{"ID":2874796,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10528","arxiv_id":"2509.10528","title":"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.","short_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....","url_abs":"https://arxiv.org/abs/2509.10528","url_pdf":"https://arxiv.org/pdf/2509.10528v1","authors":"[\"Amirhossein Ghaffari\",\"Huong Nguyen\",\"Lauri Lovén\",\"Ekaterina Gilman\"]","published":"2025-09-04T21:36:48Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Graph Neural Network\"]","has_code":false,"code_links":[{"ID":610169,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2874796,"paper_url":"https://arxiv.org/abs/2509.10528","paper_title":"STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions","repo_url":"https://github.com/Ahghaffari/stm_graph","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0},{"ID":610170,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2874796,"paper_url":"https://arxiv.org/abs/2509.10528","paper_title":"STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions","repo_url":"https://github.com/tuminguyen/stm_graph_gui","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}