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import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
def process_buildings(input_gdf, sensitive_sites_gdf, default_building_height_m, multiplier_factor):
# List to store all intersected sensitive sites
intersected_sites = []
# List to store all buffers
buffers = []
intersection_desc = ""
# Iterate over each building in the input file
for idx, building in input_gdf.iterrows():
building_name = building.get('building_name', 'Unnamed building')
# If the 'building_height' field exists and its value is not null or zero for this building,
# use it as the building height. Otherwise, use the default building height provided by the user.
if 'building_height' in building and pd.notnull(building['building_height']) and building['building_height'] != 0:
building_height_m = building['building_height'] * 0.3048
else:
building_height_m = default_building_height_m
buffer_distance_m = building_height_m * multiplier_factor
# Convert building's geometry to EPSG:3857 for accurate meter-based distance measurement
building_geometry = gpd.GeoSeries([building['geometry']], crs="EPSG:4326")
building_geometry_m = building_geometry.to_crs("EPSG:3857")
# Create a buffer around the building and convert it to a GeoDataFrame
building_buffer = building_geometry_m.buffer(buffer_distance_m)
building_buffer_gdf = gpd.GeoDataFrame(geometry=building_buffer, crs="EPSG:3857")
building_buffer_gdf = building_buffer_gdf.to_crs("EPSG:4326")
# Convert back to feet for storing and printing, rounding to the nearest foot
building_height_ft = round(building_height_m / 0.3048)
buffer_distance_ft = round(buffer_distance_m / 0.3048)
# Assign additional attributes
building_buffer_gdf['building_name'] = building_name
building_buffer_gdf['building_height'] = building_height_ft
building_buffer_gdf['buffer_distance'] = buffer_distance_ft
buffers.append(building_buffer_gdf)
# Check if the buffer intersects with any sensitive sites
intersects = gpd.overlay(building_buffer_gdf, sensitive_sites_gdf, how='intersection')
if not intersects.empty:
building_intersect_desc = f"Building {idx} ({building_name}), height: {building_height_ft}, buffer distance: {buffer_distance_ft} is in the vicinity of a sensitive site."
intersected_sites.append(intersects)
else:
building_intersect_desc = f"Building {idx} ({building_name}), height: {building_height_ft}, buffer distance: {buffer_distance_ft} is not in the vicinity of any sensitive sites."
if intersection_desc == "":
intersection_desc = building_intersect_desc
else:
intersection_desc += "\n" + building_intersect_desc
return buffers, intersected_sites, intersection_desc
def get_max_extent(*gdfs): # takes in unlimited number of gdfs and calculates max/min xy extents
minx = min(gdf.total_bounds[0] for gdf in gdfs)
miny = min(gdf.total_bounds[1] for gdf in gdfs)
maxx = max(gdf.total_bounds[2] for gdf in gdfs)
maxy = max(gdf.total_bounds[3] for gdf in gdfs)
return minx, miny, maxx, maxy
def create_plot(filename, extent, *gdfs): # takes in unlimited number of gdfs
fig, ax = plt.subplots(figsize=(10, 8)) #Sets image size by width & height (in inches)
colors = ['tan', 'mediumseagreen', 'thistle', 'lightcoral', 'sienna', 'yellow'] # Extend/improve this list as needed
for idx, gdf in enumerate(gdfs):
gdf.plot(ax=ax, color=colors[idx % len(colors)]) # Cycle through colors
ax.set_xlim(extent[0], extent[2])
ax.set_ylim(extent[1], extent[3])
# Hide axes
ax.axis('off')
plt.savefig(filename, bbox_inches='tight', pad_inches=0) # remove padding |