calculate overlapping areas

I am following the example here and successfully created convex hulls. But I have a question on how to calculate the shared areas between each convex hull in the following figure:

Thanks!

Solution

Here's an example to get the intersection of Manhattan and the Bronx. You could use pd.concat() before .overlay() if you want to combine boroughs.

import geopandas as gpd
nybb_path = gpd.datasets.get_path('nybb')

boros = gpd.read_file(nybb_path)
boros.set_index('BoroCode', inplace=True)
boros.sort_index(inplace=True)

boros['geometry'] = boros['geometry'].convex_hull

print(boros)
    BoroName    Shape_Leng  Shape_Area  geometry
BoroCode                
1   Manhattan   359299.096471   6.364715e+08    POLYGON ((977855.445 188082.322, 971830.134 19...
2   Bronx   464392.991824   1.186925e+09    POLYGON ((1017949.978 225426.885, 1015563.562 ...
3   Brooklyn    741080.523166   1.937479e+09    POLYGON ((988872.821 146772.032, 983670.606 14...
4   Queens  896344.047763   3.045213e+09    POLYGON ((1000721.532 136681.776, 994611.996 2...
5   Staten Island   330470.010332   1.623820e+09    POLYGON ((915517.688 120121.881, 915467.035 12...

manhattan_gdf = boros.iloc[0:1, :]
bronx_gdf = boros.iloc[1:2, :]

manhattan_bronx_intersecetion_polygon = gpd.overlay(manhattan_gdf, bronx_gdf, 
how='intersection')

#SPCS83 New York Long Island zone (US Survey feet)
print(manhattan_bronx_intersecetion_polygon.geometry[0].area)
164559574.89341027

ax = manhattan_bronx_intersecetion_polygon.plot(figsize=(6,6))
boros.plot(ax=ax, facecolor='none', edgecolor='k');

Here is a loop solution like you asked for in your comment.

import geopandas as gpd
nybb_path = gpd.datasets.get_path('nybb')

boros = gpd.read_file(nybb_path)
boros.set_index('BoroCode', inplace=True)
boros.sort_index(inplace=True)

boros['geometry'] = boros['geometry'].convex_hull

intersection_polygons_list = []

for idx, row in boros.iterrows():

    main_boro_gdf = boros.iloc[idx-1:idx, :]

    print('\n' + 'main boro:', main_boro_gdf['BoroName'].values.tolist()[:])

    other_boro_list = boros.index.tolist()

    other_boro_list.remove(idx)

    other_boro_gdf = boros[boros.index.isin(other_boro_list)]

    print('other boros:',other_boro_gdf['BoroName'].values.tolist()[:])

    intersection_polygons = gpd.overlay(main_boro_gdf, other_boro_gdf, how='intersection')

    intersection_polygons['intersection_area'] = intersection_polygons.geometry.area

    print('intersecton area sum:', intersection_polygons['intersection_area'].sum())

    intersection_polygons_list.append(intersection_polygons)

output:

main boro: ['Manhattan']
other boros: ['Bronx', 'Brooklyn', 'Queens', 'Staten Island']
intersecton area sum: 279710750.6116526

main boro: ['Bronx']
other boros: ['Manhattan', 'Brooklyn', 'Queens', 'Staten Island']
intersecton area sum: 216638786.2669542

main boro: ['Brooklyn']
other boros: ['Manhattan', 'Bronx', 'Queens', 'Staten Island']
intersecton area sum: 1506573115.3550038

main boro: ['Queens']
other boros: ['Manhattan', 'Bronx', 'Brooklyn', 'Staten Island']
intersecton area sum: 1560297426.3563197

main boro: ['Staten Island']
other boros: ['Manhattan', 'Bronx', 'Brooklyn', 'Queens']
intersecton area sum: 0.0

You can plot using the intersection_polygons_list index values. For example here are the overlapping areas for the Bronx:

intersection_polygons_list[1].plot()