STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions
This provides a modular and extensible tool for researchers and practitioners in urban computing to handle complex spatio-temporal data, though it is incremental as it builds on existing GNN and mapping techniques.
The authors tackled the challenge of predictive analytics for dynamic urban spatio-temporal data by introducing STM-Graph, an open-source Python framework that transforms raw data into graph representations for Graph Neural Network training and prediction, integrating mapping methods, urban features, models, and tools to facilitate experimentation and benchmarking.
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.