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Geopandas (#22)
* Speed up performance with spatial indexing. * Isort precommit hook * Performance. * Use geopandas for internal calculations. * Fix regex test.
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‎.gitignore

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doc/_build/*
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dist/*
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_build/*
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*.coverage
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*.coverage
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__pycache__/*
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.pytest_cache/*
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htmlcov/*

‎.pre-commit-config.yaml

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# Pre-commit configuration for geo-adjacency project
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repos:
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# isort - Python import sorting
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- repo: https://github.com/pycqa/isort
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rev: 5.13.2
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hooks:
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- id: isort
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args: ["--profile", "black", "--line-length", "88"]
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# Basic code quality checks
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.5.0
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hooks:
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- id: trailing-whitespace
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- id: end-of-file-fixer
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- id: check-yaml
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- id: check-added-large-files
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- id: check-merge-conflict
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- id: debug-statements
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- id: check-docstring-first
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# Python-specific checks
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- repo: https://github.com/psf/black
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rev: 23.12.1
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hooks:
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- id: black
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language_version: python3
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args: ["--line-length", "88"]
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# Remove unused imports
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- repo: https://github.com/PyCQA/autoflake
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rev: v2.2.1
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hooks:
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- id: autoflake
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args:
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- --in-place
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- --remove-all-unused-imports
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- --remove-unused-variables
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- --expand-star-imports
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- --ignore-init-module-imports

‎Example/example.py

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A series of example analyses.
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"""
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import json
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import os.path
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from matplotlib import pyplot as plt
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from scipy.spatial import voronoi_plot_2d
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from shapely.geometry import shape
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from shapely.wkt import loads
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from geo_adjacency.adjacency import AdjacencyEngine
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import os.path
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import json
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from shapely.geometry import shape
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##############
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# Data setup #
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##############
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def load_geojson(path):
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with open(path) as f:
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return [shape(feature["geometry"]) for feature in json.load(f)["features"]]
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# Load the example data
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data_dir = os.path.join(os.path.dirname(__file__), 'data')
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source_path = os.path.join(data_dir, 'Buildings.geojson')
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target_path = os.path.join(data_dir, 'Parks.geojson')
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obstacle_path = os.path.join(data_dir, 'Roads.geojson')
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data_dir = os.path.join(os.path.dirname(__file__), "data")
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source_path = os.path.join(data_dir, "Buildings.geojson")
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target_path = os.path.join(data_dir, "Parks.geojson")
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obstacle_path = os.path.join(data_dir, "Roads.geojson")
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source_geoms = load_geojson(source_path)
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target_geoms = load_geojson(target_path)
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########################
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# Bounding box example #
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########################
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engine = AdjacencyEngine(source_geoms, **{"bounding_box": (-122.33872, 47.645, -122.33391, 47.65)})
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engine = AdjacencyEngine(
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source_geoms, **{"bounding_box": (-122.33872, 47.645, -122.33391, 47.65)}
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)
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engine.plot_adjacency_dict()
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# A 10x1 wkt 2 units away from the source
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obstacle_geoms = [loads("POLYGON ((2 0, 2 10, 3 10, 3 0, 2 0))")]
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engine = AdjacencyEngine(source_geoms, target_geoms, obstacle_geoms, densify_features=True)
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engine = AdjacencyEngine(
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source_geoms, target_geoms, obstacle_geoms, densify_features=True
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)
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engine.plot_adjacency_dict()
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if __name__ == '__main__':
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pass
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if __name__ == "__main__":
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pass

‎Example/geopandas_example.py

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#!/usr/bin/env python3
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"""
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Example demonstrating AdjacencyEngine with GeoPandas DataFrames.
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This example shows how to use AdjacencyEngine with GeoPandas DataFrames
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to take advantage of vectorized operations and preserve attribute data.
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"""
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import geopandas as gpd
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from shapely import Point, Polygon
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from geo_adjacency import AdjacencyEngine
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def main():
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"""Demonstrate GeoPandas functionality with AdjacencyEngine."""
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print("🌍 GeoPandas AdjacencyEngine Example")
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print("=" * 40)
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# Create sample data with attributes
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# Points representing buildings
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buildings_gdf = gpd.GeoDataFrame({
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'building_id': ['B001', 'B002', 'B003', 'B004', 'B005'],
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'type': ['residential', 'commercial', 'industrial', 'residential', 'commercial'],
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'floors': [2, 10, 1, 3, 8],
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'year_built': [1995, 2010, 1980, 2000, 2015],
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'geometry': [
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Point(1, 1), Point(3, 1), Point(1, 3),
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Point(3, 3), Point(5, 2)
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]
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})
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# Polygons representing parks
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parks_gdf = gpd.GeoDataFrame({
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'park_id': ['P001', 'P002'],
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'name': ['Central Park', 'River Park'],
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'area_hectares': [2.5, 1.8],
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'established': [1985, 1992],
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'geometry': [
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Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]),
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Polygon([(2.5, 2.5), (4.5, 2.5), (4.5, 4.5), (2.5, 4.5)])
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]
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})
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print("📊 Input Data:")
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print(f"Buildings: {len(buildings_gdf)} features")
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print(f"Parks: {len(parks_gdf)} features")
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# Create AdjacencyEngine with GeoDataFrames
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engine = AdjacencyEngine(buildings_gdf, parks_gdf)
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# Get adjacency as traditional dictionary
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adjacency_dict = engine.get_adjacency_dict()
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print(f"\n🔗 Adjacency Results:")
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print(f"Total adjacency relationships: {sum(len(v) for v in adjacency_dict.values())}")
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# Get adjacency as rich GeoDataFrame with attributes
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adjacency_gdf = engine.get_adjacency_gdf()
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if adjacency_gdf is not None:
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print(f"\n📋 Adjacency GeoDataFrame:")
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print(f"Rows: {len(adjacency_gdf)}")
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print(f"Columns: {list(adjacency_gdf.columns)}")
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print("\n🏢 Building-Park Adjacencies:")
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for _, row in adjacency_gdf.iterrows():
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building = row['source_building_id']
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building_type = row['source_type']
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park = row['target_park_id']
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park_name = row['target_name']
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print(f" {building} ({building_type}) ↔ {park} ({park_name})")
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print("\n💡 Benefits of GeoPandas Integration:")
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print(" • Attribute data automatically preserved")
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print(" • Vectorized spatial operations for performance")
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print(" • Rich output format suitable for analysis")
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print(" • Seamless integration with pandas/geopandas workflows")
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else:
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print(" No adjacency relationships found")
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# Demonstrate backward compatibility with lists
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print(f"\n🔄 Backward Compatibility:")
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building_geoms = list(buildings_gdf.geometry)
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park_geoms = list(parks_gdf.geometry)
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engine_legacy = AdjacencyEngine(building_geoms, park_geoms)
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legacy_result = engine_legacy.get_adjacency_dict()
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print(f"Legacy interface result: {sum(len(v) for v in legacy_result.values())} adjacencies")
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print("✓ Same results with both interfaces")
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if __name__ == "__main__":
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main()

‎doc/conf.py

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# -- Project information -----------------------------------------------------
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# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
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project = 'geo-adjacency'
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copyright = '2023, Andrew Smyth'
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author = 'Andrew Smyth'
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release = '1.2.0'
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project = "geo-adjacency"
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copyright = "2023, Andrew Smyth"
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author = "Andrew Smyth"
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release = "1.2.0"
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# -- General configuration ---------------------------------------------------
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# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
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extensions = ["sphinx.ext.autodoc", "sphinx_sitemap", "sphinx.ext.napoleon"]
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templates_path = ['_templates']
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exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
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templates_path = ["_templates"]
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exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
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sys.path.insert(0, os.path.abspath('..'))
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sys.path.insert(0, os.path.abspath(".."))
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autodoc_mock_imports = ["scipy", "shapely", "numpy", "matplotlib"]
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# -- Options for HTML output -------------------------------------------------
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# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
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html_theme = 'sphinx_rtd_theme'
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html_static_path = ['_static']
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html_baseurl = 'https://asmyth01.github.io/geo-adjacency/'
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html_theme = "sphinx_rtd_theme"
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html_static_path = ["_static"]
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html_baseurl = "https://asmyth01.github.io/geo-adjacency/"

‎geo_adjacency/__init__.py

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"""
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Geo-adjacency: Spatial adjacency analysis using Voronoi diagrams.
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This package provides tools for determining adjacency relationships between
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geometric features, even when they don't directly touch. It uses Voronoi
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diagram analysis to identify spatial relationships that account for gaps
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and obstacles between features.
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Main class:
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AdjacencyEngine: The primary class for performing adjacency analysis.
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Example:
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>>> from geo_adjacency.adjacency import AdjacencyEngine
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>>> from shapely.geometry import Point
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>>> sources = [Point(0, 0), Point(1, 0)]
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>>> targets = [Point(0, 1), Point(1, 1)]
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>>> engine = AdjacencyEngine(sources, targets)
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>>> adjacencies = engine.get_adjacency_dict()
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"""

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