When working with point‑of‑interest (POI) datasets, analysts often need to quantify how crowded each location is, understand its proximity to infrastructure like streets, and explore the underlying spatial relationships through graph models. The typical workflow involves several pain points: calculating a local density metric that reflects the number of neighbors within a fixed radius, assigning the nearest street distance for each POI, encoding categorical attributes for machine‑learning pipelines, and comparing different proximity graph constructions (K‑nearest‑neighbors, Delaunay triangulation, Gabriel graph, relative neighborhood graph, Euclidean minimum spanning tree, Waxman model) to see which topology best captures the spatial structure. Manual implementations are error‑prone, slow, and hard to reproduce, especially when dealing with large GeoDataFrames.
A practical solution streamlines these steps using well‑tested libraries. First, extract the x and y coordinates from the geometry column and feed them to scikit‑learn’s NearestNeighbors with a radius of 150 meters; the length of each neighbor list minus one gives an intuitive local density score. Next, if a street network GeoDataFrame is available, use geopandas.sjoin_nearest to compute the minimum distance to any street segment, filling missing values with zero. Convert the POI category column to a categorical type and retrieve its integer codes for downstream modeling, preserving the original category names in a list for reference.
To evaluate graph topologies, define a set of builder functions that call the desired graph‑construction routines (e.g., c2g.knn_graph, c2g.delaunay_graph, etc.). A helper wrapper executes each builder, catches exceptions, and returns the number of edges and the average degree of the resulting graph. This lets you quickly compare metrics across methods in a tabular format. Finally, plot the three most informative graphs side‑by‑side with matplotlib, overlaying the POI points to visually inspect how each topology connects the same spatial set. The whole pipeline runs in a few seconds on typical urban POI layers, delivering reproducible density measures, street proximity features, categorical labels, and a clear performance‑visual comparison of spatial graph models—exactly what analysts need to move from raw points to actionable insights.
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