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total_address_etl.py
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total_address_etl.py
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from pathlib import Path
import datetime
import pandas as pd
import geopandas as gpd
import arcgis
import sqlalchemy
import numpy as np
today = datetime.datetime.now().strftime("%Y%m%d")
total_address_fldr = str(Path('../'))
gis = arcgis.gis.GIS(url='https://utah.maps.arcgis.com/')
def counties_etl():
county_df = pd.DataFrame.spatial.from_layer(arcgis.features.FeatureLayer.fromitem(gis.content.get('90431cac2f9f49f4bcf1505419583753')))
county_df = county_df.reindex(columns=['NAME', 'FIPS_STR', 'SHAPE'])
county_df = county_df.rename(columns={'NAME':'CountyName', 'FIPS_STR':'County'})
county_df.spatial.set_geometry("SHAPE")
county_df.spatial.project(4326)
county_df.spatial.sr = {"wkid": 4326}
out_json = Path(total_address_fldr, f'CNTY_49_{today}.json')
out_json.write_text(county_df.spatial.to_featureset().to_json, encoding='utf-8')
## Use if geojson output is preferred
# out_geojson = Path(total_address_fldr, f'CNTY_49_{today}.geojson')
# out_geojson.write_text(county_df.spatial.to_featureset().to_geojson, encoding='utf-8')
#counties_etl()
def municipalities_etl():
muni_df = pd.DataFrame.spatial.from_layer(arcgis.features.FeatureLayer.fromitem(gis.content.get('543fa1f073714198a3dbf8a292bdf30c')))
muni_df = muni_df.reindex(columns=['NAME', 'SHAPE'])
muni_df = muni_df.rename(columns={'NAME':'CityName'})
muni_df.spatial.set_geometry("SHAPE")
muni_df.spatial.project(4326)
muni_df.spatial.sr = {"wkid": 4326}
out_json = Path(total_address_fldr, f'CITY_49_{today}.json')
out_json.write_text(muni_df.spatial.to_featureset().to_json, encoding='utf-8')
## Use if geojson output is preferred
# out_geojson = Path(total_address_fldr, f'CITY_49_{today}.geojson')
# out_geojson.write_text(muni_df.spatial.to_featureset().to_geojson, encoding='utf-8')
#municipalities_etl()
def zips_etl():
zips_df = pd.DataFrame.spatial.from_layer(arcgis.features.FeatureLayer.fromitem(gis.content.get('4f9d1d1301864f8cabef08cbae7b2d3c')))
zips_df = zips_df.reindex(columns=['ZIP5', 'SHAPE'])
zips_df = zips_df.rename(columns={'ZIP5':'ZipCode'})
zips_df.spatial.set_geometry("SHAPE")
zips_df.spatial.project(4326)
zips_df.spatial.sr = {"wkid": 4326}
out_json = Path(total_address_fldr, f'ZIPC_49_{today}.json')
out_json.write_text(zips_df.spatial.to_featureset().to_json, encoding='utf-8')
## Use if geojson output is preferred
# out_geojson = Path(total_address_fldr, f'ZIPC_49_{today}.geojson')
# out_geojson.write_text(zips_df.spatial.to_featureset().to_geojson, encoding='utf-8')
#zips_etl()
def precinct_etl():
number_to_fips = {
'0':'49005',
'1':'49001',
'2':'49003',
'3':'49005',
'4':'49007',
'5':'49009',
'6':'49011',
'7':'49013',
'8':'49015',
'9':'49017',
'10':'49019',
'11':'49021',
'12':'49023',
'13':'49025',
'14':'49027',
'15':'49029',
'16':'49031',
'17':'49033',
'18':'49035',
'19':'49037',
'20':'49039',
'21':'49041',
'22':'49043',
'23':'49045',
'24':'49047',
'25':'49049',
'26':'49051',
'27':'49053',
'28':'49055',
'29':'49057'
}
precinct_df = pd.DataFrame.spatial.from_layer(arcgis.features.FeatureLayer.fromitem(gis.content.get('d33f596419d74948a45070275632b8e0')))
precinct_df = precinct_df.reindex(columns=['VistaID', 'CountyID', 'SHAPE'])
precinct_df['CountyID'] = precinct_df['CountyID'].astype(str)
precinct_df['CountyID'] = precinct_df['CountyID'].map(number_to_fips)
precinct_df = precinct_df.rename(columns={'VistaID':'PPartName', 'CountyID':'County'})
precinct_df.spatial.set_geometry("SHAPE")
precinct_df.spatial.project(4326)
precinct_df.spatial.sr = {"wkid": 4326}
out_json = Path(total_address_fldr, f'PRCT_49_{today}.json')
out_json.write_text(precinct_df.spatial.to_featureset().to_json, encoding='utf-8')
## Use if geojson output is preferred
# out_geojson = Path(total_address_fldr, f'PRCT_49_{today}.geojson')
# out_geojson.write_text(precinct_df.spatial.to_featureset().to_geojson, encoding='utf-8')
#precinct_etl()
def address_etl():
print('start address point etl')
engine = sqlalchemy.create_engine(
sqlalchemy.engine.url.URL.create(
drivername="postgresql",
username='agrc',
password='agrc',
database='opensgid',
host='opensgid.agrc.utah.gov',
port=5432,
)
)
addr_type_map = {
'BASE ADDRESS':'',
'Commercial':'C',
'Industrial':'C',
'Other':'',
'Residential':'',
'Unknown':'',
'':''
}
field_map = {
'fulladd':'Original Addr string',
'addnum':'Addr #',
'prefixdir':'Prefix',
'streetname':'Street Name',
'streettype':'Street type',
'suffixdir':'Suffix',
'unitid':'Multi-Unit',
'city':'City',
'zipcode':'Zip code',
'countyid':'County',
'pttype':'Addr Type'
}
add_pts_table = 'opensgid.location.address_points'
add_df = gpd.read_postgis(
f"select * from {add_pts_table}", engine, index_col='xid', crs=26912, geom_col='shape'
)
municipalities = 'opensgid.boundaries.municipal_boundaries'
muni_df = gpd.read_postgis(
f"select * from {municipalities}", engine, index_col='xid', crs=26912, geom_col='shape')
muni_name_dict = pd.Series(muni_df.name.values, index=muni_df.shortdesc).to_dict()
add_df.to_crs(crs=4326, epsg=None, inplace=True)
add_sdf = pd.DataFrame.spatial.from_geodataframe(add_df, column_name='shape')
add_sdf = add_sdf.reindex(columns=['fulladd', 'addnum', 'addnumsuff', 'prefixdir', 'streetname', 'streettype',
'suffixdir', 'unitid', 'city', 'zipcode', 'countyid', 'pttype', 'shape'])
add_sdf.spatial.set_geometry('shape')
add_sdf['city'] = add_sdf['city'].map(muni_name_dict).str.upper()
add_sdf['fulladd'] = add_sdf[['fulladd', 'city', 'zipcode']].fillna('').agg(' '.join, axis=1)
add_sdf['fulladd'] = add_sdf['fulladd'].str.replace(' ', ' ')
add_sdf['addnum'] = add_sdf['addnum'].astype(str) + add_sdf['addnumsuff'].fillna('')
add_sdf['Lat'] = add_sdf['shape'].apply(lambda shape: shape.y)
add_sdf['Lon'] = add_sdf['shape'].apply(lambda shape: shape.x)
add_sdf = add_sdf.drop('shape', axis=1)
add_sdf['unitid'] = add_sdf['unitid'].apply(lambda x: '1' if x not in ['', ' '] else '0')
add_sdf['pttype'] = add_sdf['pttype'].map(addr_type_map)
add_sdf = add_sdf.rename(columns=field_map)
add_sdf = add_sdf.reindex(columns=['Original Addr string', 'Addr #', 'Prefix', 'Street Name','Street type', 'Suffix', 'Multi-Unit',
'City', 'Zip code', 'plus 4', 'County', 'Addr Type', 'Precinct Part Name', 'Lat', 'Lon'])
address_csv = Path(total_address_fldr, f'ADDRE_49_{today}.csv')
add_sdf.to_csv(address_csv)
address_etl()