将城市时空数据转为图结构，用GNN进行预测的开源框架
STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions
- 把原始城市事件数据自动转为适合GNN的图表示
- 集成多种空间映射方法与主流GNN模型，支持快速实验
- 带图形界面，适合科研与非专业用户使用
城市时空数据因其动态性和复杂性，给预测分析带来独特挑战。我们提出STM-Graph，一个开源Python框架，可将原始城市事件时空数据转化为适用于图神经网络（GNN）训练与预测的图表示。该框架整合了多种空间映射方法、来自OpenStreetMap的城市特征、多个GNN模型、全面的可视化工具以及面向专业与非专业用户的图形化界面（GUI）。其模块化与可扩展设计支持新映射方法与自定义模型的快速集成，显著提升实验与基准测试效率。该框架为城市计算领域的研究者与实践者提供有力支持。源码与GUI界面分别开源于：https://github.com/Ahghaffari/stm_graph 与 https://github.com/tuminguyen/stm_graph_gui。
原文摘要 · Abstract (English)
Urban spatio-temporal data present unique challenges for predictive analytics due to their dynamic and complex nature. We introduce STM-Graph, an open-source Python framework that transforms raw spatio-temporal urban event data into graph representations suitable for Graph Neural Network (GNN) training and prediction. STM-Graph integrates diverse spatial mapping methods, urban features from OpenStreetMap, multiple GNN models, comprehensive visualization tools, and a graphical user interface (GUI) suitable for professional and non-professional users. This modular and extensible framework facilitates rapid experimentation and benchmarking. It allows integration of new mapping methods and custom models, making it a valuable resource for researchers and practitioners in urban computing. The source code of the framework and GUI are available at: https://github.com/Ahghaffari/stm_graph and https://github.com/tuminguyen/stm_graph_gui.
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