Python Geospatial Analysis Essentials May 2026

conda install geopandas folium shapely matplotlib # or pip (may require system GDAL) pip install geopandas folium shapely matplotlib Let's load a natural Earth dataset (Geopandas can download sample data).

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. Python GeoSpatial Analysis Essentials

from shapely.geometry import Point, LineString, Polygon nyc = Point(-74.006, 40.7128) Create a line route = LineString([(-74.006, 40.7128), (-73.935, 40.7306)]) Create a polygon (bounding box around NYC) bbox = Polygon([(-74.05, 40.68), (-73.95, 40.68), (-73.95, 40.75), (-74.05, 40.75)]) Check if point is inside polygon print(bbox.contains(nyc)) # True Step 4: The Magic of Spatial Joins This is where Geopandas shines. Let's find all countries that contain a specific point. conda install geopandas folium shapely matplotlib # or

# 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()) What geospatial project are you working on

print(result['name']) # Should output "Brazil"