Summary  
This chapter demonstrates how to perform spatial joins between two geospatial datasets and visualize the merged results through mapping.

General domain of usage  
Geospatial analysis

import unittest
import importlib
import geopandas as gpd
from shapely.geometry import Point, Polygon

class TestUserCode(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.m = importlib.import_module("user_code")

        # Reconstruct reference data
        polygons = [
            Polygon([(0,0),(2,0),(2,2),(0,2)]),
            Polygon([(2,0),(4,0),(4,2),(2,2)])
        ]
        cls.regions = gpd.GeoDataFrame({"region_id": [1, 2]}, geometry=polygons)

        points = [
            Point(0.5, 1.0),
            Point(1.5, 0.5),
            Point(3.0, 1.0),
            Point(3.2, 0.8)
        ]
        points_gdf = gpd.GeoDataFrame(geometry=points)

        joined = gpd.sjoin(points_gdf, cls.regions, how="left", predicate="within")
        counts = joined.groupby("region_id").size().reset_index(name="count")
        cls.expected = cls.regions.merge(counts, on="region_id", how="left")

    def test_region_counts(self):
        result = self.m.regions_with_counts[["region_id", "count"]]
        expected = self.expected[["region_id", "count"]]

        self.assertTrue(
            result.equals(expected),
            "Spatial join or region point counts are incorrect."
        )

if __name__ == "__main__":
    unittest.main()

test_code.py

An intermediate-level course introducing the core concepts, techniques, and practical skills for geospatial analysis using Python and key open-source libraries. Learn to work with vector and raster data, perform spatial operations, and visualize geographic information.

Explore the essential building blocks of geospatial data, including data types, geometries, GIS concepts, and reading common vector formats.

Learn to manipulate, analyze, and transform geospatial data using Python, including spatial joins, overlays, and distance calculations.

Visualize geospatial data, create thematic maps, and apply spatial analysis to real-world scenarios using Python.

Challenge: Spatial Join and Mapping

Solution

Challenge: Spatial Join and Mapping

Solution