print(result['name']) # Should output "Brazil"
Geospatial data is everywhere. From tracking delivery trucks to analyzing climate change, location is the secret ingredient that makes data science actionable.
But if you open a raw shapefile or a GeoJSON file for the first time, you’ll quickly realize:
import geopandas as gpd world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) What is this? print(type(world)) # <class 'geopandas.geodataframe.GeoDataFrame'> print(world.head()) print(world.geometry.name) # 'geometry'
A GeoDataFrame is just a Pandas DataFrame with a special column (usually geometry ) that stores shapely objects. You rarely create geometries by hand, but you must understand them.
Next week, I'll cover spatial autocorrelation (aka: "Is that cluster real or random?"). Until then, map something interesting. What geospatial project are you working on? Let me know in the comments below.
# Check CRS print(world.crs) # EPSG:4326 (Lat/Lon) world_meters = world.to_crs('EPSG:3857') # Web Mercator Or better for area: world.to_crs('EPSG:3395') Calculate area in square kilometers world['area_km2'] = world_meters.geometry.area / 10**6 print(world[['name', 'area_km2']].head())
Pro tip: Never calculate distance or area using lat/lon (EPSG:4326). Always project to a local or equal-area CRS first. Static maps are fine. Interactive maps impress stakeholders.