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arXiv · 2509.10528

STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions

Amirhossein Ghaffari·Huong Nguyen·Lauri Lovén·Ekaterina Gilman
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Abstract

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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Amirhossein Ghaffari, Huong Nguyen, Lauri Lovén, Ekaterina Gilman. 2025-09-04. STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions. https://doi.org/10.1145/3746252.3761645

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