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.
# 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') Python GeoSpatial Analysis Essentials
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. from shapely
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' Always project to a local or equal-area CRS first