Python — Geospatial Analysis Essentials

But if you open a raw shapefile or a GeoJSON file for the first time, you’ll quickly realize:

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

print(result['name']) # Should output "Brazil" But if you open a raw shapefile or

# Our point of interest (somewhere in Brazil) point_of_interest = Point(-55.0, -10.0) We'll put the point into a tiny GeoDataFrame point_gdf = gpd.GeoDataFrame(geometry=[point_of_interest], crs=world.crs) "within" joins where the point is inside the polygon result = gpd.sjoin(point_gdf, world, how='left', predicate='within') Let me know in the comments below

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'

# 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.

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