STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions
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.
Cite
@article{arxiv.2509.10528,
title = {STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions},
author = {Amirhossein Ghaffari and Huong Nguyen and Lauri Lovén and Ekaterina Gilman},
journal= {arXiv preprint arXiv:2509.10528},
year = {2025}
}
Comments
Accepted manuscript (CC BY 4.0). To appear in ACM CIKM 2025, Seoul, Nov 10-14, 2025. DOI: 10.1145/3746252.3761645. The Version of Record will be uploaded when available