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streetscape.py
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streetscape.py
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import math
import warnings
import geopandas as gpd
import numpy as np
import pandas as pd
import momepy
import shapely
import xvec # noqa: F401
from shapely import Point, MultiPoint, LineString, MultiLineString
class Streetscape:
def __init__(
self,
streets: gpd.GeoDataFrame,
buildings: gpd.GeoDataFrame,
sightline_length: float = 50,
tangent_length: float = 300,
sightline_spacing: float = 3,
intersection_offset: float = 0.5,
angle_tolerance: float = 5,
height_col: str | None = None,
category_col: str | None = None,
) -> None:
"""Streetscape analysis based on sightlines
This is a direct implementation of the algorithm proposed in Araldi and Fusco
2024.
Parameters
----------
streets : gpd.GeoDataFrame
GeoDataFrame containing LineString geometry representing streets
buildings : gpd.GeoDataFrame
GeoDataFrame containing Polygon geometry representing buildings
sightline_length : float, optional
length of the sightline generated at each sightline point perpendiculary to
the street geometry, by default 50
tangent_length : float, optional
length of the sightline generated at each sightline point tangentially to
the street geometry, by default 300
sightline_spacing : float, optional
approximate distance between sightline points generated along streets,
by default 3
intersection_offset : float, optional
Offset to use at the beginning and the end of each LineString. The first
sightline point is generated at this distance from the start and the last
one is generated at this distance from the end of each geometry,
by default 0.5
angle_tolerance : float, optional
Maximum angle between sightlines that does not require infill lines to be
generated, by default 5
height_col : str, optional
name of a column of the buildings DataFrame containing the information
about the building height in meters.
category_col : str, optional
name of a column of the buildings DataFrame containing the information
about the building category encoded as integer labels.
"""
self.sightline_length = sightline_length
self.tangent_length = tangent_length
self.sightline_spacing = sightline_spacing
self.intersection_offset = intersection_offset
self.angle_tolerance = angle_tolerance
self.height_col = height_col
self.category_col = category_col
self.building_categories_count = (
buildings[category_col].nunique() if category_col else 0
)
self.SIGHTLINE_LEFT = 0
self.SIGHTLINE_RIGHT = 1
self.SIGHTLINE_FRONT = 2
self.SIGHTLINE_BACK = 3
self.sightline_length_PER_SIGHT_TYPE = [
sightline_length,
sightline_length,
tangent_length,
tangent_length,
]
streets = streets.copy()
streets.geometry = streets.force_2d()
nodes, edges = momepy.nx_to_gdf(
momepy.node_degree(momepy.gdf_to_nx(streets, preserve_index=True))
)
edges["n1_degree"] = nodes.degree.loc[edges.node_start].values
edges["n2_degree"] = nodes.degree.loc[edges.node_end].values
edges["dead_end_left"] = edges["n1_degree"] == 1
edges["dead_end_right"] = edges["n2_degree"] == 1
edges["street_index"] = edges.index
self.streets = edges
buildings = buildings.copy()
buildings["street_index"] = np.arange(len(buildings))
self.buildings = buildings
self.rtree_buildings = self.buildings.sindex
self._compute_sightline_indicators_full()
# return empty list if no sight line could be build du to total road length
def _compute_sightlines(
self,
line: LineString,
dead_end_start,
dead_end_end,
):
################### FIRTS PART : PERPENDICULAR SIGHTLINES #################################
# Calculate the number of profiles to generate
line_length = line.length
remaining_length = line_length - 2 * self.intersection_offset
if remaining_length < self.sightline_spacing:
# no sight line
return (
gpd.GeoDataFrame(columns=["geometry", "point_id", "sight_type"]),
[],
[],
)
distances = [self.intersection_offset]
nb_inter_nodes = int(math.floor(remaining_length / self.sightline_spacing))
offset = remaining_length / nb_inter_nodes
distance = self.intersection_offset
for i in range(0, nb_inter_nodes):
distance = distance + offset
distances.append(distance)
results_sight_points = []
results_sight_points_distances = []
results_sightlines = []
previous_sigh_line_left = None
previous_sigh_line_right = None
# semi_ortho_segment_size = self.sightline_spacing/2
semi_ortho_segment_size = self.intersection_offset / 2
sightline_index = 0
last_pure_sightline_left_position_in_array = -1
FIELD_geometry = 0
FIELD_uid = 1
################### SECOND PART : TANGENT SIGHTLINES #################################
prev_distance = 0
# Start iterating along the line
for distance in distances:
# Get the start, mid and end points for this segment
seg_st = line.interpolate((distance - semi_ortho_segment_size))
seg_mid = line.interpolate(distance)
seg_end = line.interpolate(distance + semi_ortho_segment_size)
# Get a displacement vector for this segment
vec = np.array(
[
[
seg_end.x - seg_st.x,
],
[
seg_end.y - seg_st.y,
],
]
)
# Rotate the vector 90 deg clockwise and 90 deg counter clockwise
rot_anti = np.array([[0, -1], [1, 0]])
rot_clock = np.array([[0, 1], [-1, 0]])
vec_anti = np.dot(rot_anti, vec)
vec_clock = np.dot(rot_clock, vec)
# Normalise the perpendicular vectors
len_anti = ((vec_anti**2).sum()) ** 0.5
vec_anti = vec_anti / len_anti
len_clock = ((vec_clock**2).sum()) ** 0.5
vec_clock = vec_clock / len_clock
# Scale them up to the profile length
vec_anti = vec_anti * self.sightline_length
vec_clock = vec_clock * self.sightline_length
# Calculate displacements from midpoint
prof_st = (seg_mid.x + float(vec_anti[0]), seg_mid.y + float(vec_anti[1]))
prof_end = (
seg_mid.x + float(vec_clock[0]),
seg_mid.y + float(vec_clock[1]),
)
results_sight_points.append(seg_mid)
results_sight_points_distances.append(distance)
sightline_left = LineString([seg_mid, prof_st])
sightline_right = LineString([seg_mid, prof_end])
# append LEFT sight line
rec = [
sightline_left, # FIELD_geometry
sightline_index, # FIELD_uid
self.SIGHTLINE_LEFT, # FIELD_type
]
results_sightlines.append(rec)
# back up for dead end population
last_pure_sightline_left_position_in_array = len(results_sightlines) - 1
# append RIGHT sight line
rec = [
sightline_right, # FIELD_geometry
sightline_index, # FIELD_uid
self.SIGHTLINE_RIGHT, # FIELD_type
]
results_sightlines.append(rec)
line_tan_back = LineString(
[
seg_mid,
rotate(prof_end[0], prof_end[1], seg_mid.x, seg_mid.y, rad_90),
]
)
line_tan_front = LineString(
[seg_mid, rotate(prof_st[0], prof_st[1], seg_mid.x, seg_mid.y, rad_90)]
)
# extends tanline to reach parametrized width
line_tan_back = extend_line_end(line_tan_back, self.tangent_length)
line_tan_front = extend_line_end(line_tan_front, self.tangent_length)
# append tangent sigline front view
rec = [
line_tan_back, # FIELD_geometry
sightline_index, # FIELD_type
self.SIGHTLINE_BACK,
]
results_sightlines.append(rec)
# append tangent sigline front view
rec = [
line_tan_front, # FIELD_geometry
sightline_index, # FIELD_uid
self.SIGHTLINE_FRONT,
]
results_sightlines.append(rec)
################### THIRD PART: SIGHTLINE ENRICHMENT #################################
# Populate lost space between consecutive sight lines with high deviation (>angle_tolerance)
if previous_sigh_line_left is not None:
for this_line, prev_line, side in [
(sightline_left, previous_sigh_line_left, self.SIGHTLINE_LEFT),
(sightline_right, previous_sigh_line_right, self.SIGHTLINE_RIGHT),
]:
# angle between consecutive sight line
deviation = round(lines_angle(prev_line, this_line), 1)
# DEBUG_VALUES.append([this_line.coords[1],deviation])
# condition 1: large deviation
if abs(deviation) <= self.angle_tolerance:
continue
# condition 1: consecutive sight lines do not intersect
if this_line.intersects(prev_line):
continue
nb_new_sightlines = int(
math.floor(abs(deviation) / self.angle_tolerance)
)
nb_new_sightlines_this = nb_new_sightlines // 2
nb_new_sightlines_prev = nb_new_sightlines - nb_new_sightlines_this
delta_angle = deviation / (nb_new_sightlines)
theta_rad = np.deg2rad(delta_angle)
# add S2 new sight line on previous one
angle = 0
for i in range(0, nb_new_sightlines_this):
angle -= theta_rad
x0 = this_line.coords[0][0]
y0 = this_line.coords[0][1]
x = this_line.coords[1][0]
y = this_line.coords[1][1]
new_line = LineString(
[this_line.coords[0], rotate(x, y, x0, y0, angle)]
)
rec = [
new_line, # FIELD_geometry
sightline_index, # FIELD_uid
side, # FIELD_type
]
results_sightlines.append(rec)
# add S2 new sight line on this current sight line
angle = 0
for i in range(0, nb_new_sightlines_prev):
angle += theta_rad
x0 = prev_line.coords[0][0]
y0 = prev_line.coords[0][1]
x = prev_line.coords[1][0]
y = prev_line.coords[1][1]
new_line = LineString(
[prev_line.coords[0], rotate(x, y, x0, y0, angle)]
)
rec = [
new_line, # FIELD_geometry
sightline_index - 1, # FIELD_uid
side, # FIELD_type
]
results_sightlines.append(rec)
# =========================================
# iterate
previous_sigh_line_left = sightline_left
previous_sigh_line_right = sightline_right
sightline_index += 1
# ======================================================================================
# SPECIFIC ENRICHMENT FOR SIGHTPOINTS corresponding to DEAD ENDs
# ======================================================================================
if dead_end_start or dead_end_end:
for prev_sg, this_sg, dead_end in [
(
results_sightlines[0],
results_sightlines[1],
dead_end_start,
),
(
results_sightlines[last_pure_sightline_left_position_in_array + 1],
results_sightlines[last_pure_sightline_left_position_in_array],
dead_end_end,
),
]:
if not dead_end:
continue
# angle between consecutive dead end sight line LEFT and RIGHT (~180)
prev_line = prev_sg[FIELD_geometry] # FIRST sight line LEFT side
this_line = this_sg[FIELD_geometry] # FIRST sight line LEFT side
# special case --> dead end .. so 180 °
deviation = 180
nb_new_sightlines = int(
math.floor(abs(deviation) / self.angle_tolerance)
)
nb_new_sightlines_this = nb_new_sightlines // 2
nb_new_sightlines_prev = nb_new_sightlines - nb_new_sightlines_this
delta_angle = deviation / (nb_new_sightlines)
theta_rad = np.deg2rad(delta_angle)
# add S2 new sight line on previous one
angle = 0
for i in range(0, nb_new_sightlines_this):
angle -= theta_rad
x0 = this_line.coords[0][0]
y0 = this_line.coords[0][1]
x = this_line.coords[1][0]
y = this_line.coords[1][1]
new_line = LineString(
[this_line.coords[0], rotate(x, y, x0, y0, angle)]
)
rec = [
new_line, # FIELD_geometry
this_sg[FIELD_uid], # FIELD_uid
self.SIGHTLINE_LEFT,
]
results_sightlines.append(rec)
# add S2 new sight line on this current sight line
angle = 0
for i in range(0, nb_new_sightlines_prev):
angle += theta_rad
x0 = prev_line.coords[0][0]
y0 = prev_line.coords[0][1]
x = prev_line.coords[1][0]
y = prev_line.coords[1][1]
new_line = LineString(
[prev_line.coords[0], rotate(x, y, x0, y0, angle)]
)
rec = [
new_line, # FIELD_geometry
prev_sg[FIELD_uid], # FIELD_uid
self.SIGHTLINE_RIGHT,
]
results_sightlines.append(rec)
# ======================================================================================
return (
gpd.GeoDataFrame(
results_sightlines, columns=["geometry", "point_id", "sight_type"]
),
results_sight_points,
results_sight_points_distances,
)
def _compute_sigthlines_indicators(self, street_row, optimize_on=True):
street_uid = street_row.street_index
street_geom = street_row.geometry
gdf_sightlines, sightlines_points, results_sight_points_distances = (
self._compute_sightlines(
street_geom, street_row.dead_end_left, street_row.dead_end_right
)
)
# per street sightpoints indicators
current_street_uid = street_uid
current_street_sightlines_points = sightlines_points
current_street_left_OS_count = []
current_street_left_OS = []
current_street_left_SB_count = []
current_street_left_SB = []
current_street_left_H = []
current_street_left_HW = []
current_street_right_OS_count = []
current_street_right_OS = []
current_street_right_SB_count = []
current_street_right_SB = []
current_street_right_H = []
current_street_right_HW = []
current_street_left_BUILT_COVERAGE = []
current_street_right_BUILT_COVERAGE = []
# SPARSE STORAGE (one value if set back is OK ever in intersightline)
current_street_left_SEQ_SB_ids = []
current_street_left_SEQ_SB_categories = []
current_street_right_SEQ_SB_ids = []
current_street_right_SEQ_SB_categories = []
current_street_front_sb = []
current_street_back_sb = []
# [Expanded] each time a sight line or intersight line occured
left_SEQ_sightlines_end_points = []
right_SEQ_sightlines_end_points = []
if sightlines_points is None:
current_street_sightlines_points = []
return [
current_street_uid,
current_street_sightlines_points,
current_street_left_OS_count,
current_street_left_OS,
current_street_left_SB_count,
current_street_left_SB,
current_street_left_H,
current_street_left_HW,
current_street_left_BUILT_COVERAGE,
current_street_left_SEQ_SB_ids,
current_street_left_SEQ_SB_categories,
current_street_right_OS_count,
current_street_right_OS,
current_street_right_SB_count,
current_street_right_SB,
current_street_right_H,
current_street_right_HW,
current_street_right_BUILT_COVERAGE,
current_street_right_SEQ_SB_ids,
current_street_right_SEQ_SB_categories,
current_street_front_sb,
current_street_back_sb,
left_SEQ_sightlines_end_points,
right_SEQ_sightlines_end_points,
], None
# ------- SIGHT LINES
# Extract building in SIGHTLINES buffer (e.g: 50m)
# iterate throught sightlines groups.
# Eeach sigh points could have many sub sighpoint in case of snail effect)
for point_id, group in gdf_sightlines.groupby("point_id"):
front_sl_tan_sb = self.tangent_length
back_sl_tan_sb = self.tangent_length
left_sl_count = 0
left_sl_distance_total = 0
left_sl_building_count = 0
left_sl_building_sb_total = 0
left_sl_building_sb_height_total = 0
right_sl_count = 0
right_sl_distance_total = 0
right_sl_building_count = 0
right_sl_building_sb_total = 0
right_sl_building_sb_height_total = 0
left_sl_coverage_ratio_total = 0
right_sl_coverage_ratio_total = 0
# iterate throught each sightline links to the sigh point: LEFT(1-*),RIGHT(1-*),FRONT(1), BACK(1)
for row_s in group.itertuples(index=False):
sightline_geom = row_s.geometry
sightline_side = row_s.sight_type
sightline_length = self.sightline_length_PER_SIGHT_TYPE[sightline_side]
# extract possible candidates
if optimize_on and sightline_side >= self.SIGHTLINE_FRONT:
# ========== OPTIM TEST
# cut tan line in 3 block (~100m)
length_3 = sightline_geom.length / 3.0
A = sightline_geom.coords[0]
B = sightline_geom.coords[-1]
end_points = [
sightline_geom.interpolate(length_3),
sightline_geom.interpolate(length_3 * 2),
B,
]
gdf_sightline_buildings = None
start_point = A
for end_point in end_points:
sub_line = LineString([start_point, end_point])
gdf_sightline_buildings = self.buildings.iloc[
self.rtree_buildings.query(sub_line, predicate="intersects")
]
if len(gdf_sightline_buildings) > 0:
break
start_point = end_point
else:
gdf_sightline_buildings = self.buildings.iloc[
self.rtree_buildings.query(
sightline_geom, predicate="intersects"
)
]
s_pt1 = shapely.get_point(sightline_geom, 0)
endpoint = shapely.get_point(sightline_geom, -1)
# agregate
match_sl_distance = (
sightline_length # set max distance if no polygon intersect
)
match_sl_building_id = None
match_sl_building_category = None
match_sl_building_height = 0
sl_coverage_ratio_total = 0
for _, res in gdf_sightline_buildings.iterrows():
# building geom
geom = res.geometry
isect = sightline_geom.intersection(geom.exterior)
if not isect.is_empty:
dist = s_pt1.distance(isect)
if dist < match_sl_distance:
match_sl_distance = dist
match_sl_building_id = res.street_index
match_sl_building_height = (
res[self.height_col] if self.height_col else np.nan
)
match_sl_building_category = (
res[self.category_col] if self.category_col else None
)
# coverage ratio between sight line and candidate building (geom: building geom)
_coverage_isec = sightline_geom.intersection(geom)
# display(type(coverage_isec))
sl_coverage_ratio_total += _coverage_isec.length
if sightline_side == self.SIGHTLINE_LEFT:
left_sl_count += 1
left_SEQ_sightlines_end_points.append(endpoint)
left_sl_distance_total += match_sl_distance
left_sl_coverage_ratio_total += sl_coverage_ratio_total
if match_sl_building_id:
left_sl_building_count += 1
left_sl_building_sb_total += match_sl_distance
left_sl_building_sb_height_total += match_sl_building_height
# PREVALENCE: Emit each time a new setback or INTER-setback is found (campact storage structure)
current_street_left_SEQ_SB_ids.append(match_sl_building_id)
current_street_left_SEQ_SB_categories.append(
match_sl_building_category
)
elif sightline_side == self.SIGHTLINE_RIGHT:
right_sl_count += 1
right_SEQ_sightlines_end_points.append(endpoint)
right_sl_distance_total += match_sl_distance
right_sl_coverage_ratio_total += sl_coverage_ratio_total
if match_sl_building_id:
right_sl_building_count += 1
right_sl_building_sb_total += match_sl_distance
right_sl_building_sb_height_total += match_sl_building_height
# PREVALENCE: Emit each time a new setback or INTER-setback is found (campact storage structure)
current_street_right_SEQ_SB_ids.append(match_sl_building_id)
current_street_right_SEQ_SB_categories.append(
match_sl_building_category
)
elif sightline_side == self.SIGHTLINE_BACK:
back_sl_tan_sb = match_sl_distance
elif sightline_side == self.SIGHTLINE_FRONT:
front_sl_tan_sb = match_sl_distance
# LEFT
left_OS_count = left_sl_count
left_OS = left_sl_distance_total / left_OS_count
left_SB_count = left_sl_building_count
left_SB = np.nan
left_H = np.nan
left_HW = np.nan
if left_SB_count != 0:
left_SB = left_sl_building_sb_total / left_SB_count
left_H = left_sl_building_sb_height_total / left_SB_count
# HACk if SB = 0 --> 10cm
left_HW = left_H / max(left_SB, 0.1)
left_COVERAGE_RATIO = left_sl_coverage_ratio_total / left_OS_count
# RIGHT
right_OS_count = right_sl_count
right_OS = right_sl_distance_total / right_OS_count
right_SB_count = right_sl_building_count
right_SB = np.nan
right_H = np.nan
right_HW = np.nan
if right_SB_count != 0:
right_SB = right_sl_building_sb_total / right_SB_count
right_H = right_sl_building_sb_height_total / right_SB_count
# HACk if SB = 0 --> 10cm
right_HW = right_H / max(right_SB, 0.1)
right_COVERAGE_RATIO = right_sl_coverage_ratio_total / right_OS_count
current_street_left_OS_count.append(left_OS_count)
current_street_left_OS.append(left_OS)
current_street_left_SB_count.append(left_SB_count)
current_street_left_SB.append(left_SB)
current_street_left_H.append(left_H)
current_street_left_HW.append(left_HW)
current_street_right_OS_count.append(right_OS_count)
current_street_right_OS.append(right_OS)
current_street_right_SB_count.append(right_SB_count)
current_street_right_SB.append(right_SB)
current_street_right_H.append(right_H)
current_street_right_HW.append(right_HW)
# FRONT / BACK
current_street_front_sb.append(front_sl_tan_sb)
current_street_back_sb.append(back_sl_tan_sb)
# COverage ratio Built up
current_street_left_BUILT_COVERAGE.append(left_COVERAGE_RATIO)
current_street_right_BUILT_COVERAGE.append(right_COVERAGE_RATIO)
# ------- TAN LINES
# Extract building in TANLINES buffer (e.g: 300m)
# gdf_street_buildings = gdf_buildings.iloc[rtree_buildings.extract_ids(street_geom.buffer(PARAM_tangent_length))]
# building_count = len(gdf_street_buildings)
return [
current_street_uid,
current_street_sightlines_points,
current_street_left_OS_count,
current_street_left_OS,
current_street_left_SB_count,
current_street_left_SB,
current_street_left_H,
current_street_left_HW,
current_street_left_BUILT_COVERAGE,
current_street_left_SEQ_SB_ids,
current_street_left_SEQ_SB_categories,
current_street_right_OS_count,
current_street_right_OS,
current_street_right_SB_count,
current_street_right_SB,
current_street_right_H,
current_street_right_HW,
current_street_right_BUILT_COVERAGE,
current_street_right_SEQ_SB_ids,
current_street_right_SEQ_SB_categories,
current_street_front_sb,
current_street_back_sb,
left_SEQ_sightlines_end_points,
right_SEQ_sightlines_end_points,
], gdf_sightlines
def _compute_sightline_indicators_full(self):
values = []
for street_row in self.streets[
["street_index", "geometry", "dead_end_left", "dead_end_right"]
].itertuples(index=False):
indicators, _ = self._compute_sigthlines_indicators(street_row)
values.append(indicators)
df = pd.DataFrame(
values,
columns=[
"street_index",
"sightline_points",
"left_OS_count",
"left_OS",
"left_SB_count",
"left_SB",
"left_H",
"left_HW",
"left_BUILT_COVERAGE",
"left_SEQ_SB_ids",
"left_SEQ_SB_categories",
"right_OS_count",
"right_OS",
"right_SB_count",
"right_SB",
"right_H",
"right_HW",
"right_BUILT_COVERAGE",
"right_SEQ_SB_ids",
"right_SEQ_SB_categories",
"front_SB",
"back_SB",
"left_SEQ_OS_endpoints",
"right_SEQ_OS_endpoints",
],
)
df = df.set_index("street_index")
df["nodes_degree_1"] = self.streets.apply(
lambda row: (
(1 if row.n1_degree == 1 else 0) + (1 if row.n2_degree == 1 else 0)
)
/ 2,
axis=1,
)
df["nodes_degree_4"] = self.streets.apply(
lambda row: (
(1 if row.n1_degree == 4 else 0) + (1 if row.n2_degree == 4 else 0)
)
/ 2,
axis=1,
)
df["nodes_degree_3_5_plus"] = self.streets.apply(
lambda row: (
(1 if row.n1_degree == 3 or row.n1_degree >= 5 else 0)
+ (1 if row.n2_degree == 3 or row.n2_degree >= 5 else 0)
)
/ 2,
axis=1,
)
df["street_length"] = self.streets.length
df["windingness"] = 1 - momepy.linearity(self.streets)
self._sightline_indicators = df
def _compute_sigthlines_plot_indicators_one_side(
self, sightline_points, OS_count, SEQ_OS_endpoint
):
parcel_SB_count = []
parcel_SEQ_SB_ids = []
parcel_SEQ_SB = []
parcel_SEQ_SB_depth = []
N = len(sightline_points)
if N == 0:
parcel_SB_count = [0] * N
return [
parcel_SB_count,
parcel_SEQ_SB_ids,
parcel_SEQ_SB,
parcel_SEQ_SB_depth,
]
idx_end_point = 0
for sight_point, os_count in zip(sightline_points, OS_count):
n_sightlines_touching = 0
for i in range(os_count):
sightline_geom = LineString(
[sight_point, SEQ_OS_endpoint[idx_end_point]]
)
s_pt1 = Point(sightline_geom.coords[0])
gdf_items = self.plots.iloc[
self.rtree_parcels.query(sightline_geom, predicate="intersects")
]
match_distance = (
self.sightline_length # set max distance if no polygon intersect
)
match_id = None
match_geom = None
if not gdf_items.empty:
_distances = gdf_items.exterior.intersection(
sightline_geom
).distance(s_pt1)
match_id = _distances.idxmin()
match_distance = _distances.min()
match_geom = gdf_items.geometry[match_id]
# ---------------
# result in intersightline
if match_id is not None:
n_sightlines_touching += 1
parcel_SEQ_SB_ids.append(match_id)
parcel_SEQ_SB.append(match_distance)
# compute depth of plot intersect sighline etendue
if not match_geom.is_valid:
match_geom = match_geom.buffer(0)
isec = match_geom.intersection(
extend_line_end(
sightline_geom, self.sightline_plot_depth_extension
)
)
if (not isinstance(isec, LineString)) and (
not isinstance(isec, MultiLineString)
):
raise Exception("Not allowed: intersection is not of type Line")
parcel_SEQ_SB_depth.append(isec.length)
# ------- iterate
idx_end_point += 1
parcel_SB_count.append(n_sightlines_touching)
return [parcel_SB_count, parcel_SEQ_SB_ids, parcel_SEQ_SB, parcel_SEQ_SB_depth]
def compute_plots(
self, plots: gpd.GeoDataFrame, sightline_plot_depth_extension: float = 300
):
self.sightline_plot_depth_extension = sightline_plot_depth_extension
self.rtree_parcels = plots.sindex
plots = plots.copy()
plots["parcel_id"] = np.arange(len(plots))
self.plots = plots
self.plots["perimeter"] = self.plots.length
values = []
for uid, row in self._sightline_indicators.iterrows():
sightline_values = [uid]
side_values = self._compute_sigthlines_plot_indicators_one_side(
row.sightline_points, row.left_OS_count, row.left_SEQ_OS_endpoints
)
sightline_values += side_values
side_values = self._compute_sigthlines_plot_indicators_one_side(
row.sightline_points, row.right_OS_count, row.right_SEQ_OS_endpoints
)
sightline_values += side_values
values.append(sightline_values)
df = pd.DataFrame(
values,
columns=[
"street_index",
"left_parcel_SB_count",
"left_parcel_SEQ_SB_ids",
"left_parcel_SEQ_SB",
"left_parcel_SEQ_SB_depth",
"right_parcel_SB_count",
"right_parcel_SEQ_SB_ids",
"right_parcel_SEQ_SB",
"right_parcel_SEQ_SB_depth",
],
)
df = df.set_index("street_index").join(self._sightline_indicators.street_length)
self._plot_indicators = df
def _aggregate_plots(self):
values = []
for street_uid, row in self._plot_indicators.iterrows():
left_parcel_SB_count = row.left_parcel_SB_count
left_parcel_SEQ_SB_ids = row.left_parcel_SEQ_SB_ids
left_parcel_SEQ_SB = row.left_parcel_SEQ_SB
left_parcel_SEQ_SB_depth = row.left_parcel_SEQ_SB_depth
right_parcel_SB_count = row.right_parcel_SB_count
right_parcel_SEQ_SB_ids = row.right_parcel_SEQ_SB_ids
right_parcel_SEQ_SB = row.right_parcel_SEQ_SB
right_parcel_SEQ_SB_depth = row.right_parcel_SEQ_SB_depth
street_length = row.street_length
N = len(left_parcel_SB_count)
if N == 0:
values.append(
[
street_uid,
0,
0, # np_l, np_r
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
]
)
continue
left_parcel_SEQ_SB_depth = [
d if d >= 1 else 1 for d in left_parcel_SEQ_SB_depth
]
right_parcel_SEQ_SB_depth = [
d if d >= 1 else 1 for d in right_parcel_SEQ_SB_depth
]
left_unique_ids = set(left_parcel_SEQ_SB_ids)
right_unique_ids = set(right_parcel_SEQ_SB_ids)
all_unique_ids = left_unique_ids.union(right_unique_ids)
left_parcel_freq = len(left_unique_ids) / street_length
right_parcel_freq = len(right_unique_ids) / street_length
parcel_freq = len(all_unique_ids) / street_length
# compute sightline weights
left_sight_weight = []
# iterate all sight point
for sb_count in left_parcel_SB_count:
if sb_count != 0:
w = 1.0 / sb_count
for i in range(sb_count):
left_sight_weight.append(w)
right_sight_weight = []
# iterate all sight point
for sb_count in right_parcel_SB_count:
if sb_count != 0:
w = 1.0 / sb_count
for i in range(sb_count):
right_sight_weight.append(w)
# build depth dataframe with interzsighline weight
df_depth = [
[parcel_id, w, sb, depth, self.SIGHTLINE_LEFT]
for parcel_id, w, sb, depth in zip(
left_parcel_SEQ_SB_ids,
left_sight_weight,
left_parcel_SEQ_SB,
left_parcel_SEQ_SB_depth,
)
]
df_depth += [
[parcel_id, w, sb, depth, self.SIGHTLINE_RIGHT]
for parcel_id, w, sb, depth in zip(
right_parcel_SEQ_SB_ids,
right_sight_weight,
right_parcel_SEQ_SB,
right_parcel_SEQ_SB_depth,
)
]
df_depth = pd.DataFrame(
df_depth, columns=["parcel_id", "w", "sb", "depth", "side"]
).set_index("parcel_id")
df_depth["w_sb"] = df_depth.w * df_depth.sb
df_depth["w_depth"] = df_depth.w * df_depth.depth
df_depth_left = df_depth[df_depth.side == self.SIGHTLINE_LEFT]
df_depth_right = df_depth[df_depth.side == self.SIGHTLINE_RIGHT]
np_l = int(df_depth_left.w.sum())
np_r = int(df_depth_right.w.sum())
np_lr = np_l + np_r
left_parcel_SB = (
df_depth_left.w_sb.sum() / np_l if np_l > 0 else self.sightline_length
)
right_parcel_SB = (
df_depth_right.w_sb.sum() / np_r if np_r > 0 else self.sightline_length
)
parcel_SB = (
df_depth.w_sb.sum() / np_lr if np_lr > 0 else self.sightline_length
)
left_parcel_depth = df_depth_left.w_depth.sum() / np_l if np_l > 0 else 0
right_parcel_depth = df_depth_right.w_depth.sum() / np_r if np_r > 0 else 0
parcel_depth = df_depth.w_depth.sum() / np_lr if np_lr > 0 else 0
WD_ratio_list = []
WP_ratio_list = []
# TODO: this thing is pretty terrible and needs to be completely redone
# It is a massive bottleneck
for df in [df_depth, df_depth_left, df_depth_right]:
if len(df) == 0:
WD_ratio_list.append(0)
WP_ratio_list.append(0)
continue
df = (
df[["w", "w_depth"]]
.groupby(level=0)
.aggregate(
nb=pd.NamedAgg(column="w", aggfunc=len),
w_sum=pd.NamedAgg(column="w", aggfunc="sum"),