A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric
Quick Answer
This article details an end-to-end spatial graph learning pipeline using city2graph, OSMnx, and PyTorch Geometric to infer urban functions.
Quick Take
By collecting urban POI and street network data from OpenStreetMap, the authors compare various proximity graph families and train a GraphSAGE model to predict POI categories based on spatial structure.
Key Points
- Utilizes city2graph for building a spatial graph learning pipeline.
- Collects urban POI and street network data from OpenStreetMap.
- Compares multiple proximity graph families for urban representation.
- Constructs heterogeneous and homogeneous graphs for analysis.
- Trains a GraphSAGE model to predict POI categories.
Source Excerpt
Build a spatial GNN pipeline with city2graph, OSMnx, and PyTorch Geometric to construct proximity graphs and train GraphSAGE
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