This tutorial demonstrates how to build a spatial graph learning pipeline for urban function inference. It utilizes libraries like city2graph, OSMnx, and PyTorch Geometric to process OpenStreetMap data, construct graph structures, and train a GraphSAGE model. The process involves collecting Points of Interest (POI) and street network data, engineering spatial features, and creating both heterogeneous and homogeneous graph representations for predicting POI categories based on spatial context. AI
IMPACT Provides a practical guide for applying graph neural networks to urban planning and analysis.
RANK_REASON This is a tutorial demonstrating a coding implementation and workflow for a specific machine learning task, rather than a novel research paper or a new model release. [lever_c_demoted from research: ic=1 ai=1.0]
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