STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918140402728960 |
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| author | Ghaffari, Amirhossein Nguyen, Huong Lovén, Lauri Gilman, Ekaterina |
| author_facet | Ghaffari, Amirhossein Nguyen, Huong Lovén, Lauri Gilman, Ekaterina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10528 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions Ghaffari, Amirhossein Nguyen, Huong Lovén, Lauri Gilman, Ekaterina Machine Learning Artificial Intelligence 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. |
| title | STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.10528 |