limit,
+          clickAnalytics: searchAnalytics,
+          ...searchParams,
+        },
+      },
+    ],
+    transformResponse: (response) => {
+      if (!indexFields) {
+        return response.hits.map((hit) => {
+          return hit.map((item) => {
+            return {
+              ...item,
+              text: highlightMatch(query, item.text),
+            };
+          });
+        });
+      } else {
+        const remappedHits = response.hits.map((hit) => {
+          return hit.map((item) => {
+            const newItem = { ...item };
+            ["href", "section", "title", "text", "crumbs"].forEach(
+              (keyName) => {
+                const mappedName = indexFields[keyName];
+                if (
+                  mappedName &&
+                  item[mappedName] !== undefined &&
+                  mappedName !== keyName
+                ) {
+                  newItem[keyName] = item[mappedName];
+                  delete newItem[mappedName];
+                }
+              }
+            );
+            newItem.text = highlightMatch(query, newItem.text);
+            return newItem;
+          });
+        });
+        return remappedHits;
+      }
+    },
+  });
+}
+
+let subSearchTerm = undefined;
+let subSearchFuse = undefined;
+const kFuseMaxWait = 125;
+
+async function fuseSearch(query, fuse, fuseOptions) {
+  let index = fuse;
+  // Fuse.js using the Bitap algorithm for text matching which runs in
+  // O(nm) time (no matter the structure of the text). In our case this
+  // means that long search terms mixed with large index gets very slow
+  //
+  // This injects a subIndex that will be used once the terms get long enough
+  // Usually making this subindex is cheap since there will typically be
+  // a subset of results matching the existing query
+  if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) {
+    // Use the existing subSearchFuse
+    index = subSearchFuse;
+  } else if (subSearchFuse !== undefined) {
+    // The term changed, discard the existing fuse
+    subSearchFuse = undefined;
+    subSearchTerm = undefined;
+  }
+
+  // Search using the active fuse
+  const then = performance.now();
+  const resultsRaw = await index.search(query, fuseOptions);
+  const now = performance.now();
+
+  const results = resultsRaw.map((result) => {
+    const addParam = (url, name, value) => {
+      const anchorParts = url.split("#");
+      const baseUrl = anchorParts[0];
+      const sep = baseUrl.search("\\?") > 0 ? "&" : "?";
+      anchorParts[0] = baseUrl + sep + name + "=" + value;
+      return anchorParts.join("#");
+    };
+
+    return {
+      title: result.item.title,
+      section: result.item.section,
+      href: addParam(result.item.href, kQueryArg, query),
+      text: highlightMatch(query, result.item.text),
+      crumbs: result.item.crumbs,
+    };
+  });
+
+  // If we don't have a subfuse and the query is long enough, go ahead
+  // and create a subfuse to use for subsequent queries
+  if (now - then > kFuseMaxWait && subSearchFuse === undefined) {
+    subSearchTerm = query;
+    subSearchFuse = new window.Fuse([], kFuseIndexOptions);
+    resultsRaw.forEach((rr) => {
+      subSearchFuse.add(rr.item);
+    });
+  }
+  return results;
+}
diff --git a/sitemap.xml b/sitemap.xml
new file mode 100644
index 0000000..ddea52b
--- /dev/null
+++ b/sitemap.xml
@@ -0,0 +1,131 @@
+
+
+  
+    https://geowrangler.thinkingmachin.es/datasets_nightlights.html
+    2024-05-23T09:08:51.202Z
+  
+  
+    https://geowrangler.thinkingmachin.es/raster_zonal_stats.html
+    2024-05-23T09:08:50.962Z
+  
+  
+    https://geowrangler.thinkingmachin.es/readme.html
+    2024-05-23T09:08:50.914Z
+  
+  
+    https://geowrangler.thinkingmachin.es/overview.poverty_mapping_demo.html
+    2024-05-23T09:08:50.806Z
+  
+  
+    https://geowrangler.thinkingmachin.es/vector_to_raster_mask.html
+    2024-05-23T09:08:50.758Z
+  
+  
+    https://geowrangler.thinkingmachin.es/distance_zonal_stats.html
+    2024-05-23T09:08:50.866Z
+  
+  
+    https://geowrangler.thinkingmachin.es/spatialjoin_highest_intersection.html
+    2024-05-23T09:08:50.702Z
+  
+  
+    https://geowrangler.thinkingmachin.es/vector_zonal_stats.html
+    2024-05-23T09:08:50.638Z
+  
+  
+    https://geowrangler.thinkingmachin.es/tutorial.dhs.html
+    2024-05-23T09:08:50.486Z
+  
+  
+    https://geowrangler.thinkingmachin.es/tutorial.datasets.html
+    2024-05-23T09:08:50.450Z
+  
+  
+    https://geowrangler.thinkingmachin.es/dhs.html
+    2024-05-23T09:08:50.882Z
+  
+  
+    https://geowrangler.thinkingmachin.es/datasets_utils.html
+    2024-05-23T09:08:50.138Z
+  
+  
+    https://geowrangler.thinkingmachin.es/tutorial.grids.html
+    2024-05-23T09:08:50.006Z
+  
+  
+    https://geowrangler.thinkingmachin.es/tutorial.raster_process.html
+    2024-05-23T09:08:49.886Z
+  
+  
+    https://geowrangler.thinkingmachin.es/raster_to_dataframe.html
+    2024-05-23T09:08:50.446Z
+  
+  
+    https://geowrangler.thinkingmachin.es/grids.html
+    2024-05-23T09:08:49.450Z
+  
+  
+    https://geowrangler.thinkingmachin.es/area_zonal_stats.html
+    2024-05-23T09:08:50.122Z
+  
+  
+    https://geowrangler.thinkingmachin.es/datasets_ookla.html
+    2024-05-23T09:08:49.810Z
+  
+  
+    https://geowrangler.thinkingmachin.es/raster_process.html
+    2024-05-23T09:08:50.426Z
+  
+  
+    https://geowrangler.thinkingmachin.es/tutorial.spatialjoin_highest_intersection.html
+    2024-05-23T09:08:49.938Z
+  
+  
+    https://geowrangler.thinkingmachin.es/overview.usecase_ookla.html
+    2024-05-23T09:08:50.082Z
+  
+  
+    https://geowrangler.thinkingmachin.es/validation.html
+    2024-05-23T09:08:50.322Z
+  
+  
+    https://geowrangler.thinkingmachin.es/tutorial.vector_zonal_stats.html
+    2024-05-23T09:08:50.402Z
+  
+  
+    https://geowrangler.thinkingmachin.es/tutorial.tile_clustering.html
+    2024-05-23T09:08:50.478Z
+  
+  
+    https://geowrangler.thinkingmachin.es/tile_clustering.html
+    2024-05-23T09:08:50.530Z
+  
+  
+    https://geowrangler.thinkingmachin.es/tutorial.distance_zonal_stats.html
+    2024-05-23T09:08:50.538Z
+  
+  
+    https://geowrangler.thinkingmachin.es/tutorial.area_zonal_stats.html
+    2024-05-23T09:08:50.594Z
+  
+  
+    https://geowrangler.thinkingmachin.es/tutorial.raster_zonal_stats.html
+    2024-05-23T09:08:50.682Z
+  
+  
+    https://geowrangler.thinkingmachin.es/tutorial.raster_to_df.html
+    2024-05-23T09:08:50.746Z
+  
+  
+    https://geowrangler.thinkingmachin.es/index.html
+    2024-05-23T09:08:50.878Z
+  
+  
+    https://geowrangler.thinkingmachin.es/tutorial.geometry_validation.html
+    2024-05-23T09:08:50.898Z
+  
+  
+    https://geowrangler.thinkingmachin.es/datasets_geofabrik.html
+    2024-05-23T09:08:50.994Z
+  
+
diff --git a/spatialjoin_highest_intersection.html b/spatialjoin_highest_intersection.html
new file mode 100644
index 0000000..2b38bb6
--- /dev/null
+++ b/spatialjoin_highest_intersection.html
@@ -0,0 +1,1261 @@
+
+
+
+
+
+
+
+
+
+
+geowrangler - Spatial Join Highest Intersection
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      
+        
+
+
      
+
+  
+
      
+
+    
+
+
      
+
+
      
+
      
+
      Spatial Join Highest Intersection
      
+
      
+
+
      
+  
      
+    find intersection of largest area joined
+  
      
+
      
+
+
+
      
+
+    
+  
+    
+  
      
+  
+
+
+
      
+
+
+
+
      
+
      
+
      Import
      
+
      
+
      
+
      Generate Test Data
      
+
      
+
      
+
      Load a sample admin boundary file using geopandas
      
+
      
+
      admin_bounds_gdf = gpd.read_file("../data/geoboundary.geojson")
      
+
      
+
      
+
      admin_bounds_gdf.head(3)
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      shapeNameshapeISOshapeIDshapeGroupshapeTypegeometry
      0AbraNonePHL-ADM2-3_0_0-B1PHLADM2MULTIPOLYGON (((120.96795 17.95706, 120.97803 ...
      1Agusan del NorteNonePHL-ADM2-3_0_0-B2PHLADM2MULTIPOLYGON (((125.57724 9.45679, 125.59687 9...
      2Agusan del SurNonePHL-ADM2-3_0_0-B3PHLADM2MULTIPOLYGON (((125.91087 8.85625, 125.91461 8...
      
+
+
      
+
      
+
      
+
      
+
      
+
      admin_bounds_gdf.dtypes
      
+
      
+
      shapeName       object
+shapeISO        object
+shapeID         object
+shapeGroup      object
+shapeType       object
+geometry      geometry
+dtype: object
      
+
      
+
      
+
      
+
      # admin_bounds_gdf.explore()
      
+
      
+
      
+
      
+
      Generate a sample grid
      
+
      
+
      grid_generator5k = grids.SquareGridGenerator(50_000)  # 5 km x 5 km square cells
      
+
      
+
      
+
      grid_gdf = grid_generator5k.generate_grid(admin_bounds_gdf)
      
+
      
+
      CPU times: user 5.08 s, sys: 14.4 ms, total: 5.09 s
+Wall time: 5.11 s
      
+
      
+
      
+
      
+
      grid_gdf.plot();
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      ax = admin_bounds_gdf.plot(facecolor="grey", edgecolor="grey", alpha=0.2)
+ax = grid_gdf.plot(ax=ax, facecolor="none", edgecolor="green")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      grid_gdf.describe()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
      xy
      count323.000000323.000000
      mean11.77399415.018576
      std4.8615948.791210
      min0.0000000.000000
      25%8.0000008.000000
      50%11.00000014.000000
      75%16.00000021.000000
      max21.00000037.000000
      
+
+
      
+
      
+
      
+
      
+
      
+
      grid_gdf.head(3)
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
      xygeometry
      033POLYGON ((118.27581 5.92871, 118.72497 5.92871...
      123POLYGON ((117.82665 5.92871, 118.27581 5.92871...
      234POLYGON ((118.27581 6.37528, 118.72497 6.37528...
      
+
+
      
+
      
+
      
+
      
+
      
+
      grid_gdf.dtypes
      
+
      
+
      x              int64
+y              int64
+geometry    geometry
+dtype: object
      
+
      
+
      
+
      
+
      
+
      Spatial join with highest intersection
      
+
      
+
      source
      
+
      
+
      get_highest_intersection
      
+
      
+
       get_highest_intersection (gdf1:geopandas.geodataframe.GeoDataFrame,
+                           gdf2:geopandas.geodataframe.GeoDataFrame,
+                           proj_crs:str)
      
+
      
+
      Gets the intersection based on the largest area joined
      
+
+++++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      




      TypeDetails
      gdf1GeoDataFramegdf1 will be the basis of output geometry
      gdf2GeoDataFramegdf2 data will all be included during intersection
      proj_crsstrmetric CRS (e.g., Philippines uses EPSG:32651)
      ReturnsGeoDataFrame
      
+
      
+
      get_highest_intersection(grid_gdf, admin_bounds_gdf, "EPSG:32651")
      
+
      
+
      
+
      
+
+
+
+
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+
+
+
+
+
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+
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+
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+
+
+
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+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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+
+
+
      geometryxyshapeNameshapeISOshapeIDshapeGroupshapeType
      0POLYGON ((118.27581 5.92871, 118.72497 5.92871...33Tawi-TawiNonePHL-ADM2-3_0_0-B77PHLADM2
      1POLYGON ((117.82665 5.92871, 118.27581 5.92871...23Tawi-TawiNonePHL-ADM2-3_0_0-B77PHLADM2
      2POLYGON ((118.27581 6.37528, 118.72497 6.37528...34Tawi-TawiNonePHL-ADM2-3_0_0-B77PHLADM2
      3POLYGON ((118.27581 6.82146, 118.72497 6.82146...35Tawi-TawiNonePHL-ADM2-3_0_0-B77PHLADM2
      4POLYGON ((116.92834 7.26723, 117.37749 7.26723...06PalawanNonePHL-ADM2-3_0_0-B59PHLADM2
      ...........................
      318POLYGON ((125.46233 5.03451, 125.91149 5.03451...191Davao del SurNonePHL-ADM2-3_0_0-B28PHLADM2
      319POLYGON ((125.46233 9.93163, 125.91149 9.93163...1912Dinagat IslandsNonePHL-ADM2-3_0_0-B30PHLADM2
      320POLYGON ((125.46233 10.37375, 125.91149 10.373...1913Eastern SamarNonePHL-ADM2-3_0_0-B31PHLADM2
      321POLYGON ((125.91149 9.93163, 126.36065 9.93163...2012Surigao del NorteNonePHL-ADM2-3_0_0-B74PHLADM2
      322POLYGON ((125.91149 10.37375, 126.36065 10.373...2013Eastern SamarNonePHL-ADM2-3_0_0-B31PHLADM2
      
+
+
      323 rows × 8 columns
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Check plot of output
      
+
      
+
      output = get_highest_intersection(grid_gdf, admin_bounds_gdf, "EPSG:32651")
      
+
      
+
      
+
      output.plot()
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      # output.explore()
      
+
      
+
+
+
      
+
+
       
+
+
       
+
+
+
+
+
\ No newline at end of file
diff --git a/styles.css b/styles.css
new file mode 100644
index 0000000..66ccc49
--- /dev/null
+++ b/styles.css
@@ -0,0 +1,37 @@
+.cell {
+  margin-bottom: 1rem;
+}
+
+.cell > .sourceCode {
+  margin-bottom: 0;
+}
+
+.cell-output > pre {
+  margin-bottom: 0;
+}
+
+.cell-output > pre, .cell-output > .sourceCode > pre, .cell-output-stdout > pre {
+  margin-left: 0.8rem;
+  margin-top: 0;
+  background: none;
+  border-left: 2px solid lightsalmon;
+  border-top-left-radius: 0;
+  border-top-right-radius: 0;
+}
+
+.cell-output > .sourceCode {
+  border: none;
+}
+
+.cell-output > .sourceCode {
+  background: none;
+  margin-top: 0;
+}
+
+div.description {
+  padding-left: 2px;
+  padding-top: 5px;
+  font-style: italic;
+  font-size: 135%;
+  opacity: 70%;
+}
diff --git a/tile_clustering.html b/tile_clustering.html
new file mode 100644
index 0000000..2366642
--- /dev/null
+++ b/tile_clustering.html
@@ -0,0 +1,1074 @@
+
+
+
+
+
+
+
+
+
+
+geowrangler - Tile Clustering
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      
+  
      
+    
+  
+
      
+
+
      
+
+  
+
      
+
+    
+
+
      
+
+
      
+
      
+
      Tile Clustering
      
+
      
+
+
      
+  
      
+    cluster together adjacent grid tiles
+  
      
+
      
+
+
+
      
+
+    
+  
+    
+  
      
+  
+
+
+
      
+
+
+
+
      
+
      
+
      source
      
+
      
+
      TileClustering
      
+
      
+
       TileClustering (cluster_type:str='four_way')
      
+
      
+
      *Cluster together adjacent square grid cells. Grid cells belonging to the same cluster will get assigned the same ID. Optionally, you cluster adjacent cells by category by passing in category_col
      
+
      By default, with cluster_type = ” four-way”, it clusters together grid cells with adjacent edges only. If you wish to consider grid cells with adjacent corners as well, use cluster_type = ” eight-way”*
      
+
      
+
      source
      
+
      
+
      
+
      TileClustering.cluster_tiles
      
+
      
+
       TileClustering.cluster_tiles (df:pandas.core.frame.DataFrame,
+                               grid_x_col='x', grid_y_col='y',
+                               category_col:Optional[str]=None)
      
+
      
+
      Appends the cluster ID for each square grid cell
      
+
      
+
      
+
      Test data
      
+
      Create sample scores for square grid cells and cluster the cells
      
+
      
+
      import geopandas as gpd
+import numpy as np
+
+from geowrangler import grids
      
+
      
+
      
+
      np.random.seed(1562)
+
+region3_gdf = gpd.read_file("../data/region3_admin.geojson")
      
+
      
+
      
+
      grid_generator5k = grids.SquareGridGenerator(5_000)
+grid_gdf5k = grid_generator5k.generate_grid(region3_gdf)
+grid_gdf5k.head()
      
+
      
+
      
+
      
+
+
+
+
+
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+
+
+
      xygeometry
      078POLYGON ((120.10024 14.75528, 120.14516 14.755...
      168POLYGON ((120.05533 14.75528, 120.10024 14.755...
      298POLYGON ((120.19008 14.75528, 120.23499 14.755...
      399POLYGON ((120.19008 14.79871, 120.23499 14.798...
      4109POLYGON ((120.23499 14.79871, 120.27991 14.798...
      
+
+
      
+
      
+
      
+
      
+
      
+
      grid_gdf5k.plot()
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      grid_gdf5k["score"] = np.random.random(len(grid_gdf5k))
+grid_gdf5k["class"] = grid_gdf5k["score"] > 0.7
+grid_gdf5k.head()
      
+
      
+
      CPU times: user 3.15 ms, sys: 1.32 ms, total: 4.47 ms
+Wall time: 10 ms
      
+
      
+
      
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+
      xygeometryscoreclass
      078POLYGON ((120.10024 14.75528, 120.14516 14.755...0.761806True
      168POLYGON ((120.05533 14.75528, 120.10024 14.755...0.012455False
      298POLYGON ((120.19008 14.75528, 120.23499 14.755...0.446552False
      399POLYGON ((120.19008 14.79871, 120.23499 14.798...0.669020False
      4109POLYGON ((120.23499 14.79871, 120.27991 14.798...0.815914True
      
+
+
      
+
      
+
      
+
      
+
      
+
      tileclustering = TileClustering()
+grid_gdf5k = tileclustering.cluster_tiles(grid_gdf5k, category_col="class")
+grid_gdf5k.head()
      
+
      
+
      CPU times: user 25.8 ms, sys: 0 ns, total: 25.8 ms
+Wall time: 47.6 ms
      
+
      
+
      
+
      
+
      
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+
+
+
      xygeometryscoreclasstile_cluster
      078POLYGON ((120.10024 14.75528, 120.14516 14.755...0.761806True6-1
      168POLYGON ((120.05533 14.75528, 120.10024 14.755...0.012455False7-2
      298POLYGON ((120.19008 14.75528, 120.23499 14.755...0.446552False1-2
      399POLYGON ((120.19008 14.79871, 120.23499 14.798...0.669020False1-2
      4109POLYGON ((120.23499 14.79871, 120.27991 14.798...0.815914True23-1
      
+
+
      
+
      
+
      
+
      
+
      
+
      grid_gdf5k["tile_cluster"].nunique()
      
+
      
+
      160
      
+
      
+
      
+
      
+
      grid_gdf5k.plot(column="class", categorical=True, cmap="Spectral")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
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+
       
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+
+
\ No newline at end of file
diff --git a/tutorial.area_zonal_stats.html b/tutorial.area_zonal_stats.html
new file mode 100644
index 0000000..2bb0557
--- /dev/null
+++ b/tutorial.area_zonal_stats.html
@@ -0,0 +1,1427 @@
+
+
+
+
+
+
+
+
+
+geowrangler - Vector Area Zonal Stats Tutorial
+
+
+
+
+
+
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+
      
+  
      
+    
+  
+
      
+
+
      
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+  
+
      
+
+    
+
+
      
+
+
      
+
      
+
      Vector Area Zonal Stats Tutorial
      
+
      
+
+
+
+
      
+
+    
+  
+    
+  
      
+  
+
+
+
      
+
+
+
+
      
+
      A basic introduction to vector area zonal stats
      
+
      
+
      
+
      
+
      Basic Usage
      
+
      Generate area zonal stats for a GeoDataframe containing areas of interest with a vector data source containing areas associated with statistics.
      
+
      
+
      
+
      
+
+
      
+
      
+Note
+
      
+
      
+
      
+
      The assumption for the data is that the statistics are uniformly distributed over the entire area for each row in the data source.
      
+
      
+
      
+
      
+
      import geopandas as gpd
+import matplotlib.pyplot as plt
+import geowrangler.area_zonal_stats as azs
      
+
      
+
      
+
      Simple Grid AOIs and Data
      
+
      
+
      simple_aoi = gpd.read_file("../data/simple_planar_aoi.geojson")
+simple_data = gpd.read_file("../data/simple_planar_data.geojson")
      
+
      
+
      Given an AOI (simple_aoi) and geodataframe containing sample data (simple_data)
      
+
      
+
      simple_aoi
      
+
      
+
      
+
      
+
+
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+
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+
      geometry
      0POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...
      1POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...
      2POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...
      
+
+
      
+
      
+
      
+
      
+
      
+
      simple_data
      
+
      
+
      
+
      
+
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+
+
      populationinternet_speedgeometry
      010020.0POLYGON ((0.250 0.000, 0.250 1.000, 1.250 1.00...
      120010.0POLYGON ((1.250 0.000, 1.250 1.000, 2.250 1.00...
      23005.0POLYGON ((2.250 0.000, 2.250 1.000, 3.250 1.00...
      
+
+
      
+
      
+
      
+
      
+
      In order correctly apportion the statistic, we need to make sure that the AOI and data geodataframes are using a planar CRS (i.e., gdf.crs.is_geographic == False)
      
+
      
+
      simple_aoi.crs
      
+
      
+
      <Projected CRS: EPSG:3857>
+Name: WGS 84 / Pseudo-Mercator
+Axis Info [cartesian]:
+- X[east]: Easting (metre)
+- Y[north]: Northing (metre)
+Area of Use:
+- name: World between 85.06°S and 85.06°N.
+- bounds: (-180.0, -85.06, 180.0, 85.06)
+Coordinate Operation:
+- name: Popular Visualisation Pseudo-Mercator
+- method: Popular Visualisation Pseudo Mercator
+Datum: World Geodetic System 1984 ensemble
+- Ellipsoid: WGS 84
+- Prime Meridian: Greenwich
      
+
      
+
      
+
      
+
      simple_data.crs
      
+
      
+
      <Projected CRS: EPSG:3857>
+Name: WGS 84 / Pseudo-Mercator
+Axis Info [cartesian]:
+- X[east]: Easting (metre)
+- Y[north]: Northing (metre)
+Area of Use:
+- name: World between 85.06°S and 85.06°N.
+- bounds: (-180.0, -85.06, 180.0, 85.06)
+Coordinate Operation:
+- name: Popular Visualisation Pseudo-Mercator
+- method: Popular Visualisation Pseudo Mercator
+Datum: World Geodetic System 1984 ensemble
+- Ellipsoid: WGS 84
+- Prime Meridian: Greenwich
      
+
      
+
      
+
      
+
      ax = plt.axes()
+ax = simple_data.plot(
+    ax=ax, color=["orange", "brown", "purple"], edgecolor="yellow", alpha=0.4
+)
+ax = simple_aoi.plot(ax=ax, facecolor="none", edgecolor=["r", "g", "b"])
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      The red, green, and blue outlines are the 3 regions of interest (AOI) while the orange, brown, and purple areas are the data areas.
      
+
      
+
      empty_aoi_results = azs.create_area_zonal_stats(simple_aoi, simple_data)
      
+
      
+
      If no aggregations are specified, the include_intersect=True arg specifies that the sum of the data areas intersecting our AOI is computed in the column intersect_area_sum.
      
+
      
+
      empty_aoi_results
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
      geometryintersect_area_sum
      0POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...0.75
      1POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...1.00
      2POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...1.00
      
+
+
      
+
      
+
      
+
      
+
      
+
      Apportioning aggregated statistics
      
+
      To aggregate statistics over the AOI areas intersecting with the data areas, the default behavior of “apportioning” the statistic over the intersection of the data area overlapping the AOI area depends on the statistic.
      
+
        
+
      • For sum it apportions the total value of the statistic over the proportion of the data area overlapping the AOI area divided by the total area of the data.
        • 
+
      • For mean it apportions the total value of the statistic over the proportion of the data area overlapping the AOI area divided by the total area of the aoi.
        • 
+
      • For other statistics, there is no apportioning done and uses the raw statistics from the data areas overlapping the AOI area.
        • 
+
      
+
      
+
      
+
      
+
+
      
+
      
+Note
+
      
+
      
+
      
+
      This default behavior can be overridden by specifying a prefix (e.g., raw_, data_, and aoi_) to the statistic (i.e., func) – e.g. if you wish the apportion the sum as a proportion of the aoi area instead of the data area, specify the func as aoi_sum instead of sum.
      
+
      
+
      
+
      
+
      simple_aoi_results = azs.create_area_zonal_stats(
+    simple_aoi,
+    simple_data,
+    [
+        dict(func=["sum", "count"], column="population"),
+        dict(func=["mean", "max", "min", "std"], column="internet_speed"),
+    ],
+)
      
+
      
+
      CPU times: user 53.2 ms, sys: 728 µs, total: 53.9 ms
+Wall time: 51.6 ms
      
+
      
+
      
+
      
+
      simple_aoi_results
      
+
      
+
      
+
      
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+
      geometryintersect_area_sumpopulation_sumpopulation_countinternet_speed_meaninternet_speed_maxinternet_speed_mininternet_speed_std
      0POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...0.7575.0115.00020.00.0NaN
      1POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...1.00175.026.25020.010.07.071068
      2POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...1.00275.023.12510.05.03.535534
      
+
+
      
+
      
+
      
+
      
+
      
+
      simple_aoi_results.population_sum.sum(axis=None)
      
+
      
+
      525.0
      
+
      
+
      
+
      Note the value of the mean of the internet speed for the 1st area above.
      
+
      While there is one data area overlapping with an internet speed of 20.0 the computed aggregate statistic for the mean is 15.0 due to the apportioning which only accounts for 0.75 (the total AOI area is 1.0) – meaning it assigns a value of 0.0 for the 0.25 area with no overlapping data area.
      
+
      If you wish to override this behavior and impute the value of the statistic as the means of all the overlapping areas (in proportion to the overlap of their area over the total area of the AOI), you can additionally add a prefix of imputed_ as shown in the next example. Note that adding a prefix doesn’t change the default output column name, which is why we explicitly specified an output column name (internet_speed_imputed_mean in order to differentiate it from the original internet_speed_mean column.
      
+
      Notice as well that adding a raw_ prefix to the min,max,std statistics doesn’t change their values since by default these statistics don’t use any apportioning (so specifying raw_ is redundant in this case).
      
+
      Lastly, check the effect of setting of the fix_min arg to False (the default value is True) – since it uses the raw column to compute the min, it is not aware that there are areas in the AOI that have no overlapping data areas and uses the min value from the overlapping areas (in this case, since there is only 1 partially overlapping area, it uses that as the min value). The fix_min arg “fixes” this by checking if the data areas completely overlap the AOI area and sets the minimum to 0 if there is a portion of the AOI area that is not completely intersected by a data area.
      
+
      
+
      corrected_aoi_results = azs.create_area_zonal_stats(
+    simple_aoi,
+    simple_data,
+    [
+        dict(func=["sum", "count"], column="population"),
+        dict(
+            func=["mean", "imputed_mean", "raw_max", "raw_min", "raw_std"],
+            column="internet_speed",
+            output=[
+                "internet_speed_mean",
+                "internet_speed_imputed_mean",
+                "internet_speed_max",
+                "internet_speed_min",
+                "internet_speed_std",
+            ],
+        ),
+    ],
+    fix_min=False,
+)
      
+
      
+
      CPU times: user 103 ms, sys: 0 ns, total: 103 ms
+Wall time: 101 ms
      
+
      
+
      
+
      
+
      corrected_aoi_results
      
+
      
+
      
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      geometryintersect_area_sumpopulation_sumpopulation_countinternet_speed_meaninternet_speed_imputed_meaninternet_speed_maxinternet_speed_mininternet_speed_std
      0POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...0.7575.0115.00020.00020.020.0NaN
      1POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...1.00175.026.2506.25020.010.07.071068
      2POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...1.00275.023.1253.12510.05.03.535534
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Custom Grids over admin area data
      
+
      
+
      Another example using aggregated statistics over admin areas (barangay level) that are apportioned into grid areas by their overlap.
      
+
      
+
      
+
      Notice that since the grid areas does not completely overlap the admin areas (and vice versa) , the sum of their total populations and land areas are not equal.
      
+
      
+
      
+
      region3_admin_grids = gpd.read_file("../data/region3_admin_grids.geojson")
+region3_admin_grids = region3_admin_grids.to_crs("EPSG:3857")  # convert to planar
      
+
      
+
      CPU times: user 159 ms, sys: 0 ns, total: 159 ms
+Wall time: 157 ms
      
+
      
+
      
+
      
+
      region3_pop_bgy_level = gpd.read_file("../data/region3_population_bgy_level.geojson")
      
+
      
+
      CPU times: user 376 ms, sys: 29.2 ms, total: 405 ms
+Wall time: 402 ms
      
+
      
+
      
+
      
+
      aoi_result = azs.create_area_zonal_stats(
+    region3_admin_grids,
+    region3_pop_bgy_level,
+    [
+        dict(func=["sum", "count"], column="population"),
+    ],
+)
      
+
      
+
      CPU times: user 764 ms, sys: 0 ns, total: 764 ms
+Wall time: 763 ms
      
+
      
+
      
+
      
+
      aoi_result
      
+
      
+
      
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+
      xygeometryintersect_area_sumpopulation_sumpopulation_count
      0030POLYGON ((13334497.956 1771012.807, 13339497.9...1.213773e+06687.8328401
      1031POLYGON ((13334497.956 1776012.807, 13339497.9...2.471924e+06986.8908531
      2032POLYGON ((13334497.956 1781012.807, 13339497.9...2.748813e+061097.4358401
      3130POLYGON ((13339497.956 1771012.807, 13344497.9...1.081669e+06468.3686142
      4132POLYGON ((13339497.956 1781012.807, 13344497.9...8.941593e+048.5706042
      .....................
      10695444POLYGON ((13604497.956 1841012.807, 13609497.9...1.976718e+0515.3981621
      10705445POLYGON ((13604497.956 1846012.807, 13609497.9...1.019141e+071613.9133933
      10715446POLYGON ((13604497.956 1851012.807, 13609497.9...3.129991e+061033.9979792
      10725447POLYGON ((13604497.956 1856012.807, 13609497.9...8.106461e+06250.8936912
      10735448POLYGON ((13604497.956 1861012.807, 13609497.9...1.928818e+07299.3779873
      
+
+
      1074 rows × 6 columns
      
+
      
+
      
+
      
+
      
+
      
+
      (
+    aoi_result.geometry.area.sum(),
+    aoi_result.intersect_area_sum.sum(),
+    aoi_result.population_sum.sum(),
+)
      
+
      
+
      (26850000000.000072, 24548892214.947075, 12136963.9717533)
      
+
      
+
      
+
      
+
      (region3_pop_bgy_level.geometry.area.sum(), region3_pop_bgy_level.population.sum())
      
+
      
+
      (27658006631.682587, 13139695)
      
+
      
+
      
+
      
+
      gdf = region3_admin_grids
+gdf2 = region3_pop_bgy_level
      
+
      
+
      
+
      ax = plt.axes()
+ax = gdf2.plot(ax=ax, column="population", alpha=0.4)
+ax = gdf.plot(ax=ax, facecolor="none", edgecolor="blue", alpha=0.4)
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      ax = plt.axes()
+ax = gdf2.plot(ax=ax, facecolor="none", alpha=0.4, edgecolor="red")
+ax = aoi_result.plot(column="population_sum", ax=ax, alpha=0.4, edgecolor="blue")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      ax = plt.axes()
+ax = gdf2.plot(ax=ax, column="population", alpha=0.4, edgecolor="red")
+ax = aoi_result.plot(column="intersect_area_sum", ax=ax, alpha=0.4, edgecolor="blue")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
+
+
      
+
      
+
+
       
+
+
       
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zZ7o(UY8{ED)GS|
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+geowrangler - Datasets Download
+
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      Datasets Download
      
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+    
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+
      Basic introduction to downloading open geospatial data using Geowrangler
      
+
      
+
      
+
      
+
      Downloading Geofabrik Data
      
+
      
+
      import geopandas as gpd
+from geowrangler.datasets import geofabrik
      
+
      
+
      
+
      Listing down available regions
      
+
      
+
      regions = geofabrik.list_geofabrik_regions()
+# list down regions in asia
+{k: v for k, v in regions.items() if "asia" in v}
      
+
      
+
      {'afghanistan': 'https://download.geofabrik.de/asia/afghanistan-latest-free.shp.zip',
+ 'armenia': 'https://download.geofabrik.de/asia/armenia-latest-free.shp.zip',
+ 'azerbaijan': 'https://download.geofabrik.de/asia/azerbaijan-latest-free.shp.zip',
+ 'bangladesh': 'https://download.geofabrik.de/asia/bangladesh-latest-free.shp.zip',
+ 'bhutan': 'https://download.geofabrik.de/asia/bhutan-latest-free.shp.zip',
+ 'cambodia': 'https://download.geofabrik.de/asia/cambodia-latest-free.shp.zip',
+ 'central-zone': 'https://download.geofabrik.de/asia/india/central-zone-latest-free.shp.zip',
+ 'china': 'https://download.geofabrik.de/asia/china-latest-free.shp.zip',
+ 'chubu': 'https://download.geofabrik.de/asia/japan/chubu-latest-free.shp.zip',
+ 'chugoku': 'https://download.geofabrik.de/asia/japan/chugoku-latest-free.shp.zip',
+ 'east-timor': 'https://download.geofabrik.de/asia/east-timor-latest-free.shp.zip',
+ 'eastern-zone': 'https://download.geofabrik.de/asia/india/eastern-zone-latest-free.shp.zip',
+ 'gcc-states': 'https://download.geofabrik.de/asia/gcc-states-latest-free.shp.zip',
+ 'hokkaido': 'https://download.geofabrik.de/asia/japan/hokkaido-latest-free.shp.zip',
+ 'iran': 'https://download.geofabrik.de/asia/iran-latest-free.shp.zip',
+ 'iraq': 'https://download.geofabrik.de/asia/iraq-latest-free.shp.zip',
+ 'israel-and-palestine': 'https://download.geofabrik.de/asia/israel-and-palestine-latest-free.shp.zip',
+ 'java': 'https://download.geofabrik.de/asia/indonesia/java-latest-free.shp.zip',
+ 'jordan': 'https://download.geofabrik.de/asia/jordan-latest-free.shp.zip',
+ 'kalimantan': 'https://download.geofabrik.de/asia/indonesia/kalimantan-latest-free.shp.zip',
+ 'kansai': 'https://download.geofabrik.de/asia/japan/kansai-latest-free.shp.zip',
+ 'kanto': 'https://download.geofabrik.de/asia/japan/kanto-latest-free.shp.zip',
+ 'kazakhstan': 'https://download.geofabrik.de/asia/kazakhstan-latest-free.shp.zip',
+ 'kyrgyzstan': 'https://download.geofabrik.de/asia/kyrgyzstan-latest-free.shp.zip',
+ 'kyushu': 'https://download.geofabrik.de/asia/japan/kyushu-latest-free.shp.zip',
+ 'laos': 'https://download.geofabrik.de/asia/laos-latest-free.shp.zip',
+ 'lebanon': 'https://download.geofabrik.de/asia/lebanon-latest-free.shp.zip',
+ 'malaysia-singapore-brunei': 'https://download.geofabrik.de/asia/malaysia-singapore-brunei-latest-free.shp.zip',
+ 'maldives': 'https://download.geofabrik.de/asia/maldives-latest-free.shp.zip',
+ 'maluku': 'https://download.geofabrik.de/asia/indonesia/maluku-latest-free.shp.zip',
+ 'mongolia': 'https://download.geofabrik.de/asia/mongolia-latest-free.shp.zip',
+ 'myanmar': 'https://download.geofabrik.de/asia/myanmar-latest-free.shp.zip',
+ 'nepal': 'https://download.geofabrik.de/asia/nepal-latest-free.shp.zip',
+ 'north-eastern-zone': 'https://download.geofabrik.de/asia/india/north-eastern-zone-latest-free.shp.zip',
+ 'north-korea': 'https://download.geofabrik.de/asia/north-korea-latest-free.shp.zip',
+ 'northern-zone': 'https://download.geofabrik.de/asia/india/northern-zone-latest-free.shp.zip',
+ 'nusa-tenggara': 'https://download.geofabrik.de/asia/indonesia/nusa-tenggara-latest-free.shp.zip',
+ 'pakistan': 'https://download.geofabrik.de/asia/pakistan-latest-free.shp.zip',
+ 'papua': 'https://download.geofabrik.de/asia/indonesia/papua-latest-free.shp.zip',
+ 'philippines': 'https://download.geofabrik.de/asia/philippines-latest-free.shp.zip',
+ 'shikoku': 'https://download.geofabrik.de/asia/japan/shikoku-latest-free.shp.zip',
+ 'south-korea': 'https://download.geofabrik.de/asia/south-korea-latest-free.shp.zip',
+ 'southern-zone': 'https://download.geofabrik.de/asia/india/southern-zone-latest-free.shp.zip',
+ 'sri-lanka': 'https://download.geofabrik.de/asia/sri-lanka-latest-free.shp.zip',
+ 'sulawesi': 'https://download.geofabrik.de/asia/indonesia/sulawesi-latest-free.shp.zip',
+ 'sumatra': 'https://download.geofabrik.de/asia/indonesia/sumatra-latest-free.shp.zip',
+ 'syria': 'https://download.geofabrik.de/asia/syria-latest-free.shp.zip',
+ 'taiwan': 'https://download.geofabrik.de/asia/taiwan-latest-free.shp.zip',
+ 'tajikistan': 'https://download.geofabrik.de/asia/tajikistan-latest-free.shp.zip',
+ 'thailand': 'https://download.geofabrik.de/asia/thailand-latest-free.shp.zip',
+ 'tohoku': 'https://download.geofabrik.de/asia/japan/tohoku-latest-free.shp.zip',
+ 'turkmenistan': 'https://download.geofabrik.de/asia/turkmenistan-latest-free.shp.zip',
+ 'uzbekistan': 'https://download.geofabrik.de/asia/uzbekistan-latest-free.shp.zip',
+ 'vietnam': 'https://download.geofabrik.de/asia/vietnam-latest-free.shp.zip',
+ 'western-zone': 'https://download.geofabrik.de/asia/india/western-zone-latest-free.shp.zip',
+ 'yemen': 'https://download.geofabrik.de/asia/yemen-latest-free.shp.zip'}
      
+
      
+
      
+
      
+
      
+
      Downloading a region file to a directory
      
+
      
+
      downloaded_file = geofabrik.download_geofabrik_region("laos", "../test_dir")
+downloaded_file
      
+
      
+
      2023-02-01 17:00:04.495 | INFO     | geowrangler.datasets.utils:urlretrieve:27 - Retrieving https://download.geofabrik.de/asia/laos-latest-free.shp.zip into ../test_dir/laos-latest-free.shp.zip
      
+
      
+
      
+
+
+
      
+
      
+
+    
      
+      
+      100.00% [97345536/97341916 00:17<00:00]
+    
      
+    
+
      
+
      
+
      CPU times: user 1.58 s, sys: 980 ms, total: 2.56 s
+Wall time: 18 s
      
+
      
+
      
+
      Path('../test_dir/laos-latest-free.shp.zip')
      
+
      
+
      
+
      
+
      
+
      Loading geofabrik files
      
+
      
+
      gdf = gpd.read_file(downloaded_file)
+gdf.head()
      
+
      
+
      CPU times: user 19.4 s, sys: 528 ms, total: 19.9 s
+Wall time: 19.9 s
      
+
      
+
      
+
      
+
      
+
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+
+
+
+
      osm_idcodefclassnametypegeometry
      0361928111500buildingDon Chan PalaceNonePOLYGON ((102.61392 17.95514, 102.61419 17.955...
      1361956711500buildingຕະຫລາດເຊົ້າ. 早市塲NonePOLYGON ((102.61382 17.96520, 102.61393 17.965...
      2361959731500buildingMercure Hotel LaotelNonePOLYGON ((102.59386 17.96890, 102.59432 17.968...
      3367844371500buildingNoneNonePOLYGON ((105.79786 15.11905, 105.79838 15.119...
      4378864321500buildingPresidential PalaceNonePOLYGON ((102.60940 17.96246, 102.60951 17.962...
      
+
+
      
+
      
+
      
+
      
+
      You can also list the contents of the zipped shape file as well as load the shape file within it
      
+
      
+
      !unzip -l {downloaded_file.as_posix()}
      
+
      
+
      Archive:  ../test_dir/laos-latest-free.shp.zip
+  Length      Date    Time    Name
+---------  ---------- -----   ----
+      647  2023-02-01 09:00   README
+        6  2023-02-01 09:00   gis_osm_buildings_a_free_1.cpg
+ 68322734  2023-02-01 09:00   gis_osm_buildings_a_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_buildings_a_free_1.prj
+ 60089724  2023-02-01 09:00   gis_osm_buildings_a_free_1.shp
+  3312708  2023-02-01 09:00   gis_osm_buildings_a_free_1.shx
+        6  2023-02-01 09:00   gis_osm_landuse_a_free_1.cpg
+  2845642  2023-02-01 09:00   gis_osm_landuse_a_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_landuse_a_free_1.prj
+ 14407732  2023-02-01 09:00   gis_osm_landuse_a_free_1.shp
+   157092  2023-02-01 09:00   gis_osm_landuse_a_free_1.shx
+        6  2023-02-01 09:00   gis_osm_natural_a_free_1.cpg
+     7702  2023-02-01 09:00   gis_osm_natural_a_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_natural_a_free_1.prj
+    15556  2023-02-01 09:00   gis_osm_natural_a_free_1.shp
+      516  2023-02-01 09:00   gis_osm_natural_a_free_1.shx
+        6  2023-02-01 09:00   gis_osm_natural_free_1.cpg
+   753872  2023-02-01 09:00   gis_osm_natural_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_natural_free_1.prj
+   145644  2023-02-01 09:00   gis_osm_natural_free_1.shp
+    41684  2023-02-01 09:00   gis_osm_natural_free_1.shx
+        6  2023-02-01 09:00   gis_osm_places_a_free_1.cpg
+    26854  2023-02-01 09:00   gis_osm_places_a_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_places_a_free_1.prj
+   441004  2023-02-01 09:00   gis_osm_places_a_free_1.shp
+     1476  2023-02-01 09:00   gis_osm_places_a_free_1.shx
+        6  2023-02-01 09:00   gis_osm_places_free_1.cpg
+  1142544  2023-02-01 09:00   gis_osm_places_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_places_free_1.prj
+   206460  2023-02-01 09:00   gis_osm_places_free_1.shp
+    59060  2023-02-01 09:00   gis_osm_places_free_1.shx
+        6  2023-02-01 09:00   gis_osm_pofw_a_free_1.cpg
+    39602  2023-02-01 09:00   gis_osm_pofw_a_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_pofw_a_free_1.prj
+    49992  2023-02-01 09:00   gis_osm_pofw_a_free_1.shp
+     2276  2023-02-01 09:00   gis_osm_pofw_a_free_1.shx
+        6  2023-02-01 09:00   gis_osm_pofw_free_1.cpg
+    66427  2023-02-01 09:00   gis_osm_pofw_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_pofw_free_1.prj
+    12896  2023-02-01 09:00   gis_osm_pofw_free_1.shp
+     3756  2023-02-01 09:00   gis_osm_pofw_free_1.shx
+        6  2023-02-01 09:00   gis_osm_pois_a_free_1.cpg
+   274067  2023-02-01 09:00   gis_osm_pois_a_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_pois_a_free_1.prj
+   351936  2023-02-01 09:00   gis_osm_pois_a_free_1.shp
+    15212  2023-02-01 09:00   gis_osm_pois_a_free_1.shx
+        6  2023-02-01 09:00   gis_osm_pois_free_1.cpg
+  1884292  2023-02-01 09:00   gis_osm_pois_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_pois_free_1.prj
+   363932  2023-02-01 09:00   gis_osm_pois_free_1.shp
+   104052  2023-02-01 09:00   gis_osm_pois_free_1.shx
+        6  2023-02-01 09:00   gis_osm_railways_free_1.cpg
+   100110  2023-02-01 09:00   gis_osm_railways_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_railways_free_1.prj
+   101636  2023-02-01 09:00   gis_osm_railways_free_1.shp
+     5124  2023-02-01 09:00   gis_osm_railways_free_1.shx
+        6  2023-02-01 09:00   gis_osm_roads_free_1.cpg
+ 20909019  2023-02-01 09:00   gis_osm_roads_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_roads_free_1.prj
+ 50224636  2023-02-01 09:00   gis_osm_roads_free_1.shp
+   914140  2023-02-01 09:00   gis_osm_roads_free_1.shx
+        6  2023-02-01 09:00   gis_osm_traffic_a_free_1.cpg
+   102532  2023-02-01 09:00   gis_osm_traffic_a_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_traffic_a_free_1.prj
+   131304  2023-02-01 09:00   gis_osm_traffic_a_free_1.shp
+     5748  2023-02-01 09:00   gis_osm_traffic_a_free_1.shx
+        6  2023-02-01 09:00   gis_osm_traffic_free_1.cpg
+   275807  2023-02-01 09:00   gis_osm_traffic_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_traffic_free_1.prj
+    53328  2023-02-01 09:00   gis_osm_traffic_free_1.shp
+    15308  2023-02-01 09:00   gis_osm_traffic_free_1.shx
+        6  2023-02-01 09:00   gis_osm_transport_a_free_1.cpg
+    10892  2023-02-01 09:00   gis_osm_transport_a_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_transport_a_free_1.prj
+    28840  2023-02-01 09:00   gis_osm_transport_a_free_1.shp
+      692  2023-02-01 09:00   gis_osm_transport_a_free_1.shx
+        6  2023-02-01 09:00   gis_osm_transport_free_1.cpg
+    82522  2023-02-01 09:00   gis_osm_transport_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_transport_free_1.prj
+    16004  2023-02-01 09:00   gis_osm_transport_free_1.shp
+     4644  2023-02-01 09:00   gis_osm_transport_free_1.shx
+        6  2023-02-01 09:00   gis_osm_water_a_free_1.cpg
+   979927  2023-02-01 09:00   gis_osm_water_a_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_water_a_free_1.prj
+ 14026044  2023-02-01 09:00   gis_osm_water_a_free_1.shp
+    54156  2023-02-01 09:00   gis_osm_water_a_free_1.shx
+        6  2023-02-01 09:00   gis_osm_waterways_free_1.cpg
+  1662494  2023-02-01 09:00   gis_osm_waterways_free_1.dbf
+      144  2023-02-01 09:00   gis_osm_waterways_free_1.prj
+ 14396164  2023-02-01 09:00   gis_osm_waterways_free_1.shp
+    88756  2023-02-01 09:00   gis_osm_waterways_free_1.shx
+---------                     -------
+259339618                     91 files
      
+
      
+
      
+
      
+
      gdf2 = gpd.read_file(f"{downloaded_file.as_posix()}!gis_osm_pois_free_1.shp")
      
+
      
+
      CPU times: user 745 ms, sys: 19.4 ms, total: 764 ms
+Wall time: 773 ms
      
+
      
+
      
+
      
+
      
+
      Unzipping and Caching OSM
      
+
      In addition downloading, the geofabrik module provides an unzipping and caching facility (default cache directory: ~/.cache/geowrangler/osm ) to make it easier to access OSM data.
      
+
      
+
      download_path = geofabrik.download_osm_region_data("afghanistan")
+download_path
      
+
      
+
      2023-02-01 17:00:42.767 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:127 - OSM Data: Cached data available for afghanistan at /home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip? False
+2023-02-01 17:00:43.526 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:141 - OSM Data: Re-initializing OSM region zip file at /home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip...
+2023-02-01 17:00:43.529 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:148 - OSM Data: Downloading Geofabrik in /home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip...
+2023-02-01 17:00:44.247 | INFO     | geowrangler.datasets.utils:urlretrieve:27 - Retrieving https://download.geofabrik.de/asia/afghanistan-latest-free.shp.zip into /home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip
+2023-02-01 17:01:23.951 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:159 - OSM Data: Successfully downloaded and cached OSM data for afghanistan at /home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip!
      
+
      
+
      
+
+
+
      
+
      
+
+    
      
+      
+      100.00% [241524736/241516716 00:39<00:00]
+    
      
+    
+
      
+
      
+
      CPU times: user 3.22 s, sys: 1.76 s, total: 4.98 s
+Wall time: 41.2 s
      
+
      
+
      
+
      Path('/home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip')
      
+
      
+
      
+
      Downloading it the second time around should be much faster as it only checks if its in the cache and returns the path
      
+
      
+
      download_path2 = geofabrik.download_osm_region_data("afghanistan")
+download_path2
      
+
      
+
      2023-02-01 17:01:23.973 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:127 - OSM Data: Cached data available for afghanistan at /home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip? True
      
+
      
+
      
+
      CPU times: user 2.22 ms, sys: 349 µs, total: 2.57 ms
+Wall time: 2.08 ms
      
+
      
+
      
+
      Path('/home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip')
      
+
      
+
      
+
      
+
      !ls -aldh  {download_path2}
      
+
      
+
      -rw-r--r-- 1 butchtm butchtm 231M Feb  1 17:01 /home/butchtm/.cache/geowrangler/osm/afghanistan-latest-free.shp.zip
      
+
      
+
      
+
      
+
      
+
      Using the OSM Data Manager
      
+
      We also provide an OSM Data Manager which, in addition to using the default cache (~/.cache/geowrangler/osm), also caches the geodataframe for either the pois or the roads datasets from OSM in memory to avoid having to reload the OSM data from disk.
      
+
      
+
      # Create the osm data manager
+odm = geofabrik.OsmDataManager()
      
+
      
+
      
+
      pois_ph = odm.load_pois("philippines")
      
+
      
+
      2023-02-01 17:01:24.214 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:127 - OSM Data: Cached data available for philippines at /home/butchtm/.cache/geowrangler/osm/philippines-latest-free.shp.zip? False
+2023-02-01 17:01:25.631 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:141 - OSM Data: Re-initializing OSM region zip file at /home/butchtm/.cache/geowrangler/osm/philippines-latest-free.shp.zip...
+2023-02-01 17:01:25.634 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:148 - OSM Data: Downloading Geofabrik in /home/butchtm/.cache/geowrangler/osm/philippines-latest-free.shp.zip...
+2023-02-01 17:01:26.355 | INFO     | geowrangler.datasets.utils:urlretrieve:27 - Retrieving https://download.geofabrik.de/asia/philippines-latest-free.shp.zip into /home/butchtm/.cache/geowrangler/osm/philippines-latest-free.shp.zip
+2023-02-01 17:09:06.368 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:159 - OSM Data: Successfully downloaded and cached OSM data for philippines at /home/butchtm/.cache/geowrangler/osm/philippines-latest-free.shp.zip!
+2023-02-01 17:09:06.370 | DEBUG    | geowrangler.datasets.geofabrik:load_pois:218 - OSM POIs for philippines being loaded from /home/butchtm/.cache/geowrangler/osm/philippines-latest-free.shp.zip
      
+
      
+
      
+
+
+
      
+
      
+
+    
      
+      
+      100.08% [1185939456/1185011560 07:39<00:00]
+    
      
+    
+
      
+
      
+
      
+
      pois_ph.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
      osm_idcodefclassnamegeometry
      0217178202907camera_surveillanceNonePOINT (121.02120 14.57608)
      1217178722722museumAyala MuseumPOINT (121.02324 14.55358)
      2240783012402motelSogo Grand HotelPOINT (121.04515 14.56449)
      3240789592907camera_surveillanceNonePOINT (121.05945 14.60098)
      4247975112542bicycle_shopChristine Sports Cycle MarketingPOINT (120.99506 14.55224)
      
+
+
      
+
      
+
      
+
      
+
      
+
      roads_ph = odm.load_roads("philippines")
      
+
      
+
      2023-02-01 17:09:10.649 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:127 - OSM Data: Cached data available for philippines at /home/butchtm/.cache/geowrangler/osm/philippines-latest-free.shp.zip? True
+2023-02-01 17:09:10.653 | DEBUG    | geowrangler.datasets.geofabrik:load_roads:274 - OSM Roads for philippines being loaded from /home/butchtm/.cache/geowrangler/osm/philippines-latest-free.shp.zip
      
+
      
+
      
+
      
+
      roads_ph.head()
      
+
      
+
      
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+
      osm_idcodefclassnamerefonewaymaxspeedlayerbridgetunnelgeometry
      02675115tertiaryMaharlika StreetNoneF300FFLINESTRING (121.05195 14.65011, 121.05209 14.6...
      125083595153footwaySagada Mission CompoundNoneB00FFLINESTRING (120.90199 17.08090, 120.90197 17.0...
      226670975113primaryTaft Avenue170F600FFLINESTRING (120.99682 14.55577, 120.99671 14.5...
      326670995113primaryLerma Street170F600FFLINESTRING (120.98539 14.60460, 120.98545 14.6...
      426671055122residentialManalito StreetNoneB00FFLINESTRING (120.99380 14.54125, 120.99497 14.5...
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Loading OSM data from other years
      
+
      In addition to providing access to the latest OSM shape files, we also provide an optional year parameter, which allows you to download OSM data from previous years.
      
+
      
+
      
+
      
+
+
      
+
      
+Note
+
      
+
      
+
      
+
      The availability of data from previous years is dependent on what geofabrik has made available. Please check the Geofabrik download site for the list of available data.
      
+
      
+
      
+
      
+
      pois_ph = odm.load_pois("philippines", year="2017")
      
+
      
+
      2023-02-01 17:10:30.351 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:127 - OSM Data: Cached data available for philippines at /home/butchtm/.cache/geowrangler/osm/philippines-170101-free.shp.zip? False
+2023-02-01 17:10:33.834 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:141 - OSM Data: Re-initializing OSM region zip file at /home/butchtm/.cache/geowrangler/osm/philippines-170101-free.shp.zip...
+2023-02-01 17:10:33.838 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:148 - OSM Data: Downloading Geofabrik in /home/butchtm/.cache/geowrangler/osm/philippines-170101-free.shp.zip...
+2023-02-01 17:10:35.780 | INFO     | geowrangler.datasets.utils:urlretrieve:27 - Retrieving https://download.geofabrik.de/asia/philippines-170101-free.shp.zip into /home/butchtm/.cache/geowrangler/osm/philippines-170101-free.shp.zip
+2023-02-01 17:12:24.933 | INFO     | geowrangler.datasets.geofabrik:download_osm_region_data:163 - OSM Data: Successfully downloaded and cached OSM data for philippines and 2017 at /home/butchtm/.cache/geowrangler/osm/philippines-170101-free.shp.zip!
+2023-02-01 17:12:24.935 | DEBUG    | geowrangler.datasets.geofabrik:load_pois:220 - OSM POIs for philippines and year 2017 being loaded from /home/butchtm/.cache/geowrangler/osm/philippines-170101-free.shp.zip
      
+
      
+
      
+
+
+
      
+
      
+
+    
      
+      
+      100.25% [412090368/411074511 01:49<00:00]
+    
      
+    
+
      
+
      
+
      
+
      pois_ph
      
+
      
+
      
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+
      osm_idcodefclassnamegeometry
      0144016582742viewpointEast PointPOINT (120.62020 14.38562)
      1144465002721attractionIntramurosPOINT (120.97533 14.59059)
      2217178722722museumAyala MuseumPOINT (121.02324 14.55358)
      3240783012401hotelSogo Grand HotelPOINT (121.04515 14.56449)
      4240789592907camera_surveillanceNonePOINT (121.05945 14.60098)
      ..................
      6740045841294892502bakeryCome-on bakeryPOINT (123.26424 9.19066)
      6740145841301902401hotelL-mansionPOINT (121.00280 14.55968)
      6740245841645922721attractionLugnason FallsPOINT (123.53406 9.14808)
      6740345842153912742viewpointNonePOINT (123.53406 9.14804)
      6740445842530972511convenienceHerlyn's Burger HousePOINT (120.58250 14.99107)
      
+
+
      67405 rows × 5 columns
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Downloading Ookla Data
      
+
      
+
      import geopandas as gpd
+import pandas as pd
+
+from geowrangler.datasets import ookla
      
+
      
+
      
+
      Listing down available files
      
+
      
+
      ookla_files = ookla.list_ookla_files()
+ookla_files
      
+
      
+
      {OoklaQuarter(type='fixed', year='2019', quarter='1'): '2019-01-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2019', quarter='2'): '2019-04-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2019', quarter='3'): '2019-07-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2019', quarter='4'): '2019-10-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2020', quarter='1'): '2020-01-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2020', quarter='2'): '2020-04-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2020', quarter='3'): '2020-07-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2020', quarter='4'): '2020-10-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2021', quarter='1'): '2021-01-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2021', quarter='2'): '2021-04-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2021', quarter='3'): '2021-07-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2021', quarter='4'): '2021-10-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2022', quarter='1'): '2022-01-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2022', quarter='2'): '2022-04-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2022', quarter='3'): '2022-07-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='fixed', year='2022', quarter='4'): '2022-10-01_performance_fixed_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2019', quarter='1'): '2019-01-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2019', quarter='2'): '2019-04-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2019', quarter='3'): '2019-07-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2019', quarter='4'): '2019-10-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2020', quarter='1'): '2020-01-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2020', quarter='2'): '2020-04-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2020', quarter='3'): '2020-07-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2020', quarter='4'): '2020-10-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2021', quarter='1'): '2021-01-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2021', quarter='2'): '2021-04-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2021', quarter='3'): '2021-07-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2021', quarter='4'): '2021-10-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2022', quarter='1'): '2022-01-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2022', quarter='2'): '2022-04-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2022', quarter='3'): '2022-07-01_performance_mobile_tiles.parquet',
+ OoklaQuarter(type='mobile', year='2022', quarter='4'): '2022-10-01_performance_mobile_tiles.parquet'}
      
+
      
+
      
+
      
+
      
+
      Downloading an ookla file to a directory
      
+
      
+
      
+
      
+
+
      
+
      
+Warning
+
      
+
      
+
      
+
      Ookla files are >300MB and can reach ~550MB. Download with caution.
      
+
      
+
      
+
      
+
      !mkdir -p ../test_dir
+downloaded_file = ookla.download_ookla_file(
+    type_="fixed", year="2019", quarter="1", directory="../test_dir"
+)
+downloaded_file
      
+
      
+
      Path('../test_dir/2019-01-01_performance_fixed_tiles.parquet')
      
+
      
+
      
+
      
+
      
+
      Loading ookla data
      
+
      
+
      df = pd.read_parquet(downloaded_file)
+df.head()
      
+
      
+
      
+
      
+
+
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+
      quadkeytileavg_d_kbpsavg_u_kbpsavg_lat_mstestsdevices
      00022133222312322POLYGON((-160.02685546875 70.6435894914449, -1...876336464511
      10022133222330013POLYGON((-160.032348632812 70.6399478155463, -...919533474311
      20022133222330023POLYGON((-160.043334960938 70.6363054807905, -...683337884211
      30022133222330100POLYGON((-160.02685546875 70.6417687358462, -1...889534294322
      40022320121121332POLYGON((-166.739501953125 68.3526207780586, -...48779354532
      
+
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+
      
+
      
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+
       
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+
+
\ No newline at end of file
diff --git a/tutorial.dhs.html b/tutorial.dhs.html
new file mode 100644
index 0000000..4df55ba
--- /dev/null
+++ b/tutorial.dhs.html
@@ -0,0 +1,2140 @@
+
+
+
+
+
+
+
+
+
+geowrangler - DHS Wealth Index
+
+
+
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+
      DHS Wealth Index
      
+
      
+
+
+
+
      
+
+    
+  
+    
+  
      
+  
+
+
+
      
+
+
+
+
      
+
      Basic introduction to processing DHS data
      
+
      
+
      
+
      
+
      Loading DHS Data
      
+
      Note: This data is mocked data. Request official data from their available datasets through their website.
      
+
      
+
      import geopandas as gpd
+import pandas as pd
+
+from geowrangler import dhs
      
+
      
+
      
+
      dhs_household_data_path = "../data/ph.DTA"
+dhs_gps_coordinates = "../data/ph_gps.shp"
      
+
      
+
      
+
      dhs_df = dhs.load_dhs_file(dhs_household_data_path)
      
+
      
+
      CPU times: user 11.2 ms, sys: 583 µs, total: 11.8 ms
+Wall time: 10.6 ms
      
+
      
+
      
+
      
+
      dhs_df.head()
      
+
      
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      country code and phasecluster numbersource of drinking watertype of toilet facilityhas electricityhas radiohas televisionhas refrigeratorhas motorcycle/scooterhas car/truckmain floor materialnumber of rooms used for sleepinghas mobile telephonewealth index factor score combined (5 decimals)
      00PH7725806101011174200-168581
      11PH71009199211111051151127550
      22PH710729158110111265032616
      33PH724239930100017611080338
      44PH7102112700111056150-178758
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      Renaming columns to match
      
+
      DHS files do not have uniform column names. To make analysis easier, we rename commonnly ones. Feel free to extend the config to your usecase.
      
+
      
+
      ph_config = dhs.load_column_config("ph")
+ph_config
      
+
      
+
      {'cluster number': 'DHSCLUST',
+ 'wealth index factor score combined (5 decimals)': 'Wealth Index',
+ 'country code and phase': 'country code and phase',
+ 'number of rooms used for sleeping': 'rooms',
+ 'has electricity': 'electric',
+ 'has mobile telephone': 'mobile telephone',
+ 'has radio': 'radio',
+ 'has television': 'television',
+ 'has car/truck': 'car/truck',
+ 'has refrigerator': 'refrigerator',
+ 'has motorcycle/scooter': 'motorcycle',
+ 'main floor material': 'floor',
+ 'type of toilet facility': 'toilet',
+ 'source of drinking water': 'drinking water'}
      
+
      
+
      
+
      
+
      renamed_dhs_df = dhs_df.rename(columns=ph_config)
      
+
      
+
      
+
      renamed_dhs_df.head()
      
+
      
+
      
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      country code and phaseDHSCLUSTdrinking watertoiletelectricradiotelevisionrefrigeratormotorcyclecar/truckfloorroomsmobile telephoneWealth Index
      00PH7725806101011174200-168581
      11PH71009199211111051151127550
      22PH710729158110111265032616
      33PH724239930100017611080338
      44PH7102112700111056150-178758
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      Cluster Summaries
      
+
      
+
      wealth_col_name = "Wealth Index"
+cluster_col_name = "DHSCLUST"
      
+
      
+
      
+
      summarized = (
+    renamed_dhs_df[[wealth_col_name, cluster_col_name]].groupby(cluster_col_name).mean()
+)
+summarized.reset_index(inplace=True)
      
+
      
+
      
+
      summarized.head()
      
+
      
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+
      DHSCLUSTWealth Index
      03-232188.0
      14228860.0
      25157620.0
      3640308.5
      47-37595.5
      
+
+
      
+
      
+
      
+
      
+
      
+
      len(summarized)
      
+
      
+
      679
      
+
      
+
      
+
      
+
      summarized.DHSCLUST.isna().sum()
      
+
      
+
      0
      
+
      
+
      
+
      
+
      summarized.DHSCLUST.isna().sum()
      
+
      
+
      0
      
+
      
+
      
+
      
+
      dhs_shp = gpd.read_file(dhs_gps_coordinates)
      
+
      
+
      
+
      dhs_shp.head()
      
+
      
+
      
+
      
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+
      DHSIDDHSCCDHSYEARDHSCLUSTLATNUMLONGNUMgeometry
      0PH20XX725PH07250.4094410.220510POINT (0.22051 0.40944)
      1PH20XX1009PH010090.3336930.332499POINT (0.33250 0.33369)
      2PH20XX1072PH010720.3780530.089852POINT (0.08985 0.37805)
      3PH20XX242PH02420.3062770.431677POINT (0.43168 0.30628)
      4PH20XX102PH01020.5354560.716025POINT (0.71602 0.53546)
      
+
+
      
+
      
+
      
+
      
+
      
+
      survey_geo = pd.merge(summarized, dhs_shp, on="DHSCLUST")
+survey_geo
      
+
      
+
      
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+
      DHSCLUSTWealth IndexDHSIDDHSCCDHSYEARLATNUMLONGNUMgeometry
      03-232188.000000PH20XX003PH00.6099450.350830POINT (0.35083 0.60995)
      14228860.000000PH20XX004PH00.3638430.281563POINT (0.28156 0.36384)
      25157620.000000PH20XX005PH00.7154380.145014POINT (0.14501 0.71544)
      3640308.500000PH20XX006PH00.7585010.628373POINT (0.62837 0.75850)
      4640308.500000PH20XX006PH00.6694150.479379POINT (0.47938 0.66942)
      ...........................
      995124790273.333333PH20XX1247PH00.2146820.419448POINT (0.41945 0.21468)
      9961248212729.500000PH20XX1248PH00.1894010.152806POINT (0.15281 0.18940)
      9971248212729.500000PH20XX1248PH00.5634600.023900POINT (0.02390 0.56346)
      9981250101508.500000PH20XX1250PH00.1664650.617584POINT (0.61758 0.16646)
      9991250101508.500000PH20XX1250PH00.2524380.668243POINT (0.66824 0.25244)
      
+
+
      1000 rows × 8 columns
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Recalculating wealth index for a single country
      
+
      
+
      features = [
+    "rooms",
+    "electric",
+    "mobile telephone",
+    "radio",
+    "television",
+    "car/truck",
+    "refrigerator",
+    "motorcycle",
+    "floor",
+    "toilet",
+    "drinking water",
+]
      
+
      
+
      
+
      # apply a threshold
+dhs.apply_threshold(renamed_dhs_df, columns=features, config={"rooms": [0, 25]})
      
+
      
+
      
+
      
+
+
+
+
+
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      country code and phaseDHSCLUSTdrinking watertoiletelectricradiotelevisionrefrigeratormotorcyclecar/truckfloorroomsmobile telephoneWealth Index
      00PH7725806101011174200-168581
      11PH71009199211111051151127550
      22PH710729158110111265032616
      33PH724239930100017611080338
      44PH7102112700111056150-178758
      ................................................
      995995PH7834638301111128100241846
      996996PH71170747810111034171106424
      997997PH7762811411011083121246898
      998998PH76340210111111221-212590
      999999PH774856381110116971-10774
      
+
+
      1000 rows × 15 columns
      
+
      
+
      
+
      
+
      
+
      
+
      renamed_dhs_df["Recomputed Wealth Index"] = dhs.assign_wealth_index(
+    renamed_dhs_df[features], features
+)
+renamed_dhs_df.head()
      
+
      
+
      
+
      
+
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+
      country code and phaseDHSCLUSTdrinking watertoiletelectricradiotelevisionrefrigeratormotorcyclecar/truckfloorroomsmobile telephoneWealth IndexRecomputed Wealth Index
      00PH7725806101011174200-168581-63.515731
      11PH710091992111110511511275508.730482
      22PH710729158110111265032616-60.121255
      33PH724239930100017611080338-14.959946
      44PH7102112700111056150-178758-14.060453
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      Recalculating wealth index for multiple countries
      
+
      
+
      dhs_ph_path = "../data/ph.DTA"
+dhs_kh_path = "../data/kh.DTA"
+dhs_mm_path = "../data/mm.DTA"
+dhs_tl_path = "../data/tl.DTA"
+ph_config = dhs.load_column_config("ph")
+kh_config = dhs.load_column_config("kh")
+mm_config = dhs.load_column_config("mm")
+tl_config = dhs.load_column_config("tl")
+dhs_ph_df = dhs.load_dhs_file(dhs_ph_path).rename(columns=ph_config)
+dhs_kh_df = dhs.load_dhs_file(dhs_kh_path).rename(columns=kh_config)
+dhs_mm_df = dhs.load_dhs_file(dhs_mm_path).rename(columns=mm_config)
+dhs_tl_df = dhs.load_dhs_file(dhs_tl_path).rename(columns=tl_config)
      
+
      
+
      
+
      cols = list(ph_config.values()) + ["country code and phase"]
+merged_df = pd.concat(
+    [
+        dhs_ph_df[cols],
+        dhs_kh_df[cols],
+        dhs_mm_df[cols],
+        dhs_tl_df[cols],
+    ]
+)
+merged_df = merged_df.fillna(0)
      
+
      
+
      
+
      merged_df["Recomputed Wealth Index"] = dhs.assign_wealth_index(merged_df[features])
+merged_df.head()
      
+
      
+
      
+
      
+
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+
      DHSCLUSTWealth Indexcountry code and phaseroomselectricmobile telephoneradiotelevisioncar/truckrefrigeratormotorcyclefloortoiletdrinking watercountry code and phaseRecomputed Wealth Index
      0725-168581PH7200010111746180.0PH7122.888238
      11009127550PH7151111011519219.0PH778.430888
      2107232616PH751010111265891.0PH7108.113989
      324280338PH7110010100769339.0PH7105.464687
      4102-178758PH7150001011562711.0PH745.993876
      
+
+
      
+
      
+
      
+
      
+
      
+
      import matplotlib.pyplot as plt  # noqa
      
+
      
+
      
+
      merged_df.hist("Recomputed Wealth Index")
      
+
      
+
      array([[<Axes: title={'center': 'Recomputed Wealth Index'}>]],
+      dtype=object)
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      merged_df["Recomputed Wealth Index Not PCA"] = dhs.assign_wealth_index(
+    merged_df[features], use_pca=False
+)
+merged_df.head()
      
+
      
+
      
+
      
+
+
+
+
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+
      DHSCLUSTWealth Indexcountry code and phaseroomselectricmobile telephoneradiotelevisioncar/truckrefrigeratormotorcyclefloortoiletdrinking watercountry code and phaseRecomputed Wealth IndexRecomputed Wealth Index Not PCA
      0725-168581PH7200010111746180.0PH7122.888238-10730.578127
      11009127550PH7151111011519219.0PH778.430888-57116.290440
      2107232616PH751010111265891.0PH7108.11398930004.584457
      324280338PH7110010100769339.0PH7105.464687-48966.528484
      4102-178758PH7150001011562711.0PH745.993876-39722.455483
      
+
+
      
+
      
+
      
+
      
+
      
+
      merged_df.hist("Recomputed Wealth Index Not PCA")
      
+
      
+
      array([[<Axes: title={'center': 'Recomputed Wealth Index Not PCA'}>]],
+      dtype=object)
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
+
+
      
+
+
       
+
+
       
+
+
+
+
+
\ No newline at end of file
diff --git a/tutorial.dhs_files/figure-html/cell-28-output-2.png b/tutorial.dhs_files/figure-html/cell-28-output-2.png
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diff --git a/tutorial.distance_zonal_stats.html b/tutorial.distance_zonal_stats.html
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+geowrangler - Vector Distance Zonal Stats Tutorial
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+
      Vector Distance Zonal Stats Tutorial
      
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      A basic introduction to vector distance zonal stats
      
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      Basic Usage
      
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      Generate area zonal stats for a GeoDataframe containing areas of interest with a vector data source with the nearest distance.
      
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+Note
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      The data geometries can be points, lines or areas with distance computed from the closest points on both the aoi and the data source. Both the aoi and the data source must be projected using a “planar” CRS.
      
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+
      import geopandas as gpd
+import matplotlib.pyplot as plt
+from shapely.geometry import Point
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+import geowrangler.distance_zonal_stats as dzs
      
+
      
+
      
+
      Simple Grid AOIs and Nearby POI/Area Data Example
      
+
      
+
      simple_aoi = gpd.read_file("../data/simple_planar_aoi.geojson")
+simple_data = gpd.read_file("../data/simple_planar_data.geojson")
      
+
      
+
      
+
      simple_point_data = make_point_df(3, 5, offset_x=0.5, offset_y=3.0)
      
+
      
+
      Given an aoi (simple_aoi), a sample area data source (simple_data), and a sample point data source
      
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      simple_aoi
      
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      geometry
      0POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...
      1POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...
      2POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...
      
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      simple_data
      
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      populationinternet_speedgeometry
      010020.0POLYGON ((0.250 0.000, 0.250 1.000, 1.250 1.00...
      120010.0POLYGON ((1.250 0.000, 1.250 1.000, 2.250 1.00...
      23005.0POLYGON ((2.250 0.000, 2.250 1.000, 3.250 1.00...
      
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      simple_point_data
      
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      geometrypopulationinternet_speed
      0POINT (0.500 3.000)10020.0
      1POINT (0.500 4.000)600120.0
      2POINT (0.500 5.000)1100220.0
      3POINT (0.500 6.000)1600320.0
      4POINT (0.500 7.000)2100420.0
      5POINT (1.500 3.000)20010.0
      6POINT (1.500 4.000)700110.0
      7POINT (1.500 5.000)1200210.0
      8POINT (1.500 6.000)1700310.0
      9POINT (1.500 7.000)2200410.0
      10POINT (2.500 3.000)3005.0
      11POINT (2.500 4.000)800105.0
      12POINT (2.500 5.000)1300205.0
      13POINT (2.500 6.000)1800305.0
      14POINT (2.500 7.000)2300405.0
      
+
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+
      In order correctly compute distances from the aoi to the data sources, we need to make sure that the aoi, data and point data geodataframes are using a planar CRS (i.e. gdf.crs.is_geographic == False)
      
+
      
+
      simple_aoi.crs
      
+
      
+
      <Projected CRS: EPSG:3857>
+Name: WGS 84 / Pseudo-Mercator
+Axis Info [cartesian]:
+- X[east]: Easting (metre)
+- Y[north]: Northing (metre)
+Area of Use:
+- name: World between 85.06°S and 85.06°N.
+- bounds: (-180.0, -85.06, 180.0, 85.06)
+Coordinate Operation:
+- name: Popular Visualisation Pseudo-Mercator
+- method: Popular Visualisation Pseudo Mercator
+Datum: World Geodetic System 1984 ensemble
+- Ellipsoid: WGS 84
+- Prime Meridian: Greenwich
      
+
      
+
      
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+
      simple_data.crs
      
+
      
+
      <Projected CRS: EPSG:3857>
+Name: WGS 84 / Pseudo-Mercator
+Axis Info [cartesian]:
+- X[east]: Easting (metre)
+- Y[north]: Northing (metre)
+Area of Use:
+- name: World between 85.06°S and 85.06°N.
+- bounds: (-180.0, -85.06, 180.0, 85.06)
+Coordinate Operation:
+- name: Popular Visualisation Pseudo-Mercator
+- method: Popular Visualisation Pseudo Mercator
+Datum: World Geodetic System 1984 ensemble
+- Ellipsoid: WGS 84
+- Prime Meridian: Greenwich
      
+
      
+
      
+
      
+
      simple_point_data.crs
      
+
      
+
      <Projected CRS: EPSG:3857>
+Name: WGS 84 / Pseudo-Mercator
+Axis Info [cartesian]:
+- X[east]: Easting (metre)
+- Y[north]: Northing (metre)
+Area of Use:
+- name: World between 85.06°S and 85.06°N.
+- bounds: (-180.0, -85.06, 180.0, 85.06)
+Coordinate Operation:
+- name: Popular Visualisation Pseudo-Mercator
+- method: Popular Visualisation Pseudo Mercator
+Datum: World Geodetic System 1984 ensemble
+- Ellipsoid: WGS 84
+- Prime Meridian: Greenwich
      
+
      
+
      
+
      We have an aoi (simple_aoi) and geodataframe containing sample data (simple_data) that overlaps the aoi. We also have simple point data which do not intersect with our AOIs.
      
+
      
+
      ax = plt.axes()
+ax = simple_data.plot(
+    ax=ax, color=["orange", "brown", "purple"], edgecolor="yellow", alpha=0.4
+)
+ax = simple_aoi.plot(ax=ax, facecolor="none", edgecolor=["r", "g", "b"])
+ax = simple_point_data.plot(ax=ax)
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      The red,green,blue outlines are the 3 regions of interest (aoi) while the orange,brown, purple areas are the data areas.The blue dots are data which do not intersect our AOIs.
      
+
      
+
      results = dzs.create_distance_zonal_stats(
+    simple_aoi,
+    simple_point_data,
+    max_distance=7,
+    aggregations=[
+        dict(func="count"),
+        dict(func="sum", column="population"),
+        dict(func="mean", column="internet_speed"),
+    ],
+)
      
+
      
+
      CPU times: user 50.6 ms, sys: 0 ns, total: 50.6 ms
+Wall time: 47.8 ms
      
+
      
+
      
+
      The zonal stats computed for the point data only includes those points nearest to each aoi. The data geometries within nearest distance (within 7.0 m) are the only ones considered.
      
+
      
+
      
+
      
+
+
      
+
      
+Note
+
      
+
      
+
      
+
      Setting the max_distance to None or a large value can cause a possible slowdown for large datasets. See this Geopandas reference for more details.
      
+
      
+
      
+
      
+
      results
      
+
      
+
      
+
      
+
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+
+
+
+
      geometryindex_countpopulation_suminternet_speed_meannearest
      0POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...110020.02.0
      1POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...120010.02.0
      2POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...13005.02.0
      
+
+
      
+
      
+
      
+
      
+
      Data areas and geometries which overlap the aoi areas have a distance of 0.0 and are always the nearest geometries.
      
+
      
+
      results2 = dzs.create_distance_zonal_stats(
+    simple_aoi,
+    simple_data,
+    max_distance=1,
+    aggregations=[
+        dict(func="count"),
+        dict(func="sum", column="population"),
+        dict(func="mean", column="internet_speed"),
+    ],
+)
      
+
      
+
      CPU times: user 33.2 ms, sys: 1.29 ms, total: 34.5 ms
+Wall time: 29.5 ms
      
+
      
+
      
+
      
+
      results2
      
+
      
+
      
+
      
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+
+
+
+
+
      geometryindex_countpopulation_suminternet_speed_meannearest
      0POLYGON ((0.000 0.000, 0.000 1.000, 1.000 1.00...110020.00.0
      1POLYGON ((1.000 0.000, 1.000 1.000, 2.000 1.00...230015.00.0
      2POLYGON ((2.000 0.000, 2.000 1.000, 3.000 1.00...25007.50.0
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      Custom Grids and POIs Example
      
+
      
+
      region3_admin_grids = gpd.read_file("../data/region3_admin_grids.geojson")
      
+
      
+
      CPU times: user 97.8 ms, sys: 29.2 ms, total: 127 ms
+Wall time: 100 ms
      
+
      
+
      
+
      
+
      region34ncr_osm_pois = gpd.read_file("../data/region34ncr_osm_pois.geojson")
      
+
      
+
      CPU times: user 115 ms, sys: 365 µs, total: 115 ms
+Wall time: 113 ms
      
+
      
+
      
+
      
+
      ax = plt.axes()
+ax = region34ncr_osm_pois.plot(ax=ax)
+ax = region3_admin_grids.plot(ax=ax, facecolor="none", edgecolor="blue")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      region3_admin_grids = region3_admin_grids.to_crs("EPSG:3857")  # convert to planar
+region34ncr_osm_pois = region34ncr_osm_pois.to_crs("EPSG:3857")
      
+
      
+
      
+
      region34ncr_osm_pois
      
+
      
+
      
+
      
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+
+
+
      osm_idcodefclassnameBARANGAY_CODEgeometry
      03115684282701tourist_infoManila American Cemetery and Memorial Visitor ...137602022POINT (13475059.129 1636701.151)
      16725654962701tourist_infoecopark paging and first aid station137404141POINT (13477983.748 1656000.058)
      26725654982701tourist_infoEcopark ticket counter137404141POINT (13477813.318 1656135.868)
      315853895442701tourist_infoArea Formerly Occupied by Fort Bonifacio Museum137602021POINT (13476156.594 1637474.593)
      418348554242701tourist_infoLotto Booth137601020POINT (13468786.053 1622805.175)
      .....................
      282912827906362723monumentRizal Monument030808004POINT (13387876.843 1652143.344)
      283014307529672723monumentRizal Monument034903092POINT (13464327.852 1743609.163)
      283122804921172723monumentRizal Monument031421037POINT (13467118.142 1705384.453)
      283248987742232723monumentRizal Monument035414009POINT (13434387.128 1685457.796)
      283330875225572724memorialRizal Monument031410015POINT (13448861.946 1672777.772)
      
+
+
      2834 rows × 6 columns
      
+
      
+
      
+
      
+
      
+
      
+
      results3 = dzs.create_distance_zonal_stats(
+    region3_admin_grids,
+    region34ncr_osm_pois,
+    max_distance=10_000,  # within 10km
+    aggregations=[dict(func="count", output="pois_count", fillna=[True])],
+)
      
+
      
+
      CPU times: user 64.3 ms, sys: 6.26 ms, total: 70.5 ms
+Wall time: 67.8 ms
      
+
      
+
      
+
      
+
      len(results3[results3.pois_count == 0.0])
      
+
      
+
      165
      
+
      
+
      
+
      
+
      results3
      
+
      
+
      
+
      
+
+
+
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+
+
      xygeometrypois_countnearest
      0030POLYGON ((13334497.956 1771012.807, 13339497.9...1.08849.591855
      1031POLYGON ((13334497.956 1776012.807, 13339497.9...1.08844.448848
      2032POLYGON ((13334497.956 1781012.807, 13339497.9...0.0NaN
      3130POLYGON ((13339497.956 1771012.807, 13344497.9...1.03856.266007
      4132POLYGON ((13339497.956 1781012.807, 13344497.9...1.06070.762498
      ..................
      10695444POLYGON ((13604497.956 1841012.807, 13609497.9...1.0674.134053
      10705445POLYGON ((13604497.956 1846012.807, 13609497.9...2.00.000000
      10715446POLYGON ((13604497.956 1851012.807, 13609497.9...1.04237.158313
      10725447POLYGON ((13604497.956 1856012.807, 13609497.9...1.09237.158313
      10735448POLYGON ((13604497.956 1861012.807, 13609497.9...0.0NaN
      
+
+
      1074 rows × 5 columns
      
+
      
+
      
+
      
+
      
+
+
+
      
+
      
+
+
       
+
+
       
+
+
+
+
+
\ No newline at end of file
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diff --git a/tutorial.geometry_validation.html b/tutorial.geometry_validation.html
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+geowrangler - Geometry Validation Tutorial
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      Geometry Validation Tutorial
      
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+
      A basic introduction to using geometry validation
      
+
      
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+
      Basic Usage
      
+
      Loading a geojson with invalid geometries
      
+
      
+
      import geopandas as gpd
+import pandas as pd
      
+
      
+
      
+
      gdf = gpd.read_file("../data/broken.geojson")
+gdf = pd.concat([gdf, gpd.GeoDataFrame({"geometry": [None], "id": "null geometry"})])
+gdf
      
+
      
+
      /home/jt/.cache/pypoetry/virtualenvs/geowrangler-U9oiUrW5-py3.9/lib/python3.9/site-packages/geopandas/_compat.py:111: UserWarning: The Shapely GEOS version (3.10.2-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.
+  warnings.warn(
      
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      idgeometry
      0validPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 0....
      1out_of_crs_boundsPOLYGON ((200.00000 0.00000, 0.00000 1.00000, ...
      2misorientedPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....
      3self_intersectingPOLYGON ((0.00000 0.00000, 1.00000 1.00000, 2....
      4counterclockwisePOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....
      5sliverPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 0....
      6holesPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....
      7non-closed polygonPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....
      8multipolygonMULTIPOLYGON (((0.00000 0.00000, 1.00000 0.000...
      9polygon zPOLYGON Z ((0.00000 0.00000 1.00000, 1.00000 0...
      10complex self-intersecting polygonPOLYGON ((0.00000 0.00000, 5.00000 0.00000, 5....
      0null geometryNone
      
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+
      We then run Geometry Validation. By default, these append a new column if the validation fails, applies a fix if possible, and raises a warning if no fix is available.
      
+
      
+
      from geowrangler.validation import GeometryValidation
      
+
      
+
      
+
      GeometryValidation(gdf)
+
+validated_gdf = GeometryValidation(gdf).validate_all()
+validated_gdf
      
+
      
+
      /home/jt/repos/geowrangler/geowrangler/validation.py:107: UserWarning: Found null geometries
+  warnings.warn(self.warning_message)
+/home/jt/repos/geowrangler/geowrangler/validation.py:107: UserWarning: Found geometries out of bounds from crs
+  warnings.warn(self.warning_message)
+/home/jt/repos/geowrangler/geowrangler/validation.py:107: UserWarning: Found geometries with area equals or less than zero
+  warnings.warn(self.warning_message)
      
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      idgeometryis_not_nullis_not_self_intersectingis_oriented_properlyis_within_crs_boundsarea_is_not_zero
      0validPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 0....TrueTrueTrueTrueTrue
      1out_of_crs_boundsPOLYGON ((200.00000 0.00000, 0.00000 1.00000, ...TrueTrueTrueFalseTrue
      2misorientedPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....TrueTrueFalseTrueTrue
      3self_intersectingMULTIPOLYGON (((0.00000 2.00000, 0.00000 0.000...TrueFalseFalseTrueTrue
      4counterclockwisePOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....TrueTrueTrueTrueTrue
      5sliverPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 0....TrueTrueTrueTrueFalse
      6holesPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....TrueTrueTrueTrueTrue
      7non-closed polygonPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....TrueTrueTrueTrueTrue
      8multipolygonMULTIPOLYGON (((0.00000 0.00000, 1.00000 0.000...TrueTrueTrueTrueTrue
      9polygon zPOLYGON Z ((0.00000 0.00000 1.00000, 1.00000 0...TrueTrueTrueTrueTrue
      10complex self-intersecting polygonPOLYGON ((0.00000 5.00000, 0.00000 2.50000, 0....TrueFalseFalseTrueTrue
      0null geometryNoneFalseTrueTrueTrueTrue
      
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      gdf.iloc[5].geometry.area
      
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      0.0
      
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+
      Running the validation again shows that validation applies some fixes
      
+
      
+
      GeometryValidation(validated_gdf[["id", "geometry"]]).validate_all()
      
+
      
+
      /home/jt/repos/geowrangler/geowrangler/validation.py:107: UserWarning: Found null geometries
+  warnings.warn(self.warning_message)
+/home/jt/repos/geowrangler/geowrangler/validation.py:107: UserWarning: Found geometries out of bounds from crs
+  warnings.warn(self.warning_message)
+/home/jt/repos/geowrangler/geowrangler/validation.py:107: UserWarning: Found geometries with area equals or less than zero
+  warnings.warn(self.warning_message)
      
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      idgeometryis_not_nullis_not_self_intersectingis_oriented_properlyis_within_crs_boundsarea_is_not_zero
      0validPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 0....TrueTrueTrueTrueTrue
      1out_of_crs_boundsPOLYGON ((200.00000 0.00000, 0.00000 1.00000, ...TrueTrueTrueFalseTrue
      2misorientedPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....TrueTrueTrueTrueTrue
      3self_intersectingMULTIPOLYGON (((0.00000 2.00000, 0.00000 0.000...TrueTrueTrueTrueTrue
      4counterclockwisePOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....TrueTrueTrueTrueTrue
      5sliverPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 0....TrueTrueTrueTrueFalse
      6holesPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....TrueTrueTrueTrueTrue
      7non-closed polygonPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....TrueTrueTrueTrueTrue
      8multipolygonMULTIPOLYGON (((0.00000 0.00000, 1.00000 0.000...TrueTrueTrueTrueTrue
      9polygon zPOLYGON Z ((0.00000 0.00000 1.00000, 1.00000 0...TrueTrueTrueTrueTrue
      10complex self-intersecting polygonPOLYGON ((0.00000 5.00000, 0.00000 2.50000, 0....TrueTrueTrueTrueTrue
      0null geometryNoneFalseTrueTrueTrueTrue
      
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      Passing Validators
      
+
      You can pass a list of Validators to selective run validators, the default uses the following - NullValidator - Checks if geometry is null. No fix - OrientationValidator - Check the orientation of the outer most ring of each polygon is counter clockwise. Converts it to counter-clockwise if as the fix - SelfIntersectingValidator - Checks if the polygons is self intersecting. Runs shapely.validation.make_valid as the fix. - CrsBoundsValidator - Checks if bounds of each geometry are within the CRS. No fix - AreaValidator - Checks if polygons or multipolygon have an area greater than zero
      
+
      
+
      from geowrangler.validation import NullValidator, SelfIntersectingValidator
      
+
      
+
      
+
      validated_gdf = GeometryValidation(
+    gdf, validators=[NullValidator, SelfIntersectingValidator]
+).validate_all()
+validated_gdf
      
+
      
+
      /home/jt/repos/geowrangler/geowrangler/validation.py:107: UserWarning: Found null geometries
+  warnings.warn(self.warning_message)
      
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      idgeometryis_not_nullis_not_self_intersecting
      0validPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 0....TrueTrue
      1out_of_crs_boundsPOLYGON ((200.00000 0.00000, 0.00000 1.00000, ...TrueTrue
      2misorientedPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....TrueTrue
      3self_intersectingMULTIPOLYGON (((0.00000 2.00000, 1.00000 1.000...TrueFalse
      4counterclockwisePOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....TrueTrue
      5sliverPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 0....TrueTrue
      6holesPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....TrueTrue
      7non-closed polygonPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....TrueTrue
      8multipolygonMULTIPOLYGON (((0.00000 0.00000, 1.00000 0.000...TrueTrue
      9polygon zPOLYGON Z ((0.00000 0.00000 1.00000, 1.00000 0...TrueTrue
      10complex self-intersecting polygonPOLYGON ((0.00000 5.00000, 5.00000 5.00000, 5....TrueFalse
      0null geometryNoneFalseTrue
      
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      You can also use a single validator at a time
      
+
      
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      SelfIntersectingValidator().validate(gdf)
      
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+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
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+
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+
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+
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+
+
+
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+
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+
+
+
+
+
+
+
+
+
      idgeometryis_not_self_intersecting
      0validPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 0....True
      1out_of_crs_boundsPOLYGON ((200.00000 0.00000, 0.00000 1.00000, ...True
      2misorientedPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....True
      3self_intersectingMULTIPOLYGON (((0.00000 2.00000, 1.00000 1.000...False
      4counterclockwisePOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....True
      5sliverPOLYGON ((0.00000 0.00000, 0.00000 1.00000, 0....True
      6holesPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....True
      7non-closed polygonPOLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....True
      8multipolygonMULTIPOLYGON (((0.00000 0.00000, 1.00000 0.000...True
      9polygon zPOLYGON Z ((0.00000 0.00000 1.00000, 1.00000 0...True
      10complex self-intersecting polygonPOLYGON ((0.00000 5.00000, 5.00000 5.00000, 5....False
      0null geometryNoneTrue
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      Building your own validator
      
+
      Let’s build a validator that check if the is point below 0 in the x axis, if that is the case we set it to 0
      
+
      
+
      from shapely.geometry.point import Point
+from shapely.geometry.polygon import Polygon
+
+from geowrangler.validation import BaseValidator
      
+
      
+
      
+
      class PointValidator(BaseValidator):
+    validator_column_name = "is_not_point"
+    geometry_types = ["Point"]  # What kind of geometies to validate and fix
+
+    def check(self, geometry):
+        # Checks if the geometry is valid. If False, applies the fix
+        return geometry.x > 0
+
+    def fix(self, geometry):
+        return Point(0, geometry.y)
      
+
      
+
      
+
      gdf = gpd.GeoDataFrame(
+    geometry=[Point(-0.1, 0), Polygon([(-0.1, 0.1), (-0.1, 1), (1, 1)])]
+)
+validated_gdf = PointValidator().validate(gdf)
+
+ax = gdf.plot()
+ax = validated_gdf.plot()
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      There are several cases where no fix is available or you want to fix them manualy, we can create a validator that warns the users.
      
+
      
+
      from shapely.geometry.point import Point
+from shapely.geometry.polygon import Polygon
+
+from geowrangler.validation import BaseValidator
      
+
      
+
      
+
      class PointValidator(BaseValidator):
+    validator_column_name = "is_not_point"
+    fix_available = False  # Telling the validator that there is no available fixes
+    warning_message = "Found geometries that are points below 0"  # warning message
+    geometry_types = ["Point"]  # What kind of geometies to validate and fix
+
+    def check(self, geometry):
+        # Checks if the geometry is valid. If False, warn the user
+        return geometry.x > 0
+
+
+gdf = gpd.GeoDataFrame(geometry=[Point(-0.1, 0), Polygon([(0, 0.0), (0, 1), (1, 1)])])
+validated_gdf = PointValidator().validate(gdf)
+validated_gdf
      
+
      
+
      /home/jt/repos/geowrangler/geowrangler/validation.py:107: UserWarning: Found geometries that are points below 0
+  warnings.warn(self.warning_message)
      
+
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
      geometryis_not_point
      0POINT (-0.10000 0.00000)False
      1POLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....True
      
+
+
      
+
      
+
      
+
      
+
+
+
      
+
+
       
+
+
       
+
+
+
+
+
\ No newline at end of file
diff --git a/tutorial.geometry_validation_files/figure-html/cell-17-output-1.png b/tutorial.geometry_validation_files/figure-html/cell-17-output-1.png
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+
+
+
+
+
+
+
+
+geowrangler - Grid Generation Tutorial
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      
+  
      
+    
+  
+
      
+
+
      
+
+  
+
      
+
+    
+
+
      
+
+
      
+
      
+
      Grid Generation Tutorial
      
+
      
+
+
+
+
      
+
+    
+  
+    
+  
      
+  
+
+
+
      
+
+
+
+
      
+
      A basic introduction to geospatial grids
      
+
      
+
      
+
      
+
      Basic Usage
      
+
      Creating a grid from a geojson file
      
+
      
+
      Import geopandas and the geowrangler.grids module
      
+
      
+
      import geopandas as gpd
+import pandas as pd
+
+from geowrangler import grids
      
+
      
+
      
+
      
+
      Load a sample geojson file into pandas
      
+
      In our case, we are loading the Region 3 (Central Luzon Administrative Region) of the Philippines.
      
+
      
+
      region3_gdf = gpd.read_file("../data/region3_admin.geojson")
      
+
      
+
      This geopandas dataframe has the size:
      
+
      
+
      
+
      length of region3_gdf: 1 row(s)
      
+
      
+
      
+
      
+
      display(region3_gdf)
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      Reg_CodeReg_NameReg_Alt_Namegeometry
      0030000000Region IIICentral LuzonMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...
      
+
+
      
+
      
+
      
+
      
+
      Show the original plot
      
+
      
+
      import matplotlib.pyplot as plt
      
+
      
+
      
+
      ax = region3_gdf.plot(ax=plt.axes())
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      region3_gdf.crs  # CRS info
      
+
      
+
      <Geographic 2D CRS: EPSG:4326>
+Name: WGS 84
+Axis Info [ellipsoidal]:
+- Lat[north]: Geodetic latitude (degree)
+- Lon[east]: Geodetic longitude (degree)
+Area of Use:
+- name: World.
+- bounds: (-180.0, -90.0, 180.0, 90.0)
+Datum: World Geodetic System 1984 ensemble
+- Ellipsoid: WGS 84
+- Prime Meridian: Greenwich
      
+
      
+
      
+
      
+
      aoi_total_bounds = region3_gdf.total_bounds
+aoi_total_bounds
      
+
      
+
      array([119.7858332,  14.4075257, 122.2429921,  16.5092548])
      
+
      
+
      
+
      
+
      
+
      
+
      Square Grid Generator
      
+
      
+
      Creating Grids
      
+
      Create a grid generator with a size of 5,000 m. The units of the grid size are dependent on the projection parameter
      
+of the grid generator. In this case, the default is EPSG:3857.
      
+
      
+
      grids.SquareGridGenerator?
      
+
      
+
      Init signature:
+grids.SquareGridGenerator(
+    cell_size: float,
+    grid_projection: str = 'EPSG:3857',
+    boundary: Union[geowrangler.grids.SquareGridBoundary, List[float]] = None,
+)
+Docstring:      <no docstring>
+File:           ~/geowrangler/geowrangler/grids.py
+Type:           type
+Subclasses:     
      
+
      
+
      
+
      
+
      grid_generator5k = grids.SquareGridGenerator(5_000)  # 5 km x 5 km square cells
      
+
      
+
      Generate square grids > Notice the time taken to grid the multi polygon at 5K resolution
      
+
      
+
      # slow
+grid_gdf5k = grid_generator5k.generate_grid(region3_gdf)
      
+
      
+
      CPU times: user 614 ms, sys: 0 ns, total: 614 ms
+Wall time: 617 ms
      
+
      
+
      
+
      
+
      grid_gdf5k.plot()
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Show gridded version of sample geojson file at 5K resolution
      
+
      
+
      
+
      length of grid_gdf5k: 1074 row(s)
      
+
      
+
      
+
      
+
      # slow
+ax = region3_gdf.plot(facecolor="none", edgecolor="blue")
+ax = grid_gdf5k.plot(ax=ax, facecolor="none", edgecolor="green")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      In addition to the grid cells, there are 2 extra columns x and y when combined are unique per grid. It can also tell us which grids are adjacent to each other.
      
+
      
+
      # slow
+grid_gdf5k.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      xygeometry
      078POLYGON ((120.10024 14.75528, 120.14516 14.755...
      168POLYGON ((120.05533 14.75528, 120.10024 14.755...
      298POLYGON ((120.19008 14.75528, 120.23499 14.755...
      3224POLYGON ((119.87566 15.44910, 119.92058 15.449...
      4225POLYGON ((119.87566 15.49239, 119.92058 15.492...
      
+
+
      
+
      
+
      
+
      
+
      
+
      # slow
+ax = region3_gdf.plot(facecolor="none", edgecolor="blue")
+ax = grid_gdf5k[grid_gdf5k["x"] == 10].plot(ax=ax, facecolor="none", edgecolor="green")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Gridding at a coarser resolution
      
+
      Let’s try the same process but with a bigger grid (15K)
      
+
      
+
      grid_generator15k = grids.SquareGridGenerator(15_000)  # 15 km x 15 km grids
      
+
      
+
      Generate square grids > Notice the time taken to grid the multi polygon at 15K resolution (compared to 5K resolution)
      
+
      
+
      grid_gdf15k = grid_generator15k.generate_grid(region3_gdf)
      
+
      
+
      CPU times: user 548 ms, sys: 0 ns, total: 548 ms
+Wall time: 547 ms
      
+
      
+
      
+
      
+
      ax = region3_gdf.plot(facecolor="none", edgecolor="blue")
+grid_gdf15k.plot(ax=ax, facecolor="none", edgecolor="green")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Show gridded version of sample geojson file at 15K resolution
      
+
      
+
      
+
      length of grid_gdf15k: 154 row(s)
      
+
      
+
      
+
      
+
      
+
      Reusing boundaries
      
+
      If you have 2 polygons that are far from each other but wish to have them follow the reference same reference x and y, you can use pass in custom boudaries.
      
+
      Let’s load some grids that are from each other
      
+
      
+
      cell1 = grid_gdf15k.head(1)
+cell2 = grid_gdf15k.tail(1)
      
+
      
+
      
+
      pd.concat([cell1, cell2])
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      xygeometry
      022POLYGON ((120.05533 14.66839, 120.19008 14.668...
      1531816POLYGON ((122.21128 16.48548, 122.34603 16.485...
      
+
+
      
+
      
+
      
+
      
+
      
+
      ax = region3_gdf.plot(facecolor="none", edgecolor="red")
+ax = grid_gdf15k.plot(ax=ax, facecolor="none", edgecolor="green")
+ax = pd.concat([cell1, cell2]).plot(ax=ax)
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      grid_generator1k = grids.SquareGridGenerator(1_000, boundary=aoi_total_bounds)
      
+
      
+
      
+
      gridcell1 = grid_generator1k.generate_grid(cell1)
      
+
      
+
      CPU times: user 27.1 ms, sys: 0 ns, total: 27.1 ms
+Wall time: 27.8 ms
      
+
      
+
      
+
      
+
      len(gridcell1)
      
+
      
+
      272
      
+
      
+
      
+
      
+
      gridcell1.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      xygeometry
      02930POLYGON ((120.04634 14.66839, 120.05533 14.668...
      12931POLYGON ((120.04634 14.67708, 120.05533 14.677...
      22932POLYGON ((120.04634 14.68577, 120.05533 14.685...
      32933POLYGON ((120.04634 14.69446, 120.05533 14.694...
      42934POLYGON ((120.04634 14.70315, 120.05533 14.703...
      
+
+
      
+
      
+
      
+
      
+
      
+
      ax = gridcell1.plot(facecolor="none", edgecolor="green")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      gridcell2 = grid_generator1k.generate_grid(cell2)
      
+
      
+
      CPU times: user 20.2 ms, sys: 0 ns, total: 20.2 ms
+Wall time: 18.7 ms
      
+
      
+
      
+
      
+
      len(gridcell2)
      
+
      
+
      20
      
+
      
+
      
+
      
+
      gridcell2.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      xygeometry
      0269239POLYGON ((122.20230 16.47686, 122.21128 16.476...
      1269240POLYGON ((122.20230 16.48548, 122.21128 16.485...
      2269241POLYGON ((122.20230 16.49409, 122.21128 16.494...
      3269242POLYGON ((122.20230 16.50270, 122.21128 16.502...
      4270239POLYGON ((122.21128 16.47686, 122.22027 16.476...
      
+
+
      
+
      
+
      
+
      
+
      
+
      ax = gridcell2.plot(facecolor="none", edgecolor="red")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      ax = region3_gdf.plot(facecolor="none", edgecolor="blue")
+ax = gridcell1.plot(ax=ax, color="green")
+ax = gridcell2.plot(ax=ax, color="red")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Generating grids for multiple distant areas
      
+
      If you are using AOIs that are vary far from each other, Grid Generator already optmizes the implementation for you
      
+
      
+
      sparse_aois = grid_gdf15k.iloc[
+    0:1000:3,
+]  # Get areas that far from each other
+ax = region3_gdf.plot(facecolor="none", edgecolor="blue")
+ax = sparse_aois.plot(ax=ax, facecolor="none", edgecolor="green")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      sparse_grid = grid_generator1k.generate_grid(sparse_aois)
      
+
      
+
      
+
      ax = region3_gdf.plot(facecolor="none", edgecolor="blue")
+ax = sparse_grid.plot(ax=ax, facecolor="none", edgecolor="green")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      It is a bit hard to see the grids so, we get a subset to verify.
      
+
      
+
      ax = sparse_grid.head(1000).plot(facecolor="none", edgecolor="green")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      H3 Grid Generator
      
+
      
+
      Generating grids
      
+
      Let us generate grids of resolution 5. To learn more about the different resolution, visit: https://h3geo.org/docs/core-library/restable/
      
+
      
+
      h3_generator = grids.H3GridGenerator(resolution=5)
      
+
      
+
      
+
      h3_5_gdf = h3_generator.generate_grid(region3_gdf)
      
+
      
+
      CPU times: user 2.79 s, sys: 6.39 ms, total: 2.8 s
+Wall time: 2.81 s
      
+
      
+
      
+
      
+
      ax = region3_gdf.plot(aspect="equal")
+ax = h3_5_gdf.plot(ax=ax, facecolor="none", edgecolor="blue", aspect="equal")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Not exporting geometry
      
+
      If you do not plan to use use geometry, you can pass return_geometry=False to just get a normal pandas DataFrame with the ids.
      
+
      
+
      h3_generator_no_geom = grids.H3GridGenerator(resolution=5, return_geometry=False)
      
+
      
+
      
+
      h3_region3_no_geom = h3_generator_no_geom.generate_grid(region3_gdf)
      
+
      
+
      CPU times: user 2.75 s, sys: 26.9 ms, total: 2.78 s
+Wall time: 2.78 s
      
+
      
+
      
+
      
+
      len(h3_region3_no_geom)
      
+
      
+
      84
      
+
      
+
      
+
      
+
      h3_region3_no_geom.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      hex_id
      085694103fffffff
      1856941d7fffffff
      285694e83fffffff
      385694e93fffffff
      485694387fffffff
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Bing Tile Grid Generator
      
+
      
+
      Generating grids
      
+
      Let us generate grids of zoom_level 12. To learn more about the different resolution, visit: https://docs.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system
      
+
      
+
      bing_tile_grid_generator = grids.BingTileGridGenerator(12)
      
+
      
+
      
+
      # slow
+bing_tile_gdf = bing_tile_grid_generator.generate_grid(region3_gdf)
      
+
      
+
      CPU times: user 7.37 s, sys: 0 ns, total: 7.37 s
+Wall time: 7.37 s
      
+
      
+
      
+
      
+
      ax = region3_gdf.plot(facecolor="none", edgecolor="blue")
+ax = bing_tile_gdf.plot(ax=ax, facecolor="none", edgecolor="green")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Not exporting geometry
      
+
      If you do not plan to use use geometry, you can pass return_geometry=False to just get a normal pandas DataFrame with the ids.
      
+
      
+
      bing_tile_grid_generator_no_geom = grids.BingTileGridGenerator(
+    12, return_geometry=False
+)
      
+
      
+
      
+
      # slow
+bing_region3_keys = bing_tile_grid_generator_no_geom.generate_grid(region3_gdf)
      
+
      
+
      CPU times: user 7.45 s, sys: 7.19 ms, total: 7.45 s
+Wall time: 7.45 s
      
+
      
+
      
+
      
+
      bing_region3_keys.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      quadkey
      0132303030330
      1132303030331
      2132303030313
      3132303012302
      4132303012031
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      Converting quadkey to x,y,z format
      
+
      If you need to convert the quadkey to x,y format, you can pass add_xyz_cols=True to add the x and y columns to the returned dataframe.
      
+
      
+
      bing_tile_grid_generator_add_xyz = grids.BingTileGridGenerator(12, add_xyz_cols=True)
      
+
      
+
      
+
      # slow
+bing_region3_keys = bing_tile_grid_generator_add_xyz.generate_grid(region3_gdf)
      
+
      
+
      CPU times: user 8.85 s, sys: 20 ms, total: 8.87 s
+Wall time: 8.87 s
      
+
      
+
      
+
      
+
      bing_region3_keys.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      quadkeyxyzgeometry
      01323030303303414187812POLYGON ((120.05859 14.68988, 120.05859 14.774...
      11323030303313415187812POLYGON ((120.14648 14.68988, 120.14648 14.774...
      21323030303133415187712POLYGON ((120.14648 14.77488, 120.14648 14.859...
      31323030123023412186912POLYGON ((119.88281 15.45368, 119.88281 15.538...
      41323030120313411186612POLYGON ((119.79492 15.70766, 119.79492 15.792...
      
+
+
      
+
      
+
      
+
      
+
+
+
      
+
      
+
+
       
+
+
       
+
+
+
+
+
\ No newline at end of file
diff --git a/tutorial.grids_files/figure-html/cell-14-output-1.png b/tutorial.grids_files/figure-html/cell-14-output-1.png
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zG7Cp^>&yE4kA+*W=`+5~u19x%Xv$
+    
+  
      
+
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+
      
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+  
+
      
+
+    
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+
      
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+
      
+
      Raster Processing Tutorial
      
+
      
+
+
+
+
      
+
+    
+  
+    
+  
      
+  
+
+
+
      
+
+
+
+
      
+
      A basic introduction to raster processing
      
+
      
+
      
+
      
+
      Summary
      
+
      Cropping rasters based on the borders of a polygon. Usage of this function assumes an input raster and a geodataframe from which to extract bounds. There is also an option to crop multiple geometries at once (e.g., crop raster using bounds of each cell in a grid).
      
+
      
+
      
+
      How does it work?
      
+
      Returns a subset of a rasterio dataset. This function assumes that the bounding coordinates for the desired cropped rasters are present in the input geodataframe’s geometry column.
      
+
      
+
      query_window_by_polygon(raster, output_folder, gdf, mask)
      
+
      For cropping a raster based on boundaries of a single polygon. A technical step-by-step explanation of how query_window_by_polygon works is detailed in the cell blocks below.
      
+
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      typedefaultoptional/ requireddetails
      rasterStringnonerequiredlocal filename of raster or open raster dataset
      output_folderStringnonerequiredfile path where cropped raster outputs will be saved
      gdfGeoDataFramenonerequiredpolygon that will become basis of boundaries for cropping
      maskBooleannonerequiredTrue- Assign NULL to areas outside borders of a non-rectangular polygon  False- Retain values outside borders of a non-rectangular polygon
      
+
        
+
      1. Define the function and its arguments.
        2. 
+
      
+
      def query_window_by_polygon(
+    input_raster: Union[str, DatasetReader, PosixPath],
+    output_path: str,
+    geometry: Polygon,
+    mask=False,
+) -> None:
      
+
        
+
      1. Check if raster is specified as PosixPath and convert to String.
        2. 
+
      
+
          if isinstance(input_raster, PosixPath):
+        input_raster = str(input_raster)
      
+
        
+
      1. Check if raster is path to a file or an open raster dataset.
        2. 
+
      
+
          if isinstance(input_raster,str):
+         input_dset = rio.open(input_raster)
+    else:
+         input_dset = input_raster
      
+
        
+
      1. Get the window bounds (left, right, top, bottom coordinates) from polygon geometry and check if it has the correct number of elements.
        2. 
+
      
+
          window_bounds = geometry.bounds
+    assert (
+        len(window_bounds) == 4
+    ), 
      
+
        
+
      1. Unroll window bounds.
        2. 
+
      
+
          left, bottom, right, top = window_bounds
      
+
        
+
      1. Open raster as input.
        2. 
+
      
+
          with rio.open(input_raster) as input_dst:
      
+
        
+
      1. Get profile of input_dst:
        2. 
+
      
+
              input_profile = input_dst.profile
      
+
        
+
      1. Specify window and query subset.
        2. 
+
      
+
              window = rio.windows.from_bounds(left, bottom, right, top, input_dst.transform)
+        subset = input_dst.read(window=window)
      
+
        
+
      1. Get the shape of the output subset.
        2. 
+
      
+
              number_of_bands, height, width = subset.shape
      
+
        
+
      1. Get the transformation of the subset based on the window.
        2. 
+
      
+
              win_transform = input_dst.window_transform(window)
      
+
        
+
      1. Update metadata for the output.
        2. 
+
      
+
              output_profile = input_profile.copy()
+        update_params = {
+            "count": number_of_bands,
+            "height": height,
+            "width": width,
+            "transform": win_transform,
+        }
+        output_profile.update(update_params)
      
+
        
+
      1. Write image to output file.
        2. 
+
      
+
              with rio.open(output_path, "w", **output_profile) as output_dst:
+            output_dst.write(subset)
+            output_dst.colorinterp = input_dst.colorinterp
      
+
        
+
      1. Apply mask according to shape of the polygon.
        2. 
+
      
+
          if mask:
+        with rio.open(output_path) as dst:
+            masked_image, masked_transform = rio.mask.mask(dst, [geometry], crop=True)
+        with rio.open(output_path, "w", **output_profile) as output_dst:
+            update_params = {
+                "height": masked_image.shape[1],
+                "width": masked_image.shape[2],
+                "transform": masked_transform,
+            }
+            output_profile.update(update_params)
+            output_dst.write(masked_image)
      
+
        
+
      1. Return output.
        2. 
+
      
+
          return 
      
+
      
+
      
+
      query_window_by_gdf(raster, output_folder, gdf, name_col, mask)
      
+
      For cropping a raster based on boundaries of multiple polygons. A step-by-step explanation of how query_window_by_gdf works is detailed in the cell blocks below.
      
+
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+
      






      typedefaultoptional/ requireddetails
      rasterStringnonerequiredlocal filename of raster or open raster dataset
      output_folderStringnonerequiredfile path where cropped raster outputs will be saved
      gdfGeoDataFramenonerequiredpolygon that will become basis of boundaries for cropping
      name_colStringnoneoptionalcolumn name to base output filepath on. if left blank, outputs will be name sequentially as ‘output_0.tif’
      maskBooleannonerequiredTrue- Assign NULL to areas outside borders of a non-rectangular polygon  False- Retain values outside borders of a non-rectangular polygon
      
+
        
+
      1. Define the function and create a copy of the geodataframe.
        2. 
+
      
+
      def query_window_by_gdf(
+    input_raster: Union[str, DatasetReader, PosixPath],
+    output_folder: str,
+    gdf,
+    name_col=None,
+    mask=False,
+) -> None:
+    gdf = gdf.copy()
      
+
        
+
      1. Check if the coordinate reference systems of the raster and geodataframe match.
        2. 
+
      
+
          with rio.open(input_raster) as dst:
+        assert dst.meta["crs"] == gdf.crs, "input_raster and gdf CRS must match!"
      
+
        
+
      1. Name outputs based on values from name_col.
        2. 
+
      
+
          if name_col is None:
+        name_col = "name"
+        gdf[name_col] = "output_" + gdf.reset_index().index.astype(str) + ".tif"
+    else:
+        gdf[name_col] = gdf[name_col] + ".tif"
+
+    for i, row in gdf.iterrows():
+        polygon = row.geometry
+        output_name = row[name_col]
+        output_path = output_folder / output_name
+        print(output_path)
+        query_window_by_polygon(input_raster, output_path, polygon, mask)
      
+
      
+
      
+
      
+
      Sample use case 1- Crop raster with single circular polygon
      
+
      Input: - input_image - raster of 2020 Philippine population - output_folder - filepath to where output raster will be saved - circle_gdf - GeoDataFrame containining single circular polygon - mask - True- to assign NULL values to cells outside the circular polygon
      
+
      Output: - cropped raster (GeoTiff) containing only values within circular polygon
      
+
      
+
      warnings.filterwarnings("ignore", category=UserWarning, module="geopandas")
      
+
      
+
      
+
      Step 1: Import packages
      
+
      
+
      import rasterio as rio
+
+import geowrangler.raster_process as raster_process
      
+
      
+
      
+
      
+
      Step 2: Load raster
      
+
      
+
      input_image
      
+
      
+
      '../data/phl_ppp_2020_constrained.tif'
      
+
      
+
      
+
      
+
      raster = rio.open(input_image)
      
+
      
+
      
+
      show(raster.read(1), cmap="plasma", transform=raster.transform);
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Step 3: Load polygon
      
+
      
+
      circle_gdf
      
+
      
+
      
+
      
+
+
+
+
+
+
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+
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+
      latlongeometry
      014.599512120.984222POLYGON ((121.98422 14.59951, 121.97941 14.501...
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      Step 4: Check if the polygon is within bounds of the raster.
      
+
      
+
      fig, ax = plt.subplots(1, 1, figsize=(4, 8))
+show(raster.read(1), cmap="viridis", ax=ax, transform=raster.transform)
+circle_gdf.plot(ax=ax, facecolor="none", edgecolor="yellow")
+ax;
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Step 5: State filepath of output folder.
      
+
      
+
      output_folder = Path("../data")
      
+
      
+
      
+
      
+
      Step 6: Crop raster
      
+
      
+
      raster_process.query_window_by_gdf(input_image, output_folder, circle_gdf, mask=True)
      
+
      
+
      ../data/output_0.tif
      
+
      
+
      
+
      
+
      with rio.open(output_folder / "output_0.tif") as dst:
+    fig, ax = plt.subplots(1, 1, figsize=(4, 4))
+
+    show(dst.read(1), cmap="viridis", ax=ax, transform=dst.transform)
+    circle_gdf.plot(facecolor="none", edgecolor="yellow", ax=ax)
+    print(dst.read(1))
+ax;
      
+
      
+
      [[-99999. -99999. -99999. ... -99999. -99999. -99999.]
+ [-99999. -99999. -99999. ... -99999. -99999. -99999.]
+ [-99999. -99999. -99999. ... -99999. -99999. -99999.]
+ ...
+ [-99999. -99999. -99999. ... -99999. -99999. -99999.]
+ [-99999. -99999. -99999. ... -99999. -99999. -99999.]
+ [-99999. -99999. -99999. ... -99999. -99999. -99999.]]
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Sample use case 2- Crop raster with multiple cells from a grid
      
+
      Input: - input_image - raster of 2020 Philippine population - output_folder - filepath to where output rasters will be saved - grid_gdf - GeoDataFrame containining multiple polygons (grid cells) - mask - False- no need to assign NULL values because grid cells occupy full space from image center to boundaries
      
+
      Output: - multiple cropped rasters (GeoTiff) split according to grid cell polygons
      
+
      
+
      Step 1: Import packages
      
+
      
+
      import rasterio as rio
+
+import geowrangler.raster_process
      
+
      
+
      
+
      
+
      Step 2: Load raster
      
+
      
+
      input_image
      
+
      
+
      '../data/phl_ppp_2020_constrained.tif'
      
+
      
+
      
+
      
+
      raster = rio.open(input_image)
+show(raster.read(1), cmap="plasma", transform=raster.transform)
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Step 3: Load grid
      
+
      
+
      grid_gdf
      
+
      
+
      
+
      
+
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      xygeometryname
      000POLYGON ((119.98422 13.59951, 120.88254 13.599...gridxy-0-0
      101POLYGON ((119.98422 14.47100, 120.88254 14.471...gridxy-0-1
      202POLYGON ((119.98422 15.33908, 120.88254 15.339...gridxy-0-2
      310POLYGON ((120.88254 13.59951, 121.78085 13.599...gridxy-1-0
      411POLYGON ((120.88254 14.47100, 121.78085 14.471...gridxy-1-1
      512POLYGON ((120.88254 15.33908, 121.78085 15.339...gridxy-1-2
      620POLYGON ((121.78085 13.59951, 122.67917 13.599...gridxy-2-0
      721POLYGON ((121.78085 14.47100, 122.67917 14.471...gridxy-2-1
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      Step 4: Check if the grid is within bounds of the raster.
      
+
      
+
      fig, ax = plt.subplots(1, 1, figsize=(4, 8))
+show(raster.read(1), cmap="viridis", ax=ax, transform=raster.transform)
+grid_gdf.plot(ax=ax, facecolor="none", edgecolor="yellow")
+ax
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Step 5: State filepath of output folder.
      
+
      
+
      output_folder = Path("../data")
      
+
      
+
      
+
      
+
      Step 6: Crop raster
      
+
      
+
      raster_process.query_window_by_gdf(
+    input_image, output_folder, grid_gdf, name_col="name", mask=False
+)
      
+
      
+
      ../data/gridxy-0-0.tif
+../data/gridxy-0-1.tif
+../data/gridxy-0-2.tif
+../data/gridxy-1-0.tif
+../data/gridxy-1-1.tif
+../data/gridxy-1-2.tif
+../data/gridxy-2-0.tif
+../data/gridxy-2-1.tif
      
+
      
+
      
+
      
+
      for name in grid_gdf["name"]:
+    image_path = output_folder / (name + ".tif")
+    with rio.open(image_path) as dst:
+        fig, ax = plt.subplots(1, 1, figsize=(4, 4))
+        ax.set_title(image_path)
+        show(dst.read(1), cmap="viridis", ax=ax, transform=dst.transform)
      
+
      
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diff --git a/tutorial.raster_process_files/figure-html/cell-14-output-1.png b/tutorial.raster_process_files/figure-html/cell-14-output-1.png
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diff --git a/tutorial.raster_to_df.html b/tutorial.raster_to_df.html
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+geowrangler - Raster to Dataframe Tutorial
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+
      Raster to Dataframe Tutorial
      
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+  
      
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+
      A basic introduction to generating a vector-to-raster mask and converting rasters to Dataframes.
      
+
      
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+
      Basic Usage
      
+
      Generate a raster mask from a reference TIF image based on the labels assigned to certain regions of the area. The generated raster mask can also be used to convert the raster to a dataframe with respect to their assigned labels.
      
+
      Terms: * raster mask - processed image wherein some parts of it are set to no value or in other words hidden * labels - parts of an area within the satellite image are categorized as such. Examples are mining, agriculture, etc.
      
+
      
+
      import geopandas as gpd
+import matplotlib.pyplot as plt
+import pandas as pd
+
+import geowrangler.raster_to_dataframe as rdf
      
+
      
+
      
+
      from rasterio import features
+from rasterio.plot import show
+from rasterio.windows import Window, transform
      
+
      
+
      
+
      Load reference raster image and shape file
      
+
      The shape file that should be loaded should contain a column that consists of labels.
      
+
      
+
      tiff_file = "../data/vector_to_raster_mask_sample/cabanglasan.tif"
+shape_file = "../data/vector_to_raster_mask_sample/labels_20220816.gpkg"
+target_file = shape_file.replace("gpkg", "tiff")
      
+
      
+
      We create a dictionary of our target labels that we want to create a raster mask for.
      
+
      
+
      labels = {
+    "mining": 1,
+    "neg": 2,
+    "agriculture": 3,
+    "product_extraction": 4,
+    "kaingin": 5,
+    "biophysical": 6,
+}
      
+
      
+
      
+
      
+
      Generate raster mask
      
+
      To create the mask we input the reference raster image, shape file with labels, and the dictionary with our target labels into the function.
      
+
      
+
      # Generate masks for a file
+masks, grids, values = rdf.generate_mask(
+    tiff_file=tiff_file,
+    shape_file=shape_file,
+    output_file=target_file,
+    labels_column="label",
+    labels_dict=labels,
+    plot=True,
+)
      
+
      
+
      /home/abbymoreno/geowrangler/geowrangler/raster_to_dataframe.py:29: FutureWarning: Currently, index_parts defaults to True, but in the future, it will default to False to be consistent with Pandas. Use `index_parts=True` to keep the current behavior and True/False to silence the warning.
+  gs = gdf.explode()
      
+
      
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+
      
+
      Load rasters and the outputed raster mask
      
+
      
+
      tiff_files = [
+    "../data/vector_to_raster_mask_sample/cabanglasan.tif",
+]
+mask_file = "../data/vector_to_raster_mask_sample/labels_20220816.tiff"
      
+
      
+
      
+
      
+
      Convert raster to dataframe
      
+
      To convert raster/s to a dataframe, we just need to input the images into the function and add the mask file. The mask file will be used to create the last column label in the dataframe. The result is a tabular dataset that contains the Band values per raster image and their labels.
      
+
      
+
      data = rdf.read_bands(tiff_files, mask_file)
      
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      data
      
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      B1_0B2_0B3_0B4_0B5_0B6_0B7_0B8_0B9_0B10_0B11_0B12_0label
      00.11980.096350.093300.06980.106650.202500.24900.235250.281250.03770.199250.10020
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      20.11480.094200.094600.07070.103800.203950.24780.231500.271650.03850.182400.09020
      30.11480.091900.088500.06310.103800.203950.24780.233000.271650.03850.182400.09020
      40.11480.093500.090800.06430.105650.208300.24660.242050.269900.03850.180500.08940
      ..........................................
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+
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      775829 rows × 13 columns
      
+
      
+
      
+
      
+
      
+
      Checking the label column we can see that the TIF image that we converted does not contain the desired labels.
      
+
      
+
      data["label"].unique()
      
+
      
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      array([0], dtype=uint16)
      
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\ No newline at end of file
diff --git a/tutorial.raster_to_df_files/figure-html/cell-14-output-2.png b/tutorial.raster_to_df_files/figure-html/cell-14-output-2.png
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zuY!Z4oMi_*vY9jDQ6FJHi@RV
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+geowrangler - Raster Zonal Stats Tutorial
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+
      Raster Zonal Stats Tutorial
      
+
      
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+
      
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+    
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+
      A basic introduction to raster zonal stats
      
+
      
+
      
+
      
+
      Basic Usage
      
+
      Generate zonal stats for a GeoDataframe containing areas of interest using raster data
      
+
      Terms:
      
+
        
+
      • aoi - (area of interest) a geodataframe which we are interested in generating zonal statistics for
        • 
+
      • raster data - the source raster containing the features which we are interested in collecting zonal stats for our aoi.
        • 
+
      
+
      
+
      import geopandas as gpd
+import matplotlib.pyplot as plt
+import geowrangler.raster_zonal_stats as rzs
      
+
      
+
      
+
      Load AOI
      
+
      Our areas of interest (AOI) are three (3) Level 1 administration regions (ADM1) of the Philippines located in the island of Luzon.
      
+
      
+
      # area multipolygons for regions 3,4,ncr of the philippines
+aoi = gpd.read_file("../data/region34ncr_admin.geojson")
      
+
      
+
      CPU times: user 3.26 s, sys: 195 ms, total: 3.45 s
+Wall time: 3.44 s
      
+
      
+
      
+
      
+
      ax = aoi.plot(
+    ax=plt.axes(),
+    facecolor="none",
+    edgecolor=[
+        "#C62828",
+        "#283593",
+        "#FF9800",
+    ],
+)
      
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      aoi
      
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      Reg_CodeReg_NameReg_Alt_Namegeometry
      0130000000National Capital RegionNCRMULTIPOLYGON (((121.03842 14.78525, 121.03815 ...
      1030000000Region IIICentral LuzonMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...
      2040000000Region IV-ACalabarzonMULTIPOLYGON (((122.72165 13.36485, 122.72143 ...
      
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+
      
+
      Download Philippine Population Data
      
+
      
+
      see the Humanitarian Data Exchange World Population Counts - Philippines
      
+
      
+
      We download our raster Data as GeoTiff files from the Humanitarian Data Exchange site.
      
+
      
+
      
+
      
+
+
      
+
      
+Note
+
      
+
      
+
      
+
      This maybe slow as the file is about 180 Mb and depending on your internet download speed may take more than 5 minutes.
      
+
      
+
      
+
      
+
      # PHL population HDX links
+phil_pop_link = "https://data.worldpop.org/GIS/Population/Global_2000_2020/2020/PHL/phl_ppp_2020.tif"
+phil_pop_dset = "phl_pop_2020.tif"
      
+
      
+
      
+
      ![ ! -e ../data/{phil_pop_dset} ] && curl -o ../data/{phil_pop_dset} {phil_pop_link}
      
+
      
+
        % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
+                                 Dload  Upload   Total   Spent    Left  Speed
+100  180M  100  180M    0     0   576k      0  0:05:19  0:05:19 --:--:--  430k
+CPU times: user 12 s, sys: 3.57 s, total: 15.6 s
+Wall time: 5min 19s
      
+
      
+
      
+
      To create our raster zonal stats, we just need to set the aggregations, as well as some extra arguments, such as the nodata value in the raster.
      
+
      
+
      results = rzs.create_raster_zonal_stats(
+    aoi,
+    f"../data/{phil_pop_dset}",
+    aggregation=dict(
+        func=["sum", "count"],
+        column="population",
+        output=["population_count", "samples"],
+    ),
+    extra_args=dict(nodata=-99999),  # nodata value is -99999
+)
      
+
      
+
      CPU times: user 4.22 s, sys: 128 ms, total: 4.35 s
+Wall time: 4.34 s
      
+
      
+
      
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+
      results
      
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      Reg_CodeReg_NameReg_Alt_Namegeometrysamplespopulation_count
      0130000000National Capital RegionNCRMULTIPOLYGON (((121.03842 14.78525, 121.03815 ...7078613165866.0
      1030000000Region IIICentral LuzonMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...255837711493727.0
      2040000000Region IV-ACalabarzonMULTIPOLYGON (((122.72165 13.36485, 122.72143 ...187624415952383.0
      
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+
      Using Grid Tile AOIs
      
+
      We can also use tile grids as our AOIs.
      
+
      
+
      # note that you don't need to load the aoi first
+grid_aoi_file = "../data/region3_admin_grids.geojson"
      
+
      
+
      
+
      grid_aoi_results = rzs.create_raster_zonal_stats(
+    grid_aoi_file,
+    f"../data/{phil_pop_dset}",
+    aggregation=dict(
+        func=["sum", "count"],
+        column="population",
+        output=["population_count", "samples"],
+        fillna=[True, True],
+    ),
+    extra_args=dict(nodata=-99999),  # nodata value is -99999
+)
      
+
      
+
      CPU times: user 8.57 s, sys: 12.2 ms, total: 8.59 s
+Wall time: 8.58 s
      
+
      
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+
      grid_aoi_results.head()
      
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      xygeometrysamplespopulation_count
      0030POLYGON ((119.78583 15.70870, 119.83075 15.708...1711171.764038
      1031POLYGON ((119.78583 15.75193, 119.83075 15.751...329278.567200
      2032POLYGON ((119.78583 15.79516, 119.83075 15.795...345279.140198
      3130POLYGON ((119.83075 15.70870, 119.87566 15.708...158808.681152
      4132POLYGON ((119.83075 15.79516, 119.87566 15.795...200.000000
      
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+
      ax = aoi[aoi.Reg_Name == "Region III"].plot(
+    ax=plt.axes(), facecolor="none", edgecolor="black"
+)
+ax = grid_aoi_results.plot(
+    ax=ax, column="population_count", edgecolor="blue", alpha=0.5
+)
      
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      Using Bing Tile Grid Tile AOIs
      
+
      We can also use pre-existing Bing tile grids as our AOIs.
      
+
      
+
      # note that you don't need to load the aoi first
+bingtile_grid_aoi_file = "../data/region3_bingtile_grid13.geojson"
      
+
      
+
      
+
      bingtile_grid_aoi_results = rzs.create_raster_zonal_stats(
+    bingtile_grid_aoi_file,
+    f"../data/{phil_pop_dset}",
+    aggregation=dict(
+        func=["sum", "count"],
+        column="population",
+        output=["population_count", "samples"],
+        fillna=[True, True],
+    ),
+    extra_args=dict(nodata=-99999),  # nodata value is -99999
+)
      
+
      
+
      CPU times: user 10.6 s, sys: 124 ms, total: 10.7 s
+Wall time: 10.7 s
      
+
      
+
      
+
      
+
      bingtile_grid_aoi_results.head()
      
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+
+
+
+
+
+
+
+
+
+
+
+
      quadkeygeometrysamplespopulation_count
      01323030303301POLYGON ((120.10254 14.73239, 120.10254 14.774...737196.756744
      11323030303300POLYGON ((120.05859 14.73239, 120.05859 14.774...5035.732861
      21323030303311POLYGON ((120.19043 14.73239, 120.19043 14.774...248383.338013
      31323030303133POLYGON ((120.19043 14.77488, 120.19043 14.817...9015621.879395
      41323030303131POLYGON ((120.19043 14.81737, 120.19043 14.859...13286584.988770
      
+
+
      
+
      
+
      
+
      
+
      
+
      ax = aoi[aoi.Reg_Name == "Region III"].plot(
+    ax=plt.axes(), facecolor="none", edgecolor="black"
+)
+ax = bingtile_grid_aoi_results.plot(
+    ax=ax, column="population_count", edgecolor="blue", alpha=0.5
+)
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
+
+
      
+
      
+
+
       
+
+
       
+
+
+
+
+
\ No newline at end of file
diff --git a/tutorial.raster_zonal_stats_files/figure-html/cell-11-output-1.png b/tutorial.raster_zonal_stats_files/figure-html/cell-11-output-1.png
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+geowrangler - Spatial Join Using Highest Intersection Tutorial
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      Spatial Join Using Highest Intersection Tutorial
      
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      A basic introduction to using spatial join using highest intersection
      
+
      
+
      
+
      
+
      Summary
      
+
      Joins attributes of two tables based on largest area of overlap. Usage of this function assumes that the two datasets are both vector polygon data.
      
+
      
+
      
+
      How does it work?
      
+
      Spatial join using highest intersection works by assigning a unique ID to the first dataframe (gdf1) and using its geometry as basis for the intersection. Data from the second dataframe (gdf2), apart from its geometry, is retained in the output.
      
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      get_highest_intersection (gdf1, gdf2, proj_crs)
      
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      typedefaultoptional/ requireddetails
      gdf1GeoDataFramenonerequiredbasis of output geometry
      gdf2GeoDataFramenonerequireddata to be included during intersection
      proj_crsStringnonerequiredmetric CRS (e.g., Philippines uses EPSG:32651)
      
+
      A technical step-by-step explanation of how get_highest_intersection works is detailed in the cell blocks below. An example on how to use it with its arguments is shown in the sample use case section thereafter.
      
+
        
+
      1. Define the function and its arguments.
        2. 
+
      
+
      def get_highest_intersection(
+    gdf1: gpd.GeoDataFrame,
+    gdf2: gpd.GeoDataFrame,
+    proj_crs: str,
+) -> gpd.GeoDataFrame:
      
+
        
+
      1. Create a copy of the two geodataframes.
        2. 
+
      
+
          gdf1 = gdf1.copy()
+    gdf2 = gdf2.copy()
      
+
        
+
      1. Rename new columns with “__” prefixes and suffixes to prevent overwriting existing columns in the original geodataframes.
        2. 
+
      
+
          uid_col = "__uid__"
+    area_col = "__area_highest_intersection__"
+    auxiliary_cols = [uid_col, area_col]
      
+
        
+
      1. Conduct checks to make sure we are not overwriting existing columns.
        2. 
+
      
+
          for col in auxiliary_cols:
+        if col in gdf1.columns:
+            raise ValueError(f"Make sure {col} isn't already a column in gdf1")
+        if col in gdf2.columns:
+            raise ValueError(f"Make sure {col} isn't already a column in gdf2")
      
+
        
+
      1. Assign a unique ID to the first geodataframe. Note that uid_col is also defined in Step 2.
        2. 
+
      
+
          gdf1[uid_col] = range(len(gdf1))
      
+
        
+
      1. Get intersection of the geodataframes.
        2. 
+
      
+
          overlay = gdf1.overlay(gdf2, how="intersection")
      
+
        
+
      1. Add a column for overlapping area. Note that area_col is also defined in Step 2.
        2. 
+
      
+
          overlay["geometry"] = overlay["geometry"].to_crs(proj_crs)
+    overlay[area_col] = (overlay.geometry.area)
      
+
        
+
      1. Sort values by area. Drop duplicates and null values.
        2. 
+
      
+
          overlay = overlay.sort_values(by=area_col, ascending=True)
+    overlay = overlay.drop_duplicates(subset=[uid_col], keep="last")
+    overlay = overlay.dropna(subset=[uid_col])
+    assert not overlay[uid_col].duplicated().any()
+    overlay = overlay.sort_values(by=[uid_col], ascending=True)
      
+
        
+
      1. Drop geometry from the overlay dataframe and merge the original geometry from gdf1. Also drop additional columns (uid_col, area_col) used to accomplish calculate overlapping area for the function.
        2. 
+
      
+
          overlay_merge = overlay.drop("geometry", axis=1)
+
+    output = pd.merge(
+        left=gdf1[[uid_col, "geometry"]],
+        right=overlay_merge,
+        on=uid_col,
+        how="left",
+        validate="one_to_one",
+    )
+
+    output = output.drop(columns=auxiliary_cols)
      
+
        
+
      1. Drop the additional columns (uid_col, area_col) and return the final output.
        2. 
+
      
+
          return output
      
+
      
+
      
+
      
+
      Sample use case - Labeling grid with administrative boundaries
      
+
      Input: - grid - GeoDataFrame that will become the basis of geometries for the output - adm_bounds - GeoDataFrame containing attributes that we want to append to the grid - proj_crs - metric coordinate reference system (e.g., “EPSG:32651” for the Philippines)
      
+
      Output - grid with one province name per cell
      
+
      
+
      Step 1: Import package
      
+
      
+
      import geopandas as gpd
+import geowrangler.grids as grids
      
+
      
+
      
+
      import geowrangler.spatialjoin_highest_intersection as spatial_join
      
+
      
+
      
+
      
+
      Step 2: Load the first dataset (grid)
      
+
      This dataset will become the basis of geometry for the output.
      
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      grid
      
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      xygeometry
      033POLYGON ((118.27581 5.92871, 118.72497 5.92871...
      123POLYGON ((117.82665 5.92871, 118.27581 5.92871...
      234POLYGON ((118.27581 6.37528, 118.72497 6.37528...
      335POLYGON ((118.27581 6.82146, 118.72497 6.82146...
      406POLYGON ((116.92834 7.26723, 117.37749 7.26723...
      ............
      318191POLYGON ((125.46233 5.03451, 125.91149 5.03451...
      3191912POLYGON ((125.46233 9.93163, 125.91149 9.93163...
      3201913POLYGON ((125.46233 10.37375, 125.91149 10.373...
      3212012POLYGON ((125.91149 9.93163, 126.36065 9.93163...
      3222013POLYGON ((125.91149 10.37375, 126.36065 10.373...
      
+
+
      323 rows × 3 columns
      
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      grid.plot(facecolor="none", edgecolor="blue");
      
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+
      
+
      # grid.explore()
      
+
      
+
      
+
      
+
      Step 3: Load the second dataset (admin_bounds)
      
+
      Info from this dataset (apart from geometry) will be appended to the output based on highest spatial intersection.
      
+
      
+
      admin_bounds
      
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      shapeNameshapeISOshapeIDshapeGroupshapeTypegeometry
      0AbraNonePHL-ADM2-3_0_0-B1PHLADM2MULTIPOLYGON (((120.96795 17.95706, 120.97803 ...
      1Agusan del NorteNonePHL-ADM2-3_0_0-B2PHLADM2MULTIPOLYGON (((125.57724 9.45679, 125.59687 9...
      2Agusan del SurNonePHL-ADM2-3_0_0-B3PHLADM2MULTIPOLYGON (((125.91087 8.85625, 125.91461 8...
      3AklanNonePHL-ADM2-3_0_0-B4PHLADM2MULTIPOLYGON (((122.43667 11.59833, 122.43667 ...
      4AlbayNonePHL-ADM2-3_0_0-B5PHLADM2MULTIPOLYGON (((123.28764 13.04923, 123.28686 ...
      .....................
      76Tawi-TawiNonePHL-ADM2-3_0_0-B77PHLADM2MULTIPOLYGON (((119.46694 4.58694, 119.46639 4...
      77ZambalesNonePHL-ADM2-3_0_0-B78PHLADM2MULTIPOLYGON (((120.08285 14.75048, 120.08222 ...
      78Zamboanga del NorteNonePHL-ADM2-3_0_0-B79PHLADM2MULTIPOLYGON (((122.09467 7.53152, 122.09467 7...
      79Zamboanga del SurNonePHL-ADM2-3_0_0-B80PHLADM2MULTIPOLYGON (((122.06223 6.87278, 122.06250 6...
      80Zamboanga SibugayNonePHL-ADM2-3_0_0-B81PHLADM2MULTIPOLYGON (((122.84063 7.27694, 122.84048 7...
      
+
+
      81 rows × 6 columns
      
+
      
+
      
+
      
+
      
+
      
+
      admin_bounds.plot(facecolor="none", edgecolor="blue");
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      # admin_bounds.explore()
      
+
      
+
      
+
      
+
      Step 4: Get spatial join with highest intersection
      
+
      
+
      output = spatial_join.get_highest_intersection(grid, admin_bounds, "EPSG:32651")
      
+
      
+
      
+
      Note that each grid now has admin bounds columns (shapeName, shapeISO, shapeID, … etc.) based on the intersection with the admin boundaries with the highest overlapping area over each grid.
      
+
      
+
      
+
      output
      
+
      
+
      
+
      
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      geometryxyshapeNameshapeISOshapeIDshapeGroupshapeType
      0POLYGON ((118.27581 5.92871, 118.72497 5.92871...33Tawi-TawiNonePHL-ADM2-3_0_0-B77PHLADM2
      1POLYGON ((117.82665 5.92871, 118.27581 5.92871...23Tawi-TawiNonePHL-ADM2-3_0_0-B77PHLADM2
      2POLYGON ((118.27581 6.37528, 118.72497 6.37528...34Tawi-TawiNonePHL-ADM2-3_0_0-B77PHLADM2
      3POLYGON ((118.27581 6.82146, 118.72497 6.82146...35Tawi-TawiNonePHL-ADM2-3_0_0-B77PHLADM2
      4POLYGON ((116.92834 7.26723, 117.37749 7.26723...06PalawanNonePHL-ADM2-3_0_0-B59PHLADM2
      ...........................
      318POLYGON ((125.46233 5.03451, 125.91149 5.03451...191Davao del SurNonePHL-ADM2-3_0_0-B28PHLADM2
      319POLYGON ((125.46233 9.93163, 125.91149 9.93163...1912Dinagat IslandsNonePHL-ADM2-3_0_0-B30PHLADM2
      320POLYGON ((125.46233 10.37375, 125.91149 10.373...1913Eastern SamarNonePHL-ADM2-3_0_0-B31PHLADM2
      321POLYGON ((125.91149 9.93163, 126.36065 9.93163...2012Surigao del NorteNonePHL-ADM2-3_0_0-B74PHLADM2
      322POLYGON ((125.91149 10.37375, 126.36065 10.373...2013Eastern SamarNonePHL-ADM2-3_0_0-B31PHLADM2
      
+
+
      323 rows × 8 columns
      
+
      
+
      
+
      
+
      
+
      
+
      output.plot(facecolor="none", edgecolor="blue");
      
+
      
+
      
+
      
+
      
+
      
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+
      
+
      
+
      
+
      # output.explore()
      
+
      
+
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+
      
+
      
+
+
       
+
+
       
+
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+
+
+
\ No newline at end of file
diff --git a/tutorial.spatialjoin_highest_intersection_files/figure-html/cell-14-output-1.png b/tutorial.spatialjoin_highest_intersection_files/figure-html/cell-14-output-1.png
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diff --git a/tutorial.tile_clustering.html b/tutorial.tile_clustering.html
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+geowrangler - Tile Clustering Tutorial
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      Tile Clustering Tutorial
      
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      A basic introduction to using tile clustering
      
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      Summary
      
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      Clusters adjacent grid tiles together. Usage of this function assumes that the input is a grid dataset.
      
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      How does it work?
      
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      Tile clustering works by assigning the same ID to grid cells belonging to the same cluster. There are options to: (a) cluster adjacent cells by category, (b) cluster grid cells via adjacent edges or corners.
      
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      TileClustering().cluster_tiles(df, category_col)
      
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      typedefaultoptional/requireddetails
      cluster_typeStringfour_wayoptionalLeave blank () if using four_way cluster_type (top, bottom, left, right sides of a cell). Put in eight_way if preference is to cluster via four sides and corners of a cell.
      dfGeoDataFramenonerequireddataframe for clustering
      category_colStringnoneoptionalcolumn for category if need to cluster adjacent cells by category
      
+
      A technical step-by-step explanation of how TileClustering().cluster_tiles works is detailed in the cell blocks below. An example on how to use it with its arguments is shown in the sample use case section thereafter.
      
+
        
+
      1. Define the class.
        2. 
+
      
+
      class TileClustering:
+    def __init__(
+        self,
+        cluster_type: str = "four_way",
+    ) -> None:
+
+        assert cluster_type in ["four_way", "eight_way"]
+        self.cluster_type = cluster_type
+        self.tile_cluster_col = "tile_cluster"
      
+
        
+
      1. Define the function.
        2. 
+
      
+
      @patch
+def cluster_tiles(
+    self: TileClustering,
+    df: pd.DataFrame,
+    grid_x_col="x",
+    grid_y_col="y",
+    category_col: Optional[str] = None,
+) -> pd.DataFrame:
+    """
+    Appends the cluster ID for each square grid cell
+    """
+
+    if category_col is None:
+        cluster_df = self._cluster_tiles_single(df, grid_x_col, grid_y_col)
+    else:
+        assert (
+            not df[category_col].isnull().any()
+        ), f"There shouldn't be null values for {category_col}"
+        unique_categories = df[category_col].unique().tolist()
+
+        cluster_df_list = []
+        for i, category in enumerate(unique_categories, start=1):
+            bool_mask = df[category_col] == category
+            filtered_df = df.loc[bool_mask, :].copy()
+            cluster_filtered_df = self._cluster_tiles_single(
+                filtered_df, grid_x_col, grid_y_col
+            )
+            cluster_filtered_df[self.tile_cluster_col] = cluster_filtered_df[
+                self.tile_cluster_col
+            ].apply(lambda key: f"{key}-{i}")
+            cluster_df_list.append(cluster_filtered_df)
+        cluster_df = pd.concat(cluster_df_list, axis=0, ignore_index=True)
+
+    df = pd.merge(left=df, right=cluster_df, on=[grid_x_col, grid_y_col], how="left")
+
+    return df
      
+
        
+
      1. Categorize commands depending on input of category_col. Append cluster ID for each grid cell.
        2. 
+
      
+
          # no entry in category_col - run the patched function _cluster_tiles_single
+    if category_col is None:
+        cluster_df = self._cluster_tiles_single(df, grid_x_col, grid_y_col)
+
+    # entry in category_col - make sure that all rows have values
+    else:
+        assert (
+            not df[category_col].isnull().any()
+        ), f"There shouldn't be null values for {category_col}"
+        unique_categories = df[category_col].unique().tolist()
+
+    # append cluster ID for each grid cell
+        cluster_df_list = []
+        for i, category in enumerate(unique_categories, start=1):
+            bool_mask = df[category_col] == category
+            filtered_df = df.loc[bool_mask, :].copy()
+            cluster_filtered_df = self._cluster_tiles_single(
+                filtered_df, grid_x_col, grid_y_col
+            )
+            cluster_filtered_df[self.tile_cluster_col] = cluster_filtered_df[
+                self.tile_cluster_col
+            ].apply(lambda key: f"{key}-{i}")
+            cluster_df_list.append(cluster_filtered_df)
+        cluster_df = pd.concat(cluster_df_list, axis=0, ignore_index=True)
      
+
        
+
      1. Merge grid cells with same cluster ID.
        2. 
+
      
+
          df = pd.merge(left=df, right=cluster_df, on=[grid_x_col, grid_y_col], how="left")
      
+
        
+
      1. Return output dataframe.
        2. 
+
      
+
          return df
      
+
        
+
      1. Other @patch functions
        2. 
+
      
+
      _cluster_tiles_single - called when there is no entry in category_col
      
+
      @patch
+def _cluster_tiles_single(
+    self: TileClustering,
+    df: pd.DataFrame,
+    grid_x_col="x",
+    grid_y_col="y",
+) -> pd.DataFrame:
+    """
+    Performs tile clustering on a single category
+    """
+
+    if self.tile_cluster_col in df.columns:
+        raise ValueError(
+            f"{self.tile_cluster_col} already exists as a column. Please rename"
+        )
+
+    grid_x = df[grid_x_col]
+    grid_y = df[grid_y_col]
+
+    self.grid_idx = set(zip(grid_x, grid_y))
+
+    self.tile_cluster_dict = {}
+    self.cluster_id = 0
+
+    for key in self.grid_idx:
+        if key not in self.tile_cluster_dict.keys():
+            self.cluster_id += 1
+
+            # reset the call stack per iteration
+            self.call_stack = deque()
+            self._dfs_connected_components(key)
+
+    cluster_df = pd.DataFrame.from_dict(
+        self.tile_cluster_dict, orient="index", columns=[self.tile_cluster_col]
+    )
+    cluster_df = cluster_df.reset_index()
+    cluster_df[grid_x_col] = cluster_df["index"].apply(lambda idx: idx[0])
+    cluster_df[grid_y_col] = cluster_df["index"].apply(lambda idx: idx[1])
+    cluster_df = cluster_df.drop(columns="index")
+
+    return cluster_df
      
+
      _get_adjacent_keys - defines how four_way and eight_way clustering works
      
+
      @patch
+def _get_adjacent_keys(
+    self: TileClustering,
+    key: Tuple[int, int],
+) -> List[Tuple[int, int]]:
+
+    x_idx = key[0]
+    y_idx = key[1]
+
+    east_key = (x_idx + 1, y_idx)
+    west_key = (x_idx - 1, y_idx)
+    south_key = (x_idx, y_idx - 1)
+    north_key = (x_idx, y_idx + 1)
+
+    if self.cluster_type == "four_way":
+        adjacent_keys = [east_key, west_key, south_key, north_key]
+
+    if self.cluster_type == "eight_way":
+        northeast_key = (x_idx + 1, y_idx + 1)
+        northwest_key = (x_idx - 1, y_idx + 1)
+        southeast_key = (x_idx + 1, y_idx - 1)
+        southwest_key = (x_idx - 1, y_idx - 1)
+
+        adjacent_keys = [
+            east_key,
+            west_key,
+            south_key,
+            north_key,
+            northeast_key,
+            northwest_key,
+            southeast_key,
+            southwest_key,
+        ]
+
+    return adjacent_keys
      
+
      _dfs_connected_components - a non-recursive depth-first search implementation of connected components
      
+
      @patch
+def _dfs_connected_components(
+    self: TileClustering,
+    key: Tuple[int, int],
+) -> None:
+
+    self.call_stack.append(key)
+    while self.call_stack:
+        ref_key = self.call_stack.pop()
+
+        # check if key exists in the first place
+        if ref_key in self.grid_idx:
+            # check if adjacent key has already been assigned
+            if ref_key not in self.tile_cluster_dict.keys():
+                self.tile_cluster_dict[ref_key] = self.cluster_id
+
+                adjacent_keys = self._get_adjacent_keys(ref_key)
+                for adjacent_key in adjacent_keys:
+                    self.call_stack.append(adjacent_key)
      
+
      
+
      
+
      
+
      Sample use case- Clustering areas based on scores
      
+
      Input: - grid_gdf5k - GeoDataFrame of randomly scored 5km x 5km grid cells - class - category column for basis of of clustering
      
+
      Output: - clustered grid based on class
      
+
      
+
      Step 1: Import packages
      
+
      
+
      import geopandas as gpd
+import numpy as np
+
+import geowrangler.grids as grids
+
+import geowrangler.tile_clustering as tile_clustering
      
+
      
+
      
+
      
+
      Step 2: Load GeoDataFrame and generate grid
      
+
      
+
      grid_generator5k = grids.SquareGridGenerator(5_000)
+grid_gdf5k = grid_generator5k.generate_grid(region3_gdf)
      
+
      
+
      
+
      grid_gdf5k
      
+
      
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      xygeometry
      078POLYGON ((120.10024 14.75528, 120.14516 14.755...
      168POLYGON ((120.05533 14.75528, 120.10024 14.755...
      298POLYGON ((120.19008 14.75528, 120.23499 14.755...
      3224POLYGON ((119.87566 15.44910, 119.92058 15.449...
      4225POLYGON ((119.87566 15.49239, 119.92058 15.492...
      ............
      10695444POLYGON ((122.21128 16.31312, 122.25620 16.313...
      10705445POLYGON ((122.21128 16.35623, 122.25620 16.356...
      10715446POLYGON ((122.21128 16.39932, 122.25620 16.399...
      10725447POLYGON ((122.21128 16.44240, 122.25620 16.442...
      10735448POLYGON ((122.21128 16.48548, 122.25620 16.485...
      
+
+
      1074 rows × 3 columns
      
+
      
+
      
+
      
+
      
+
      
+
      grid_gdf5k.plot();
      
+
      
+
      
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+
      
+
      
+
      
+
      Step 3: Assign scores
      
+
      
+
      grid_gdf5k
      
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      xygeometryscoreclass
      078POLYGON ((120.10024 14.75528, 120.14516 14.755...0.154930False
      168POLYGON ((120.05533 14.75528, 120.10024 14.755...0.953133True
      298POLYGON ((120.19008 14.75528, 120.23499 14.755...0.719994True
      3224POLYGON ((119.87566 15.44910, 119.92058 15.449...0.283082False
      4225POLYGON ((119.87566 15.49239, 119.92058 15.492...0.626144False
      ..................
      10695444POLYGON ((122.21128 16.31312, 122.25620 16.313...0.862839True
      10705445POLYGON ((122.21128 16.35623, 122.25620 16.356...0.659405False
      10715446POLYGON ((122.21128 16.39932, 122.25620 16.399...0.863707True
      10725447POLYGON ((122.21128 16.44240, 122.25620 16.442...0.246483False
      10735448POLYGON ((122.21128 16.48548, 122.25620 16.485...0.785704True
      
+
+
      1074 rows × 5 columns
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Step 4: Tile clustering
      
+
      
+
      output = tile_clustering.TileClustering().cluster_tiles(
+    df=grid_gdf5k, category_col="class"
+)
      
+
      
+
      
+
      output
      
+
      
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      xygeometryscoreclasstile_cluster
      078POLYGON ((120.10024 14.75528, 120.14516 14.755...0.154930False1-1
      168POLYGON ((120.05533 14.75528, 120.10024 14.755...0.953133True123-2
      298POLYGON ((120.19008 14.75528, 120.23499 14.755...0.719994True2-2
      3224POLYGON ((119.87566 15.44910, 119.92058 15.449...0.283082False1-1
      4225POLYGON ((119.87566 15.49239, 119.92058 15.492...0.626144False1-1
      .....................
      10695444POLYGON ((122.21128 16.31312, 122.25620 16.313...0.862839True73-2
      10705445POLYGON ((122.21128 16.35623, 122.25620 16.356...0.659405False2-1
      10715446POLYGON ((122.21128 16.39932, 122.25620 16.399...0.863707True36-2
      10725447POLYGON ((122.21128 16.44240, 122.25620 16.442...0.246483False2-1
      10735448POLYGON ((122.21128 16.48548, 122.25620 16.485...0.785704True74-2
      
+
+
      1074 rows × 6 columns
      
+
      
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+
      output.plot(column="class", categorical=True, cmap="Spectral");
      
+
      
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\ No newline at end of file
diff --git a/tutorial.tile_clustering_files/figure-html/cell-14-output-1.png b/tutorial.tile_clustering_files/figure-html/cell-14-output-1.png
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diff --git a/tutorial.tile_clustering_files/figure-html/cell-19-output-1.png b/tutorial.tile_clustering_files/figure-html/cell-19-output-1.png
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+
+
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+
+
+geowrangler - Vector Zonal Stats Tutorial
+
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+
      
+
      Vector Zonal Stats Tutorial
      
+
      
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+
      
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+    
+  
+    
+  
      
+  
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+
      
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+
      
+
      A basic introduction to vector zonal stats
      
+
      
+
      
+
      
+
      Basic Usage
      
+
      Generate zonal stats for a GeoDataframe containing areas of interest
      
+
      Terms:
      
+
        
+
      • aoi - (area of interest) a geodataframe which we are interested in generating zonal statistics for
        • 
+
      • data - the source geodataframe containing the features which are interested in collecting zonal stats for our aoi.
        • 
+
      
+
      
+
      Use case 1 - Count POIs (Points of Interest)
      
+
      
+
      Example 1: Count POIs for Regions
      
+
        
+
      • Input:
+
          
+
        • aoi - region3,4,ncr regions (Admin Level 1) (Central Luzon) geometry (geom_type - polygon, multipolygon
          • 
+
        • data - Philippine pois (geom_type - points)
          • 
+
        • overlap_method = ‘intersects’
          • 
+
        • aggregations:
+
            
+
          • count - number of pois within aoi
            • 
+
          • 
+
        • 
+
      • Output
+
          
+
        • aoi with pois count (default output column: index_count)
          • 
+
        • 
+
      
+
      
+
      import geopandas as gpd
+import matplotlib.pyplot as plt
+import geowrangler.vector_zonal_stats as vzs
      
+
      
+
      
+
      # area multipolygons for regions 3,4,ncr of the philippines
+aoi = gpd.read_file("../data/region34ncr_admin.geojson")
      
+
      
+
      CPU times: user 3 s, sys: 219 ms, total: 3.22 s
+Wall time: 3.21 s
      
+
      
+
      
+
      
+
      ax = aoi.plot(
+    ax=plt.axes(),
+    facecolor="none",
+    edgecolor=[
+        "#C62828",
+        "#283593",
+        "#FF9800",
+    ],
+)
      
+
      
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      aoi
      
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      Reg_CodeReg_NameReg_Alt_Namegeometry
      0130000000National Capital RegionNCRMULTIPOLYGON (((121.03842 14.78525, 121.03815 ...
      1030000000Region IIICentral LuzonMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...
      2040000000Region IV-ACalabarzonMULTIPOLYGON (((122.72165 13.36485, 122.72143 ...
      
+
+
      
+
      
+
      
+
      
+
      
+
      # raw pois from osm data (subset of region3,4, ncr only)
+raw_data = gpd.read_file("../data/region34ncr_osm_pois.geojson")
      
+
      
+
      CPU times: user 94.6 ms, sys: 4.12 ms, total: 98.7 ms
+Wall time: 96.1 ms
      
+
      
+
      
+
      
+
      raw_data.columns.values
      
+
      
+
      array(['osm_id', 'code', 'fclass', 'name', 'BARANGAY_CODE', 'geometry'],
+      dtype=object)
      
+
      
+
      
+
      
+
      raw_data.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      osm_idcodefclassnameBARANGAY_CODEgeometry
      03115684282701tourist_infoManila American Cemetery and Memorial Visitor ...137602022POINT (121.04852 14.54398)
      16725654962701tourist_infoecopark paging and first aid station137404141POINT (121.07479 14.71173)
      26725654982701tourist_infoEcopark ticket counter137404141POINT (121.07326 14.71291)
      315853895442701tourist_infoArea Formerly Occupied by Fort Bonifacio Museum137602021POINT (121.05837 14.55071)
      418348554242701tourist_infoLotto Booth137601020POINT (120.99216 14.42312)
      
+
+
      
+
      
+
      
+
      
+
      
+
      ax = aoi.plot(
+    ax=plt.axes(),
+    facecolor="none",
+    edgecolor=[
+        "#C62828",
+        "#283593",
+        "#FF9800",
+    ],
+)
+ax = raw_data.plot(ax=ax)
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Compute POIs count per region
      
+
      
+
      aoi = vzs.create_zonal_stats(
+    aoi,
+    raw_data,
+    overlap_method="intersects",
+    aggregations=[{"func": "count"}],
+)
      
+
      
+
      CPU times: user 274 ms, sys: 0 ns, total: 274 ms
+Wall time: 272 ms
      
+
      
+
      
+
      New aoi with pois count in the column index_count. (The column name can be overridden as shown in the next example)
      
+
      
+
      aoi
      
+
      
+
      CPU times: user 2 µs, sys: 0 ns, total: 2 µs
+Wall time: 4.29 µs
      
+
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      Reg_CodeReg_NameReg_Alt_Namegeometryindex_count
      0130000000National Capital RegionNCRMULTIPOLYGON (((121.03842 14.78525, 121.03815 ...880
      1030000000Region IIICentral LuzonMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...701
      2040000000Region IV-ACalabarzonMULTIPOLYGON (((122.72165 13.36485, 122.72143 ...1253
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      Example 2: Count attractions for Regions
      
+
        
+
      • Input:
+
          
+
        • aoi - region3,4,ncr regions (Admin Level 1) (Central Luzon, NCR, Calabarzon) geometry (geom_type - polygon, multipolygon)
          • 
+
        • data - attractions: filtered Philippine pois (Central Luzon, NCR, Calabarzon only) (geom_type - points) with fclass == ‘attraction’
          • 
+
        • overlap_method = ‘intersects’
          • 
+
        • aggregations:
+
            
+
          • count
+
              
+
            • number of pois within aoi
              • 
+
            • output column name attractions
              • 
+
            • 
+
          • 
+
        • 
+
      • Output
+
          
+
        • aoi with attractions count
          • 
+
        • 
+
      
+
      Filter the raw data
      
+
      
+
      # select only 'attraction' pois
+attractions = raw_data[raw_data.fclass == "attraction"]
      
+
      
+
      
+
      attractions.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      osm_idcodefclassnameBARANGAY_CODEgeometry
      491594735542721attractionChinatown Arch133902005POINT (120.97671 14.59672)
      506224139782721attractionPeace Bell137404020POINT (121.04934 14.65026)
      516251807012721attractionThe Glass Garden137403027POINT (121.08194 14.61932)
      526812229772721attractionLa Madre Filipina133908008POINT (120.97773 14.58172)
      538206340392721attractionIndependence Flag Pole133908008POINT (120.97659 14.58155)
      
+
+
      
+
      
+
      
+
      
+
      Create zonal stats for filtered data. Add output key to specify output column name for count
      
+
      
+
      aoi_attr = vzs.create_zonal_stats(
+    aoi, attractions, aggregations=[{"func": "count", "output": "attractions"}]
+)
      
+
      
+
      CPU times: user 290 ms, sys: 0 ns, total: 290 ms
+Wall time: 288 ms
      
+
      
+
      
+
      
+
      aoi_attr
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      Reg_CodeReg_NameReg_Alt_Namegeometryattractions
      0130000000National Capital RegionNCRMULTIPOLYGON (((121.03842 14.78525, 121.03815 ...136
      1030000000Region IIICentral LuzonMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...205
      2040000000Region IV-ACalabarzonMULTIPOLYGON (((122.72165 13.36485, 122.72143 ...312
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      Example 3: Grid Tiles over POIs
      
+
        
+
      • Input:
+
          
+
        • aoi - gridded tiles for Region 3 (Central Luzon) at 15km x 15km size
          • 
+
        • data - region 3 data filtered from philippine pois (geom_type - points)
          • 
+
        • overlap_method = ‘intersects’
          • 
+
        • aggregations:
+
            
+
          • count
+
              
+
            • number of pois within aoi
              • 
+
            • output column name:: pois_count
              • 
+
            • 
+
          • 
+
        • 
+
      • Output
+
          
+
        • aoi with pois count
          • 
+
        • 
+
      
+
      
+
      # load gridded tiles
+grid_aoi = gpd.read_file("../data/region3_admin_grids.geojson")
      
+
      
+
      CPU times: user 96.3 ms, sys: 0 ns, total: 96.3 ms
+Wall time: 87.4 ms
      
+
      
+
      
+
      
+
      ax = aoi[aoi.Reg_Name == "Region III"].plot(
+    ax=plt.axes(), facecolor="none", edgecolor="#C62828"
+)
+ax = grid_aoi.plot(ax=ax, facecolor="none", edgecolor="blue")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      region3_pois = gpd.read_file("../data/region3_osm_pois.geojson")
      
+
      
+
      CPU times: user 50.4 ms, sys: 0 ns, total: 50.4 ms
+Wall time: 48 ms
      
+
      
+
      
+
      
+
      region3_pois.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      osm_idcodefclassnameBARANGAY_CODEgeometry
      05604109862701tourist_infoGawad Kalinga Office031420009POINT (121.08516 14.83601)
      112443016722701tourist_infoN 15deg 26' 11.1", E 120deg 25' 50.2", El...036918006POINT (120.43045 15.43663)
      216666843932701tourist_infoEco Park Tourist Information & DENR Environmen...036918006POINT (120.44958 15.46446)
      316799929292701tourist_infoLa Paz Tarlac - Zaragoza Nueva Ecija Boundary034932027POINT (120.75832 15.44284)
      417146457292701tourist_infoLucy Pineda035409019POINT (120.61452 15.23359)
      
+
+
      
+
      
+
      
+
      
+
      
+
      len(region3_pois)
      
+
      
+
      701
      
+
      
+
      
+
      
+
      ax = aoi[aoi.Reg_Name == "Region III"].plot(
+    ax=plt.axes(), facecolor="none", edgecolor="#C62828"
+)
+ax = region3_pois.plot(ax=ax)
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Compute pois count per grid
      
+
      
+
      grid_aoi = vzs.create_zonal_stats(
+    grid_aoi,
+    region3_pois,
+    overlap_method="intersects",
+    aggregations=[{"func": "count", "output": "pois_count"}],
+)
      
+
      
+
      CPU times: user 33.9 ms, sys: 0 ns, total: 33.9 ms
+Wall time: 31.5 ms
      
+
      
+
      
+
      
+
      grid_aoi[grid_aoi.pois_count > 0].head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      xygeometrypois_count
      11228POLYGON ((119.87566 15.62220, 119.92058 15.622...1.0
      14231POLYGON ((119.87566 15.75193, 119.92058 15.751...1.0
      18323POLYGON ((119.92058 15.40581, 119.96550 15.405...1.0
      21326POLYGON ((119.92058 15.53567, 119.96550 15.535...4.0
      22327POLYGON ((119.92058 15.57894, 119.96550 15.578...1.0
      
+
+
      
+
      
+
      
+
      
+
      
+
      grid_aoi.pois_count.sum()
      
+
      
+
      701.0
      
+
      
+
      
+
      
+
      # show grids with at least 1 poi
+ax = aoi[aoi.Reg_Name == "Region III"].plot(
+    ax=plt.axes(),
+    facecolor="none",
+    edgecolor=[
+        "#C62828",
+    ],
+)
+ax = grid_aoi[grid_aoi.pois_count > 0].plot(ax=ax, facecolor="none", edgecolor="green")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Use case 2 - Stats on a metric column
      
+
      
+
      Example 1 Regions over Population per Bgy Level
      
+
        
+
      • Input:
+
          
+
        • aoi - region3,4,ncr geometry (geom_type - polygon, multipolygon)
          • 
+
        • data - region3,4, ncr population data (geom_type - pois)
          • 
+
        • data_type: ‘individual_pois’
          • 
+
        • overlap_method = ‘intersects’
          • 
+
        • aggregations:
+
            
+
          • metric_columns: ‘population’,‘men’, etc.
+
              
+
            • each row in the data has a column: population, men, women, etc. with numeric value
              • 
+
            • aggregation_functions - ‘min’,‘max’, ‘mean’, ‘sum’, etc.
              • 
+
            • output_columns - ‘pop_min’, ‘pop_max’, for each column
              • 
+
            • 
+
          • 
+
        • 
+
      • Output:
+
          
+
        • aoi with new output columns pop_min, pop_max etc
          • 
+
        • 
+
      
+
      Load 2020 Region 3, Region 4, and NCR Population Data at Barangay level (Admin Level 4)
      
+
      
+
      
+
      
+
+
      
+
      
+Note
+
      
+
      
+
      
+
      The population data came from the 2020 Facebook HRSL Dataset filtered for barangays covering Regions 3, Region 4, and NCR and converted the geometries from polygons to points by computing their centroids (projected to EPSG:3969). The land area was computed by projecting to EPSG:3123 and getting the geometry.area .
      
+
      
+
      
+
      
+
      # load region3,4,ncr population data at barangay level
+region34ncr_pop_data = gpd.read_file("../data/region34ncr_population_land.geojson")
      
+
      
+
      CPU times: user 543 ms, sys: 9.45 ms, total: 552 ms
+Wall time: 548 ms
      
+
      
+
      
+
      
+
      region34ncr_pop_data.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      BARANGAY_CODEpopulationmenwomenchildren_under_fiveyouth_15_24women_of_reproductive_age_15_49elderly_60_plusland_areageometry
      031420020.01807920887202359468772.183518e+05POINT (121.05919 14.85825)
      134915006.03093159414993526588261597.327482e+06POINT (120.99590 15.62242)
      235403009.042412158208347482111122568.740450e+05POINT (120.77730 15.14580)
      335409023.03373175016232966118952446.484611e+05POINT (120.58052 15.22315)
      435413009.020884105391034421983893558912602.015571e+06POINT (120.68523 15.09163)
      
+
+
      
+
      
+
      
+
      
+
      
+
      region34ncr_pop_data.columns.values
      
+
      
+
      array(['BARANGAY_CODE', 'population', 'men', 'women',
+       'children_under_five', 'youth_15_24',
+       'women_of_reproductive_age_15_49', 'elderly_60_plus', 'land_area',
+       'geometry'], dtype=object)
      
+
      
+
      
+
      Compute zonal stats for regions 3,4,NCR * barangay count per region (bgy_count) * sum and mean for each population statistic (population, men, women, etc) * sum, mean, std, min, max for land area statistic
      
+
      
+
      aoi = vzs.create_zonal_stats(
+    aoi,
+    region34ncr_pop_data,
+    aggregations=[
+        {"func": "count", "output": "bgy_count"},
+        {
+            "column": "population",
+            "func": ["sum", "mean"],
+            "output": ["pop_total", "pop_avg"],
+        },
+        {"column": "men", "func": ["sum", "mean"], "output": ["men_total", "men_avg"]},
+        {
+            "column": "women",
+            "func": ["sum", "mean"],
+            "output": ["women_total", "women_avg"],
+        },
+        {
+            "column": "children_under_five",
+            "func": ["sum", "mean"],
+            "output": ["under5_total", "under5_avg"],
+        },
+        {
+            "column": "youth_15_24",
+            "func": ["sum", "mean"],
+            "output": ["youth_total", "youth_avg"],
+        },
+        {
+            "column": "women_of_reproductive_age_15_49",
+            "func": ["sum", "mean"],
+            "output": ["women_repro_total", "women_repro_avg"],
+        },
+        {
+            "column": "elderly_60_plus",
+            "func": ["sum", "mean"],
+            "output": ["elderly_total", "elderly_avg"],
+        },
+        {
+            "column": "land_area",
+            "func": ["sum", "mean", "min", "max", "std"],
+            "output": ["land_total", "land_avg", "land_min", "land_max", "land_std"],
+        },
+    ],
+    overlap_method="intersects",
+)
      
+
      
+
      CPU times: user 313 ms, sys: 9.88 ms, total: 322 ms
+Wall time: 320 ms
      
+
      
+
      
+
      
+
      aoi.columns.values
      
+
      
+
      array(['Reg_Code', 'Reg_Name', 'Reg_Alt_Name', 'geometry', 'bgy_count',
+       'pop_total', 'pop_avg', 'men_total', 'men_avg', 'women_total',
+       'women_avg', 'under5_total', 'under5_avg', 'youth_total',
+       'youth_avg', 'women_repro_total', 'women_repro_avg',
+       'elderly_total', 'elderly_avg', 'land_total', 'land_avg',
+       'land_min', 'land_max', 'land_std'], dtype=object)
      
+
      
+
      
+
      
+
      aoi.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
      Reg_CodeReg_NameReg_Alt_Namegeometrybgy_countpop_totalpop_avgmen_totalmen_avgwomen_total...youth_avgwomen_repro_totalwomen_repro_avgelderly_totalelderly_avgland_totalland_avgland_minland_maxland_std
      0130000000National Capital RegionNCRMULTIPOLYGON (((121.03842 14.78525, 121.03815 ...1707124845357313.72876461328283592.7521976350844...1468.07732936998392167.451084653306382.7217345.945955e+083.483277e+052890.9479062.750653e+071.065422e+06
      1030000000Region IIICentral LuzonMULTIPOLYGON (((120.11687 14.76309, 120.11684 ...3099105818593414.60438953533091727.4311075227035...655.7734752808810906.360116664150214.3110682.120885e+106.843771e+069589.4671143.496501e+081.826504e+07
      2040000000Region IV-ACalabarzonMULTIPOLYGON (((122.72165 13.36485, 122.72143 ...4010140818573511.68503770353341754.4473827044548...671.2261853857217961.899501780294194.5870321.551525e+103.869139e+063087.4523314.310269e+081.119929e+07
      
+
+
      3 rows × 24 columns
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Example 2 : Grids over Population per Bgy Level
      
+
        
+
      • Input:
+
          
+
        • aoi - region3 grids geometry (geom_type - polygon)
          • 
+
        • data - population data (geom_type - pois)
          • 
+
        • data_type: ‘individual_pois’
          • 
+
        • overlap_method = ‘intersects’
          • 
+
        • aggregations:
+
            
+
          • metric_columns: ‘population’,‘men’, ‘land_area’
+
              
+
            • each row in the data has a column population, men, women, including land with numeric value
              • 
+
            • aggregation_functions - ‘min’,‘max’, ‘mean’, ‘sum’, etc.
              • 
+
            • output_columns - ‘pop_min’, ‘pop_max’, for each
              • 
+
            • 
+
          • 
+
        • 
+
      • Output:
+
          
+
        • aoi with new columns pop_min, pop_max etc.
          • 
+
        • 
+
      
+
      Load population and land POIs (Bgy level)
      
+
      
+
      
+
      
+
+
      
+
      
+Note
+
      
+
      
+
      
+
      The dataset is similar to the previous one (Region 3, Region 4, and NCR) except that it has been filtered only data for the Region 3.
      
+
      
+
      
+
      
+
      region3_pop_pois = gpd.read_file("../data/region3_population_pois.geojson")
      
+
      
+
      CPU times: user 372 ms, sys: 0 ns, total: 372 ms
+Wall time: 370 ms
      
+
      
+
      
+
      
+
      region3_pop_pois.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
      BARANGAY_CODEpopulationmenwomenchildren_under_fiveyouth_15_24women_of_reproductive_age_15_49elderly_60_plusland_areaReg_Namegeometry
      031420020.01807920887202359468772.183518e+05Region IIIPOINT (121.05919 14.85825)
      134915006.03093159414993526588261597.327482e+06Region IIIPOINT (120.99590 15.62242)
      235403009.042412158208347482111122568.740450e+05Region IIIPOINT (120.77730 15.14580)
      335409023.03373175016232966118952446.484611e+05Region IIIPOINT (120.58052 15.22315)
      435413009.020884105391034421983893558912602.015571e+06Region IIIPOINT (120.68523 15.09163)
      
+
+
      
+
      
+
      
+
      
+
      
+
      region3_pop_pois.columns.values
      
+
      
+
      array(['BARANGAY_CODE', 'population', 'men', 'women',
+       'children_under_five', 'youth_15_24',
+       'women_of_reproductive_age_15_49', 'elderly_60_plus', 'land_area',
+       'Reg_Name', 'geometry'], dtype=object)
      
+
      
+
      
+
      Create zonal stats (same as previous example, but now for a more granular level for region 3 only)
      
+
      
+
      grid_aoi = vzs.create_zonal_stats(
+    grid_aoi,
+    region3_pop_pois,
+    aggregations=[
+        {"func": "count", "output": "bgy_count"},
+        {
+            "column": "population",
+            "func": ["sum", "mean"],
+            "output": ["pop_total", "pop_avg"],
+        },
+        {"column": "men", "func": ["sum", "mean"], "output": ["men_total", "men_avg"]},
+        {
+            "column": "women",
+            "func": ["sum", "mean"],
+            "output": ["women_total", "women_avg"],
+        },
+        {
+            "column": "children_under_five",
+            "func": ["sum", "mean"],
+            "output": ["under5_total", "under5_avg"],
+        },
+        {
+            "column": "youth_15_24",
+            "func": ["sum", "mean"],
+            "output": ["youth_total", "youth_avg"],
+        },
+        {
+            "column": "women_of_reproductive_age_15_49",
+            "func": ["sum", "mean"],
+            "output": ["women_repro_total", "women_repro_avg"],
+        },
+        {
+            "column": "elderly_60_plus",
+            "func": ["sum", "mean"],
+            "output": ["elderly_total", "elderly_avg"],
+        },
+        {
+            "column": "land_area",
+            "func": ["sum", "mean", "min", "max", "std"],
+            "output": ["land_total", "land_avg", "land_min", "land_max", "land_std"],
+        },
+    ],
+    overlap_method="intersects",
+)
      
+
      
+
      CPU times: user 115 ms, sys: 10.5 ms, total: 126 ms
+Wall time: 121 ms
      
+
      
+
      
+
      
+
      grid_aoi.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
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+
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      xygeometrypois_countbgy_countpop_totalpop_avgmen_totalmen_avgwomen_total...youth_avgwomen_repro_totalwomen_repro_avgelderly_totalelderly_avgland_totalland_avgland_minland_maxland_std
      0030POLYGON ((119.78583 15.70870, 119.83075 15.708...NaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
      1031POLYGON ((119.78583 15.75193, 119.83075 15.751...NaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
      2032POLYGON ((119.78583 15.79516, 119.83075 15.795...NaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
      3130POLYGON ((119.83075 15.70870, 119.87566 15.708...NaN1.03415.03415.01744.01744.01670.0...648.0848.0848.0229.0229.05.552401e+065.552401e+065.552401e+065.552401e+06NaN
      4132POLYGON ((119.83075 15.79516, 119.87566 15.795...NaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
      
+
+
      5 rows × 24 columns
      
+
      
+
      
+
      
+
      
+
      Show grids with bgy_count > 0 and bgy_count == 0
      
+
      
+
      ax = aoi[aoi.Reg_Name == "Region III"].plot(
+    ax=plt.axes(), facecolor="none", edgecolor="black"
+)
+ax = grid_aoi[grid_aoi.bgy_count.notna()].plot(
+    ax=ax, facecolor="none", edgecolor="blue"
+)
+ax = grid_aoi[grid_aoi.bgy_count.isna()].plot(ax=ax, facecolor="none", edgecolor="red")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Generating Bing Tile Grid Zonal Stats
      
+
      If our areas of interest use Bing Tile Grids (with the associated quadkeys),
      
+
      we can use a much faster way of generating zonal stats by pre-computing the quadkeys for our data.
      
+
      
+
      
+
      
+
+
      
+
      
+Note
+
      
+
      
+
      
+
      You can use the geowrangler.grids modules BingTileGridGenerator to convert your AOI into a Bing Tile Grid AOI.
      
+
      
+
      
+
      
+
      Example 1: Count POIs for Region 3 Bing Tile Grid AOI
      
+
        
+
      • Input:
+
          
+
        • aoi bing tile grids zoom level 13 - region3 (Admin Level 1) (Central Luzon) geometry (geom_type - polygon, multipolygon)
          • 
+
        • data - Region 3 pois (geom_type - points)
          • 
+
        • aggregations:
+
            
+
          • count - number of pois within aoi
            • 
+
          • 
+
        • 
+
      • Output
+
          
+
        • aoi with pois count (default output column: index_count)
          • 
+
        • 
+
      
+
      Load our bing tile grid aoi
      
+
      
+
      # load region3_admin area in bing tile grid ('ADM level 1 - Philippines, region 3, zoom level13')
+
+region3_bingtile_grid = gpd.read_file("../data/region3_bingtile_grid13.geojson")
      
+
      
+
      
+
      region3_bingtile_grid.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      quadkeygeometry
      01323030303301POLYGON ((120.10254 14.73239, 120.10254 14.774...
      11323030303300POLYGON ((120.05859 14.73239, 120.05859 14.774...
      21323030303311POLYGON ((120.19043 14.73239, 120.19043 14.774...
      31323030303133POLYGON ((120.19043 14.77488, 120.19043 14.817...
      41323030303131POLYGON ((120.19043 14.81737, 120.19043 14.859...
      
+
+
      
+
      
+
      
+
      
+
      
+
      ax = plt.axes()
+ax = aoi[aoi.Reg_Name == "Region III"].plot(ax=ax, facecolor="none", edgecolor="blue")
+ax = region3_bingtile_grid.plot(ax=ax, facecolor="none", edgecolor="red")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Lets compute the quadkeys for our Region3 pois
      
+
      
+
      DATA_ZOOM_LEVEL = 23  # for pois, it can be as high as 24
      
+
      
+
      
+
      region3_pois_quadkeys = vzs.compute_quadkey(region3_pois, DATA_ZOOM_LEVEL)
      
+
      
+
      CPU times: user 236 ms, sys: 320 µs, total: 236 ms
+Wall time: 233 ms
      
+
      
+
      
+
      
+
      region3_pois_quadkeys.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      osm_idcodefclassnameBARANGAY_CODEgeometryquadkey
      05604109862701tourist_infoGawad Kalinga Office031420009POINT (121.08516 14.83601)13230312020312101332220
      112443016722701tourist_infoN 15deg 26' 11.1", E 120deg 25' 50.2", El...036918006POINT (120.43045 15.43663)13230301323000331033200
      216666843932701tourist_infoEco Park Tourist Information & DENR Environmen...036918006POINT (120.44958 15.46446)13230301321223132232132
      316799929292701tourist_infoLa Paz Tarlac - Zaragoza Nueva Ecija Boundary034932027POINT (120.75832 15.44284)13230301332111310110220
      417146457292701tourist_infoLucy Pineda035409019POINT (120.61452 15.23359)13230303110021032011230
      
+
+
      
+
      
+
      
+
      
+
      Lets compute the zonal stats for each grid as the pois_count. > Notice the computation time is pretty fast
      
+
      
+
      region3_bingtile_pois = vzs.create_bingtile_zonal_stats(
+    region3_bingtile_grid,
+    region3_pois_quadkeys,
+    aggregations=[dict(func="count", output="pois_count", fillna=True)],
+)
      
+
      
+
      CPU times: user 71.6 ms, sys: 0 ns, total: 71.6 ms
+Wall time: 69.3 ms
      
+
      
+
      
+
      
+
      region3_bingtile_pois[region3_bingtile_pois.pois_count > 0].head()
      
+
      
+
      
+
      
+
+
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+
+
+
      quadkeygeometrypois_count
      51323030312020POLYGON ((120.23438 14.81737, 120.23438 14.859...8.0
      81323030121200POLYGON ((119.88281 15.74996, 119.88281 15.792...1.0
      111323030121222POLYGON ((119.88281 15.62304, 119.88281 15.665...1.0
      131323030123002POLYGON ((119.88281 15.53838, 119.88281 15.580...1.0
      221323030123001POLYGON ((119.92676 15.58071, 119.92676 15.623...1.0
      
+
+
      
+
      
+
      
+
      
+
      
+
      ax = aoi[aoi.Reg_Name == "Region III"].plot(
+    ax=plt.axes(), facecolor="none", edgecolor="black"
+)
+ax = region3_bingtile_pois[region3_bingtile_pois.pois_count > 0].plot(
+    column="pois_count", ax=ax, edgecolor="blue", alpha=0.4
+)
+ax = region3_bingtile_pois[region3_bingtile_pois.pois_count == 0].plot(
+    ax=ax, facecolor="none", edgecolor="red", alpha=0.4
+)
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Example 2: Population stats for Region 3 Bing Tile Grid AOI
      
+
        
+
      • Input:
+
          
+
        • aoi bing tile grids zoom level 13 - region3 (Admin Level 1) (Central Luzon) geometry (geom_type - polygon, multipolygon)
          • 
+
        • data - Region 3 pop data (geom_type - points)
          • 
+
        • aggregations:
+
            
+
          • pop totals, avg, men total and avg, etc , barangay counts, etc.
            • 
+
          • 
+
        • 
+
      • Output
+
          
+
        • aoi bing tile grids zoom level 13 with additional stats
          • 
+
        • 
+
      
+
      We can also reuse the region34ncr population data to add more zonal statistics to our region3 bingtile pois
      
+
      
+
      # Add quadkeys to region34ncr population data
+region34ncr_pop_data = vzs.compute_quadkey(
+    region34ncr_pop_data, DATA_ZOOM_LEVEL, inplace=True
+)  # inplace saves copying
      
+
      
+
      CPU times: user 1.5 s, sys: 0 ns, total: 1.5 s
+Wall time: 1.5 s
      
+
      
+
      
+
      
+
      region34ncr_pop_data.head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
      BARANGAY_CODEpopulationmenwomenchildren_under_fiveyouth_15_24women_of_reproductive_age_15_49elderly_60_plusland_areageometryquadkey
      031420020.01807920887202359468772.183518e+05POINT (121.05919 14.85825)13230312020301100210231
      134915006.03093159414993526588261597.327482e+06POINT (120.99590 15.62242)13230310220010101012220
      235403009.042412158208347482111122568.740450e+05POINT (120.77730 15.14580)13230303111220301103213
      335409023.03373175016232966118952446.484611e+05POINT (120.58052 15.22315)13230303101131332000021
      435413009.020884105391034421983893558912602.015571e+06POINT (120.68523 15.09163)13230303112102100203003
      
+
+
      
+
      
+
      
+
      
+
      
+
      region3_bingtile_pois_pop_data = vzs.create_bingtile_zonal_stats(
+    region3_bingtile_pois,  # reuse from previous example
+    region34ncr_pop_data,  # updated with quadkeys
+    aggregations=[
+        {"func": "count", "output": "bgy_count"},
+        {
+            "column": "population",
+            "func": ["sum", "mean"],
+            "output": ["pop_total", "pop_avg"],
+        },
+        {"column": "men", "func": ["sum", "mean"], "output": ["men_total", "men_avg"]},
+        {
+            "column": "women",
+            "func": ["sum", "mean"],
+            "output": ["women_total", "women_avg"],
+        },
+        {
+            "column": "children_under_five",
+            "func": ["sum", "mean"],
+            "output": ["under5_total", "under5_avg"],
+        },
+        {
+            "column": "youth_15_24",
+            "func": ["sum", "mean"],
+            "output": ["youth_total", "youth_avg"],
+        },
+        {
+            "column": "women_of_reproductive_age_15_49",
+            "func": ["sum", "mean"],
+            "output": ["women_repro_total", "women_repro_avg"],
+        },
+        {
+            "column": "elderly_60_plus",
+            "func": ["sum", "mean"],
+            "output": ["elderly_total", "elderly_avg"],
+        },
+        {
+            "column": "land_area",
+            "func": ["sum", "mean", "min", "max", "std"],
+            "output": ["land_total", "land_avg", "land_min", "land_max", "land_std"],
+        },
+    ],
+)
      
+
      
+
      CPU times: user 528 ms, sys: 0 ns, total: 528 ms
+Wall time: 526 ms
      
+
      
+
      
+
      
+
      region3_bingtile_pois_pop_data[region3_bingtile_pois_pop_data.bgy_count.notna()].head()
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
      quadkeygeometrypois_countbgy_countpop_totalpop_avgmen_totalmen_avgwomen_totalwomen_avg...youth_avgwomen_repro_totalwomen_repro_avgelderly_totalelderly_avgland_totalland_avgland_minland_maxland_std
      51323030312020POLYGON ((120.23438 14.81737, 120.23438 14.859...8.05.040890.08178.00000020398.04079.60000020490.04098.000000...1662.00000011493.02298.6000002337.0467.44.156777e+068.313554e+051.319242e+052.503691e+069.727404e+05
      61323030120313POLYGON ((119.83887 15.70766, 119.83887 15.749...0.01.03415.03415.0000001744.01744.0000001670.01670.000000...648.000000848.0848.000000229.0229.05.552401e+065.552401e+065.552401e+065.552401e+06NaN
      71323030121022POLYGON ((119.88281 15.79225, 119.88281 15.834...0.01.04742.04742.0000002438.02438.0000002304.02304.000000...828.0000001114.01114.000000370.0370.05.485826e+065.485826e+065.485826e+065.485826e+06NaN
      81323030121200POLYGON ((119.88281 15.74996, 119.88281 15.792...1.07.021755.03107.85714311047.01578.14285710707.01529.571429...587.8571435364.0766.2857141631.0233.01.343751e+071.919645e+065.222719e+054.416378e+061.395113e+06
      91323030121202POLYGON ((119.88281 15.70766, 119.88281 15.749...0.05.010146.02029.2000005257.01051.4000004887.0977.400000...376.6000002393.0478.600000668.0133.61.836666e+073.673332e+062.364678e+065.247429e+061.068957e+06
      
+
+
      5 rows × 23 columns
      
+
      
+
      
+
      
+
      
+
      
+
      ax = aoi[aoi.Reg_Name == "Region III"].plot(
+    ax=plt.axes(), facecolor="none", edgecolor="black"
+)
+ax = region3_bingtile_pois_pop_data[
+    region3_bingtile_pois_pop_data.bgy_count.notna()
+].plot(column="pop_total", ax=ax, edgecolor="blue", alpha=0.4)
+ax = region3_bingtile_pois_pop_data[
+    region3_bingtile_pois_pop_data.bgy_count.isna()
+].plot(ax=ax, facecolor="none", edgecolor="red", alpha=0.4)
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
+
+
      
+
      
+
+
       
+
+
       
+
+
+
+
+
\ No newline at end of file
diff --git a/tutorial.vector_zonal_stats_files/figure-html/cell-11-output-1.png b/tutorial.vector_zonal_stats_files/figure-html/cell-11-output-1.png
new file mode 100644
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zhHCZo)2#AN)7Jd@Tl;n&-2|eeUJQE>_CJpQ1YQ6D

literal 0
HcmV?d00001

diff --git a/validation.html b/validation.html
new file mode 100644
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+++ b/validation.html
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+
+
+
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+
+geowrangler - Geometry Validation
+
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+  
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+    
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+
      
+
      Geometry Validation
      
+
      
+
+
      
+  
      
+    geometry validations
+  
      
+
      
+
+
+
      
+
+    
+  
+    
+  
      
+  
+
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+
      
+
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+
      
+
      
+
      source
      
+
      
+
      ValidationError
      
+
      Common base class for all non-exit exceptions.
      
+
      
+
      source
      
+
      
+
      
+
      BaseValidator
      
+
      
+
       BaseValidator (add_new_column:bool=True, apply_fix:bool=True)
      
+
      
+
      Abstract Base Class for single validator
      
+
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      TypeDefaultDetails
      add_new_columnboolTrueAdd new column to show errors
      apply_fixboolTrueUpdate geometry
      
+
      
+
      source
      
+
      
+
      
+
      BaseValidator.validate
      
+
      
+
       BaseValidator.validate (gdf:geopandas.geodataframe.GeoDataFrame,
+                         clone=True)
      
+
      
+
      Method that checks the validity of a each geometry and applies a fix to these geometries or raise a warning
      
+
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      TypeDefaultDetails
      gdfGeoDataFrameGeoDataFrame to validate
      cloneboolTrueApply validation to copy
      ReturnsGeoDataFrame
      
+
      
+
      source
      
+
      
+
      
+
      OrientationValidator
      
+
      
+
       OrientationValidator (add_new_column:bool=True, apply_fix:bool=True)
      
+
      
+
      Checks and fixes Orientation of the geometry to ensure it follows a counter-clockwise orientation
      
+
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      TypeDefaultDetails
      add_new_columnboolTrueAdd new column to show errors
      apply_fixboolTrueUpdate geometry
      
+
      
+
      source
      
+
      
+
      
+
      OrientationValidator.check
      
+
      
+
       OrientationValidator.check (geometry:shapely.geometry.base.BaseGeometry)
      
+
      
+
      Checks if orientation is counter clockwise
      
+
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+
      TypeDetails
      geometryBaseGeometryGeometry to validate
      Returnsbool
      
+
      
+
      source
      
+
      
+
      
+
      OrientationValidator.fix
      
+
      
+
       OrientationValidator.fix (geometry:shapely.geometry.base.BaseGeometry)
      
+
      
+
      Fixes orientation if orientation is clockwise
      
+
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+
      TypeDetails
      geometryBaseGeometryGeometry to fix
      ReturnsBaseGeometry
      
+
      
+
      source
      
+
      
+
      
+
      CrsBoundsValidator
      
+
      
+
       CrsBoundsValidator (add_new_column:bool=True, apply_fix:bool=True)
      
+
      
+
      Checks bounds of the geometry to ensure it is within bounds of the crs
      
+
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      TypeDefaultDetails
      add_new_columnboolTrueAdd new column to show errors
      apply_fixboolTrueUpdate geometry
      
+
      
+
      source
      
+
      
+
      
+
      CrsBoundsValidator.get_check_arguments
      
+
      
+
       CrsBoundsValidator.get_check_arguments
+                                         (gdf:geopandas.geodataframe.GeoDa
+                                         taFrame)
      
+
      
+
      Return check arguments
      
+
+
+
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+
      TypeDetails
      gdfGeoDataFrameGeoDataFrame to check
      Returnsdict
      
+
      
+
      source
      
+
      
+
      
+
      CrsBoundsValidator.check
      
+
      
+
       CrsBoundsValidator.check (geometry:shapely.geometry.base.BaseGeometry,
+                           gdf:geopandas.geodataframe.GeoDataFrame)
      
+
      
+
      Checks if polygon is within bounds of crs.
      
+
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      TypeDetails
      geometryBaseGeometryGeometry to validate
      gdfGeoDataFrameGeoDataframe to check
      Returnsbool
      
+
      
+
      source
      
+
      
+
      
+
      CrsBoundsValidator.fix
      
+
      
+
       CrsBoundsValidator.fix (geometry:shapely.geometry.base.BaseGeometry)
      
+
      
+
      No fix available
      
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      TypeDetails
      geometryBaseGeometryGeometry to fix
      ReturnsBaseGeometrypragma: no cover
      
+
      
+
      source
      
+
      
+
      
+
      SelfIntersectingValidator
      
+
      
+
       SelfIntersectingValidator (add_new_column:bool=True, apply_fix:bool=True)
      
+
      
+
      Checks bounds of the geometry to ensure it is within bounds or crs
      
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      TypeDefaultDetails
      add_new_columnboolTrueAdd new column to show errors
      apply_fixboolTrueUpdate geometry
      
+
      
+
      source
      
+
      
+
      
+
      SelfIntersectingValidator.check
      
+
      
+
       SelfIntersectingValidator.check
+                                  (geometry:shapely.geometry.base.BaseGeom
+                                  etry)
      
+
      
+
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      TypeDetails
      geometryBaseGeometryGeometry to check
      Returnsbool
      
+
      
+
      source
      
+
      
+
      
+
      SelfIntersectingValidator.fix
      
+
      
+
       SelfIntersectingValidator.fix
+                                (geometry:shapely.geometry.base.BaseGeomet
+                                ry)
      
+
      
+
      Fix intersection geometry by applying shapely.validation.make_valid
      
+
      
+
      source
      
+
      
+
      
+
      NullValidator
      
+
      
+
       NullValidator (add_new_column:bool=True, apply_fix:bool=True)
      
+
      
+
      Checks bounds of the geometry to ensure it is within bounds or crs
      
+
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      TypeDefaultDetails
      add_new_columnboolTrueAdd new column to show errors
      apply_fixboolTrueUpdate geometry
      
+
      
+
      source
      
+
      
+
      
+
      NullValidator.check
      
+
      
+
       NullValidator.check (geometry:shapely.geometry.base.BaseGeometry)
      
+
      
+
      Checks if polygon is null
      
+
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+
      TypeDetails
      geometryBaseGeometry
      ReturnsboolGeometry to check
      
+
      
+
      source
      
+
      
+
      
+
      NullValidator.fix
      
+
      
+
       NullValidator.fix (geometry:shapely.geometry.base.BaseGeometry)
      
+
      
+
      No fix available
      
+
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+
      TypeDetails
      geometryBaseGeometryGeometry to fix
      ReturnsBaseGeometrypragma: no cover
      
+
      
+
      source
      
+
      
+
      
+
      AreaValidator
      
+
      
+
       AreaValidator (add_new_column:bool=True, apply_fix:bool=True)
      
+
      
+
      Checks area of the geometry to ensure it greater than 0
      
+
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      TypeDefaultDetails
      add_new_columnboolTrueAdd new column to show errors
      apply_fixboolTrueUpdate geometry
      
+
      
+
      source
      
+
      
+
      
+
      AreaValidator.check
      
+
      
+
       AreaValidator.check (geometry:shapely.geometry.base.BaseGeometry)
      
+
      
+
      Checks if area is greater than 0
      
+
      
+
      source
      
+
      
+
      
+
      AreaValidator.fix
      
+
      
+
       AreaValidator.fix (geometry:shapely.geometry.base.BaseGeometry)
      
+
      
+
      No fix available
      
+
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      TypeDetails
      geometryBaseGeometryGeometry to fix
      ReturnsBaseGeometrypragma: no cover
      
+
      
+
      source
      
+
      
+
      
+
      GeometryValidation
      
+
      
+
       GeometryValidation (gdf:geopandas.geodataframe.GeoDataFrame,
+                     validators:Sequence[Union[str,__main__.BaseValidator]
+                     ]=('null', 'self_intersecting', 'orientation',
+                     'crs_bounds', 'area'),
+                     add_validation_columns:bool=True,
+                     apply_fixes:bool=True)
      
+
      
+
      Applies a list of validation checks and tries to fix them
      
+
++++++
+
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+
      





      TypeDefaultDetails
      gdfGeoDataFrameGeoDataFrame to validate
      validatorsSequence(‘null’, ‘self_intersecting’, ‘orientation’, ‘crs_bounds’, ‘area’)Validators to apply
      add_validation_columnsboolTrueAdd column to show errors
      apply_fixesboolTrueUpdate geometry
      ReturnsGeoDataFrame
      
+
      
+
      source
      
+
      
+
      
+
      GeometryValidation.validate_all
      
+
      
+
       GeometryValidation.validate_all ()
      
+
      
+
      Sequentially run validators
      
+
+
+
      
+
+
       
+
+
       
+
+
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+
+
\ No newline at end of file
diff --git a/vector_to_raster_mask.html b/vector_to_raster_mask.html
new file mode 100644
index 0000000..440f697
--- /dev/null
+++ b/vector_to_raster_mask.html
@@ -0,0 +1,971 @@
+
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+
+geowrangler - Vector to Raster mask
+
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+  
      
+    
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+
      
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+  
+
      
+
+    
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+
      
+
+
      
+
      
+
      Vector to Raster mask
      
+
      
+
+
      
+  
      
+    convert vectors into rasters where each pixel is a numerical representation of a feature in the vector data
+  
      
+
      
+
+
+
      
+
+    
+  
+    
+  
      
+  
+
+
+
      
+
+
+
+
      
+
      
+
      from rasterio import features
+from rasterio.plot import show
+from rasterio.windows import Window, transform
      
+
      
+
      
+
      source
      
+
      
+
      generate_mask
      
+
      
+
       generate_mask (tiff_file, shape_file, output_file, labels_column,
+                labels_dict:Dict[str,Any], plot=False)
      
+
      
+
      Generates a segmentation mask for one TIFF image. Arguments: tiff_file (str): Path to reference TIFF file || shape_file (str): Path to shapefile || output_file (str): Path to output file || labels_column (str): Feature in the shapefile that contains labels/categories || labels_dict (dict): Dictionary of desired labels and assigned values for the mask || Returns: image (np.array): A binary mask as a numpy array
      
+
      
+
      
+
      Test data
      
+
      
+
      Generating a raster mask
      
+
      
+
      # Get filepaths
+tiff_file = "../data/vector_to_raster_mask_sample/cabanglasan.tif"
+shape_file = "../data/vector_to_raster_mask_sample/labels_20220816.gpkg"
+target_file = shape_file.replace("gpkg", "tiff")
      
+
      
+
      Given a raster image of a certain area that will be masked to use as a reference and a shape file that contains that area. Note that the shape file must include a column that contains labels/categories.
      
+
      
+
      gpd.read_file(shape_file).head(3)
      
+
      
+
      
+
      
+
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+
      yearlabeluidADM3_ENADM3_PCODEADM2_ENADM2_PCODEADM1_ENADM1_PCODEgeometry
      02017.0mining72_2017_miningSofronio EspañolaPH175324000PalawanPH175300000Region IV-BPH170000000MULTIPOLYGON Z (((117.95961 9.03303 0.00000, 1...
      12017.0mining71_2017_miningSofronio EspañolaPH175324000PalawanPH175300000Region IV-BPH170000000MULTIPOLYGON Z (((117.95507 9.03809 0.00000, 1...
      22017.0mining70_2017_miningSofronio EspañolaPH175324000PalawanPH175300000Region IV-BPH170000000MULTIPOLYGON Z (((117.95663 9.03869 0.00000, 1...
      
+
+
      
+
      
+
      
+
      
+
      And a dictionary with the labels and assigned values to be used in creating a mask
      
+
      
+
      labels = {
+    "mining": 1,
+    "neg": 2,
+    "agriculture": 3,
+    "product_extraction": 4,
+    "kaingin": 5,
+    "biophysical": 6,
+}
      
+
      
+
      Input them in the generate_mask function to create a raster mask of the same dimension as the reference raster image
      
+
      
+
      # Generate masks for a file
+masks, grids, values = generate_mask(
+    tiff_file=tiff_file,
+    shape_file=shape_file,
+    output_file=target_file,
+    labels_column="label",
+    labels_dict=labels,
+    plot=True,
+)
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      masks
      
+
      
+
      array([[0, 0, 0, ..., 0, 0, 0],
+       [0, 0, 0, ..., 0, 0, 0],
+       [0, 0, 0, ..., 0, 0, 0],
+       ...,
+       [0, 0, 0, ..., 0, 0, 0],
+       [0, 0, 0, ..., 0, 0, 0],
+       [0, 0, 0, ..., 0, 0, 0]], dtype=uint16)
      
+
      
+
      
+
      
+
      grids
      
+
      
+
      array([[0, 0, 0, ..., 0, 0, 0],
+       [0, 0, 0, ..., 0, 0, 0],
+       [0, 0, 0, ..., 0, 0, 0],
+       ...,
+       [0, 0, 0, ..., 0, 0, 0],
+       [0, 0, 0, ..., 0, 0, 0],
+       [0, 0, 0, ..., 0, 0, 0]], dtype=uint16)
      
+
      
+
      
+
      
+
      values
      
+
      
+
      {'mining': 1,
+ 'neg': 2,
+ 'agriculture': 3,
+ 'product_extraction': 4,
+ 'kaingin': 5,
+ 'biophysical': 6}
      
+
      
+
      
+
+
+
      
+
      
+
+
       
+
+
       
+
+
+
+
+
\ No newline at end of file
diff --git a/vector_zonal_stats.html b/vector_zonal_stats.html
new file mode 100644
index 0000000..b6055c2
--- /dev/null
+++ b/vector_zonal_stats.html
@@ -0,0 +1,1744 @@
+
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+
+geowrangler - Vector Zonal Stats
+
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+
      
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+    
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+  
+
      
+
+    
+
+
      
+
+
      
+
      
+
      Vector Zonal Stats
      
+
      
+
+
      
+  
      
+    generate vector zonal stat features
+  
      
+
      
+
+
+
      
+
+    
+  
+    
+  
      
+  
+
+
+
      
+
+
+
+
      
+
      
+
      Aggregations
      
+
      In order to generate zonal stats for an area (or areas) of interest (aoi) , we have come up with the concept of an aggregation specification or agg spec, which is a way to specify what aggregation functions (such as count,sum, mean,std etc.) are to be applied to columns in the source dataframe (data).
      
+
      The method create_zonal_stats can then take in a list of these agg specs and apply them to create zonal stats from the data for the aoi.
      
+
      Each agg spec consists of a dict with the following keys:
      
+
        
+
      • func: (Required) a str or a list [str] of aggregation functions. See the pandas documentation for agg
        • 
+
      • column: (Optional) an existing column in the data to generate the zonal statistic from. If not specified, the grouping key based on the index of the aoi applied to the data is used as default.
        • 
+
      • output: (Optional) a str or a list [str] of the name(s) of the output zonal statistic column. If not specified it is concatenated from the column and func i.e. {column}_{func} (e.g. 'func':'mean' on 'column':'population' has a default value 'output':'population_mean')
        • 
+
      • fillna: (Optional) a bool or a list [bool] of the flag(s) that indicates whether to to a fillna(0) step for the new zonal column, True meaning it will set any NA values in the resulting zonal stat to 0, and False will retain any NA values. The default value of the flag(s) is False.
        • 
+
      
+
      Examples
      
+
        
+
      • The simplest aggregation spec. This will result in an output column named index_count as it will use the aoi index as the default column.
        • 
+
      
+
      {"func":"count"}
      
+
        
+
      • The sum function is applied to the data column population which will create an output column named total_population.
        • 
+
      
+
      {
+ "func:"sum",
+ "column": "population",
+ "output": "total_population"
+}
      
+
        
+
      • Compute the zonal stats mean,sum,max on the population column and rename the output columns (by default) to population_mean, population_sum and population_max.
        • 
+
      
+
      {
+ "func": ["mean","sum","max"],
+ "column": "population",
+}
      
+
        
+
      • A full aggregation spec with fillna. fillna == False for std means it will remain an NA if there is no data for the column in the group. The default value for fillna is True which means that 0 is used to replace any NA in the output column.
        • 
+
      
+
      {
+ "func": ["mean", "sum", "std"],
+ "column":"population",
+ "output": ["avg_pop", "total_pop", "std_dev"],
+ "fillna": [True,True,False],
+}
      
+
      The agg spec in the list of aggregations can contain the same columns, but the output columns must be unique since they will added as columns in the results.
      
+
      
+
      
+
      Regular and Grid Zonal Stats
      
+
      
+
      Vector zonal stats for user defined areas and grids (e.g. Admin areas)
      
+
      
+
      
+
      source
      
+
      
+
      
+
      create_zonal_stats
      
+
      
+
       create_zonal_stats (aoi:geopandas.geodataframe.GeoDataFrame,
+                     data:geopandas.geodataframe.GeoDataFrame,
+                     aggregations:List[Dict[str,Any]],
+                     overlap_method:str='intersects')
      
+
      
+
      Create zonal stats for area of interest from data using aggregration operations on data columns. Returns the same aoi with additional columns containing the computed zonal features.
      
+
+
+
+
+
+
+
+
+
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+
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+
+
+
+
+
+
+
+
      TypeDefaultDetails
      aoiGeoDataFrameArea of interest for which zonal stats are to be computed for
      dataGeoDataFrameSource gdf containing data to compute zonal stats from
      aggregationsListList of agg specs, with each agg spec applied to a data column
      overlap_methodstrintersectsspatial predicate to used in spatial join of aoi and data geopandas.sjoin for more details
      ReturnsGeoDataFramecategorical_column_options: str = None,
      
+
      
+
      simple_aoi  # sample aoi
      
+
      
+
      
+
      
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      col1lat0lon0lat1lon1lat2lon2lat3lon3geometry
      010.00.00.01.01.01.01.00.0POLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....
      121.00.01.01.02.01.02.00.0POLYGON ((1.00000 0.00000, 1.00000 1.00000, 2....
      232.00.02.01.03.01.03.00.0POLYGON ((2.00000 0.00000, 2.00000 1.00000, 3....
      
+
+
      
+
      
+
      
+
      
+
      
+
      simple_data  # sample data
      
+
      
+
      
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+
      col1latlongeometry
      010.500.50POINT (0.50000 0.50000)
      121.500.50POINT (1.50000 0.50000)
      232.500.50POINT (2.50000 0.50000)
      340.450.50POINT (0.45000 0.50000)
      451.450.50POINT (1.45000 0.50000)
      562.450.50POINT (2.45000 0.50000)
      670.450.45POINT (0.45000 0.45000)
      781.450.45POINT (1.45000 0.45000)
      892.450.45POINT (2.45000 0.45000)
      9100.451.45POINT (0.45000 1.45000)
      10111.451.45POINT (1.45000 1.45000)
      11122.451.45POINT (2.45000 1.45000)
      
+
+
      
+
      
+
      
+
      
+
      
+
      ax = simple_aoi.plot(
+    ax=plt.axes(), facecolor="none", edgecolor=["red", "blue", "green"]
+)
+ax = simple_data.plot(ax=ax, color="purple")
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      results = create_zonal_stats(simple_aoi, simple_data, aggregations=[{"func": "count"}])
+results
      
+
      
+
      
+
      
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+
      col1lat0lon0lat1lon1lat2lon2lat3lon3geometryindex_count
      010.00.00.01.01.01.01.00.0POLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....3
      121.00.01.01.02.01.02.00.0POLYGON ((1.00000 0.00000, 1.00000 1.00000, 2....3
      232.00.02.01.03.01.03.00.0POLYGON ((2.00000 0.00000, 2.00000 1.00000, 3....3
      
+
+
      
+
      
+
      
+
      
+
      Index name is not none
      
+
      
+
      named_index_aoi = simple_aoi.copy()
+named_index_aoi.index.name = "myindex"
      
+
      
+
      
+
      named_index_aoi
      
+
      
+
      
+
      
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+
      col1lat0lon0lat1lon1lat2lon2lat3lon3geometry
      myindex
      010.00.00.01.01.01.01.00.0POLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....
      121.00.01.01.02.01.02.00.0POLYGON ((1.00000 0.00000, 1.00000 1.00000, 2....
      232.00.02.01.03.01.03.00.0POLYGON ((2.00000 0.00000, 2.00000 1.00000, 3....
      
+
+
      
+
      
+
      
+
      
+
      
+
      named_index_results = create_zonal_stats(
+    named_index_aoi, simple_data, aggregations=[{"func": "count"}]
+)
      
+
      
+
      
+
      named_index_results.head()
      
+
      
+
      
+
      
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+
      col1lat0lon0lat1lon1lat2lon2lat3lon3geometryindex_count
      myindex
      010.00.00.01.01.01.01.00.0POLYGON ((0.00000 0.00000, 0.00000 1.00000, 1....3
      121.00.01.01.02.01.02.00.0POLYGON ((1.00000 0.00000, 1.00000 1.00000, 2....3
      232.00.02.01.03.01.03.00.0POLYGON ((2.00000 0.00000, 2.00000 1.00000, 3....3
      
+
+
      
+
      
+
      
+
      
+
      
+
      
+
      Bing Map Tile Grid Zonal Stats
      
+
      
+
      Generating zonal stats for Bing tile grid AOIs
      
+
      
+
      
+
      source
      
+
      
+
      
+
      compute_quadkey
      
+
      
+
       compute_quadkey (data:geopandas.geodataframe.GeoDataFrame,
+                  zoom_level:int, inplace:bool=False,
+                  quadkey_column:str='quadkey')
      
+
      
+
      Computes the quadkeys for the geometries of the data. If geometries are not points, the quadkeys are computed from the centroids of the geometries.
      
+
++++++
+
+
+
+
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+
+
+
+
+
+
+
      





      TypeDefaultDetails
      dataGeoDataFrameThe geodataframe
      zoom_levelintThe quadkey zoom level (1-23)
      inplaceboolFalseWhether to change data inplace or not
      quadkey_columnstrquadkeyThe name of the quadkey output column
      ReturnsGeoDataFrame
      
+
      If our existing data geodataframe doesn’t have quadkeys, we can use the compute_quadkey to generate the quadkeys for the centroid of the data’s geometries.
      
+
      
+
      DATA_ZOOM_LEVEL = 19
+AOI_ZOOM_LEVEL = 9
+simple_data_quadkey = compute_quadkey(simple_data, DATA_ZOOM_LEVEL)
      
+
      
+
      CPU times: user 34.6 ms, sys: 0 ns, total: 34.6 ms
+Wall time: 34.4 ms
      
+
      
+
      
+
      
+
      simple_data_quadkey.head()
      
+
      
+
      
+
      
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+
      col1latlongeometryquadkey
      010.500.5POINT (0.50000 0.50000)1222222221211211222
      121.500.5POINT (1.50000 0.50000)1222222320210201222
      232.500.5POINT (2.50000 0.50000)1222222331200311222
      340.450.5POINT (0.45000 0.50000)1222222221210201333
      451.450.5POINT (1.45000 0.50000)1222222320200311333
      
+
+
      
+
      
+
      
+
      
+
      
+
      source
      
+
      
+
      
+
      create_bingtile_zonal_stats
      
+
      
+
       create_bingtile_zonal_stats (aoi:pandas.core.frame.DataFrame,
+                              data:pandas.core.frame.DataFrame,
+                              aggregations:List[Dict[str,Any]],
+                              aoi_quadkey_column:str='quadkey',
+                              data_quadkey_column:str='quadkey')
      
+
      
+
++++++
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
      





      TypeDefaultDetails
      aoiDataFrameAn aoi with quadkey column
      dataDataFrameData with quadkey column
      aggregationsListList of agg specs, with each agg spec applied to a data column
      aoi_quadkey_columnstrquadkeyColumn name of aoi quadkey
      data_quadkey_columnstrquadkeyColumn name of data quadkey
      ReturnsDataFrame
      
+
      To create bingtile zonal stats, we need to compute the quadkeys for the areas of interest (AOI) and the data.
      
+
      The geowrangler.grids module provides a BingTileGridGenerator that will generate the quadkeys for the areas covered by your AOIs.
      
+
      
+
      import geowrangler.grids as gr
      
+
      
+
      
+
      bgtile_generator = gr.BingTileGridGenerator(AOI_ZOOM_LEVEL)
+simple_aoi_bingtiles = bgtile_generator.generate_grid(simple_aoi)
      
+
      
+
      Using the data with the computed quadkeys, we can generate zonal stats for our bingtile grid aois. This just uses the regular pandas grouping and merging function and skips any geospatial joins which results in faster computation.
      
+
      
+
      bingtile_results = create_bingtile_zonal_stats(
+    simple_aoi_bingtiles,
+    simple_data_quadkey,
+    aggregations=[dict(func="count", fillna=True)],
+)
      
+
      
+
      CPU times: user 35.7 ms, sys: 2.56 ms, total: 38.2 ms
+Wall time: 37.5 ms
      
+
      
+
      
+
      
+
      bingtile_results
      
+
      
+
      
+
      
+
+
+
+
+
+
+
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+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      quadkeygeometryindex_count
      0122222220POLYGON ((-0.00000 0.70311, -0.00000 1.40611, ...0.0
      1122222222POLYGON ((-0.00000 0.00000, -0.00000 0.70311, ...3.0
      2122222221POLYGON ((0.70312 0.70311, 0.70312 1.40611, 1....0.0
      3122222223POLYGON ((0.70312 0.00000, 0.70312 0.70311, 1....0.0
      4122222230POLYGON ((1.40625 0.70311, 1.40625 1.40611, 2....0.0
      5122222232POLYGON ((1.40625 0.00000, 1.40625 0.70311, 2....3.0
      6122222231POLYGON ((2.10937 0.70311, 2.10937 1.40611, 2....0.0
      7122222233POLYGON ((2.10937 0.00000, 2.10937 0.70311, 2....3.0
      8122222320POLYGON ((2.81250 0.70311, 2.81250 1.40611, 3....0.0
      9122222322POLYGON ((2.81250 0.00000, 2.81250 0.70311, 3....0.0
      
+
+
      
+
      
+
      
+
      
+
      We can also use any bingtile grid for any zoom level lower than the data’s zoom level
      
+
      
+
      bgtile_generator10 = gr.BingTileGridGenerator(AOI_ZOOM_LEVEL + 1)
+simple_aoi_bingtiles10 = bgtile_generator10.generate_grid(simple_aoi)
      
+
      
+
      
+
      bingtile_results10 = create_bingtile_zonal_stats(
+    simple_aoi_bingtiles10,
+    simple_data_quadkey,
+    aggregations=[dict(func="count", fillna=True)],
+)
      
+
      
+
      
+
      bingtile_results10[bingtile_results10.index_count > 0]
      
+
      
+
      
+
      
+
+
+
+
+
+
+
+
+
+
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+
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+
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+
+
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+
+
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+
+
+
+
+
+
+
+
+
      quadkeygeometryindex_count
      41222222221POLYGON ((0.35156 0.35156, 0.35156 0.70311, 0....3.0
      131222222320POLYGON ((1.40625 0.35156, 1.40625 0.70311, 1....3.0
      191222222330POLYGON ((2.10937 0.35156, 2.10937 0.70311, 2....2.0
      221222222331POLYGON ((2.46094 0.35156, 2.46094 0.70311, 2....1.0
      
+
+
      
+
      
+
      
+
      
+
      
+
      ax = results.plot(ax=plt.axes(), column="index_count", edgecolor="blue", alpha=0.2)
+ax = simple_data.plot(ax=ax, color="purple")
+ax = bingtile_results.plot(ax=ax, column="index_count", edgecolor="black", alpha=0.4)
+ax = bingtile_results10.plot(ax=ax, column="index_count", edgecolor="red", alpha=0.4)
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
+
+
      
+
+
       
+
+
       
+
+
+
+
+
\ No newline at end of file

      
+    
+  
+
      
+    
+  
+
      
+    
+  
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