b001_11.py

b001_11.py

# -- St Luke office
# narrow dataset to the geometry
mask_st_luke_office = gdf_commuters_workplace.intersects(shape(polygon_st_luke_office))
df_commuters_st_luke_office = df_commuter[mask_st_luke_office]

# embed shape into a geopandas to visualise in kepler
gdf_st_luke_geometry = gpd.GeoDataFrame({'geometry':[shape(polygon_st_luke_office)], "display_name": ["St Luke's Close Office"]})

# -- Same for Albert Road office
mask_albert_road = gdf_commuters_workplace.intersects(shape(polygon_albert_road))
df_commuters_albert_road = df_commuter[mask_albert_road]
gdf_albert_road = gpd.GeoDataFrame({'geometry':[shape(polygon_albert_road)], "display_name": ["St Luke's Close Office"]})

#  --- Visualisation ---

try:
    from kepler_config import config_map_4
except ImportError:
    config_map_4 = config

map_4 = KeplerGl(
    data={
        "St Luke's Close Office": gdf_st_luke_geometry.copy(),  
        "commuters to St Luke": df_commuters_st_luke_office.copy(),
        "Albert Road Office": gdf_albert_road.copy(),
        "commuters to Albert": df_commuters_albert_road.copy(),
    }, 
    config=config_map_4, 
    height=800
)
display(map_4)

print(f"Commuters to St Luke office {len(df_commuters_st_luke_office)} ({100 * len(df_commuters_st_luke_office) / len(df_commuter)} %)" )
print(f"Commuters to Albert Road office {len(df_commuters_albert_road)} ({100 *  len(df_commuters_albert_road) / len(df_commuter)} %)")