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

2025/09/04 by Ghaffari, Amirhossein, Nguyen, Huong, Lovén, Lauri +1
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2509.10528

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.

Citations

• Modeling and Analyzing Urban Networks and Amenities With OSMnx
• EasyST: A Simple Framework for Spatio-Temporal Prediction
• FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction
• Using Graph Neural Networks to Predict Local Culture
• Explainable Spatio-Temporal Graph Neural Networks
• Spatio-Temporal Meta Contrastive Learning
• UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction
• Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting
• Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution
• PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural\n Machine Learning Models
• The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
• Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction

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