手把手教你用图神经网络分析城市数据
本文介绍了一个端到端的空间图学习流程，使用city2graph从OpenStreetMap收集城市POI和街道网络数据，并构建合成回退数据以增强可靠性。通过工程化空间特征，构建多种邻近图族并比较其表示效果。将异构和同构图转换为PyTorch Geometric格式，训练GraphSAGE模型从空间结构预测POI类别。
A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric
We build an end-to-end spatial graph learning pipeline using city2graph. We collect urban POI and street network data from OpenStreetMap, with a synthetic fallback for reliability. We engineer spatial features, construct several proximity graph families, and compare how each represents the same urban environment. We then build heterogeneous and homogeneous graphs, convert them to PyTorch Geometric, and train a GraphSAGE model to predict POI categories from spatial structure. The post A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric appeared first on MarkTechPost .