Summary  
This chapter explains how to calculate distances between points using various metrics and how to build and query spatial indexes (e.g., k-d trees) for efficient nearest-neighbor and range searches.  

General domain of usage  
Geographic information systems (GIS) and location-based services

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


def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)


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

        # Reference GeoDataFrame
        points = [
            Point(0, 0),
            Point(1, 1),
            Point(2, 2),
            Point(-1, -1)
        ]
        cls.gdf = gpd.GeoDataFrame(geometry=points)

        # Expected distances
        ref_point = Point(0, 0)
        cls.ref_distances = [
            geom.distance(ref_point) for geom in cls.gdf.geometry
        ]

        # Expected spatial index results
        bbox = (-0.5, -0.5, 1.5, 1.5)
        cls.ref_matches = sorted(
            list(cls.gdf.sindex.intersection(bbox))
        )

    def test_distances_exist(self):
        _dynamic_test(
            self,
            hasattr(self.m, "distances"),
            "distances variable exists",
            "Expected variable `distances` to be defined"
        )

    def test_distances_correct(self):
        _dynamic_test(
            self,
            list(self.m.distances) == self.ref_distances,
            "Distances are computed correctly",
            "Distance calculation is incorrect"
        )

    def test_spatial_index_exist(self):
        _dynamic_test(
            self,
            hasattr(self.m, "matches"),
            "matches variable exists",
            "Expected variable `matches` to be defined"
        )

    def test_spatial_index_correct(self):
        _dynamic_test(
            self,
            sorted(self.m.matches) == self.ref_matches,
            "Spatial index query returned correct results",
            "Spatial index query returned incorrect results"
        )


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: Distance Calculations and Spatial Indexing

解答