Use of city2graph, OSMnx and PyTorch Geometric to implement spatial Graph Neural networks for the inference of urban functions

poi_gdf["cx"] = poi_gdf.geometry.x
poi_gdf["cy"] = poi_gdf.geometry.y
Coords = Poi_gdf[["cx", "cy"]].to_numpy()
nn = NearestNeighbors(radius=150.0).fit(coords)
poi_gdf["local_density"] = [len(idx) - 1 for idx in nn.radius_neighbors(coords, return_distance=False)]
If segments_gdf does not equal None, and len (segments_gdf), then:
   try:
       joined = gpd.sjoin_nearest(poi_gdf[["geometry"]], segments_gdf[["geometry"]],
                                  distance_col="dist_street")
       poi_gdf["dist_street"] = joined.groupby(level=0)["dist_street"].min().reindex(poi_gdf.index).fillna(0.0)
 Except Exception
       poi_gdf["dist_street"] = 0.0
else:
   poi_gdf["dist_street"] = 0.0
poi_gdf["category"] = poi_gdf["category"].astype("category")
poi_gdf["label"] = poi_gdf["category"].cat.codes.astype(int)
CLASS_NAMES = list(poi_gdf["category"].cat.categories)
print("Classes:", CLASS_NAMES)
def graph_stats (name, builder:
   try:
 Builder = nodes + edges()
       deg = pd.Series(np.r_[edges.index.get_level_values(0),
                             edges.index.get_level_values(1)]).value_counts()
       return name, len(edges), round(deg.mean(), 2), (nodes, edges)
 Except Exception As e.
 The return number is fBuilders ="ERR: {e}", None, None
builders = {
   "KNN (k=8)":  lambda: c2g.knn_graph(poi_gdf, distance_metric="euclidean", k=8, as_nx=False),
   "Delaunay":   lambda: c2g.delaunay_graph(poi_gdf, as_nx=False),
   "Gabriel":    lambda: c2g.gabriel_graph(poi_gdf, as_nx=False),
   "RNG":        lambda: c2g.relative_neighborhood_graph(poi_gdf, as_nx=False),
   "EMST":       lambda: c2g.euclidean_minimum_spanning_tree(poi_gdf, as_nx=False),
   "Waxman":     lambda: c2g.waxman_graph(poi_gdf, distance_metric="euclidean", r0=150, beta=0.6),
}
print("n--- Proximity graph comparison ---")
print(f"{'graph':10}{'avg_degree':>12}")
built = {}
Builders.items for nm and b():
   name, ne, avgdeg, payload = graph_stats(nm, b)
   print(f"{name:10}{str(avgdeg):>12}")
 If payload is built[nm] The payload
Axes = plt.subplots (1, 3, figsize=(16, 5 )
for ax, key in zip(axes, ["KNN (k=8)", "Delaunay", "EMST"]):
 If key is built in:
 Built = n_, E_[key]
       e_.plot(ax=ax, linewidth=0.4, color="#3b7dd8", alpha=0.6)
       poi_gdf.plot(ax=ax, markersize=4, color="#d83b5c")
       ax.set_title(key); ax.set_axis_off()
plt.suptitle("Spatial graph topologies on the same POI set", y=1.02)
plt.tight_layout(); plt.show()