Population around points¶

The tutorial sums WorldPop onto H3 cells. This guide sums it inside a radius around each point, such as schools or clinics in a country. The result is a view: your points, with a population column.

from gigaspatial import PoiViewGenerator

points = [(-1.9441, 30.0619), (-1.9500, 30.1000)]
places = PoiViewGenerator(points)
view = places.map_wp_pop(country="RWA", map_radius_meters=1000)
print(view.head())

map_radius_meters is the buffer around each point in meters. The first run downloads the WorldPop raster for the country into the data store.

Estimate how many people live near each school in a country¶

Pass a GeoDataFrame of schools or clinics directly to PoiViewGenerator to estimate the population living within a catchment radius of each place.

With schools or clinics from OpenStreetMap (no API key required):

from gigaspatial import OSMLocationFetcher, PoiViewGenerator

schools = OSMLocationFetcher(country="RWA", location_types=["school"]).fetch_locations()
view = PoiViewGenerator(schools).map_wp_pop(country="RWA", map_radius_meters=1000)
print(view[["population"]].head())

With school locations from the Giga API (requires GIGA_SCHOOL_LOCATION_API_KEY; see Configuration):

from gigaspatial import GigaSchoolLocationFetcher, PoiViewGenerator

schools = GigaSchoolLocationFetcher(country="RWA").fetch_locations(process_geospatial=True)
view = PoiViewGenerator(schools, poi_id_column="giga_id_school").map_wp_pop(
    country="RWA",
    map_radius_meters=1000,
)
print(view[["giga_id_school", "population"]].head())

For school-age or under-18 population, pass WorldPop options such as school_age=True or under_18=True to map_wp_pop. See Points and nearest buildings to also attach the nearest building footprint to each school.