Real-time prediction model of public safety events driven by multi-source heterogeneous data

To address the challenge of efficiently integrating multi-source heterogeneous data to improve the accuracy of public safety event prediction, this study proposes and validates a novel public safety event prediction model, GATPNet, based on multi-source heterogeneous data. The model integrates Graph Attention Networks (GAT), Spatiotemporal Transformers, and Proximal Policy Optimization (PPO) to achieve effective data fusion, spatiotemporal feature extraction, and real-time decision support. Through experiments conducted on the Los Angeles Crime Data and CrisisLexT26 datasets, this study demonstrates that GATPNet outperforms other baseline models. On the Los Angeles Crime Data dataset, GATPNet achieved an accuracy of 90%, recall of 89%, Spatiotemporal Prediction Accuracy (STPA) of 80%, and a response time of 1.9 s, showing a 5% improvement in accuracy and a 10% improvement in STPA over the best baseline method. On the CrisisLexT26 dataset, it achieved an accuracy of 89%, recall of 88%, STPA of 78%, and a response time of 2.1 s, showing a 4% improvement in accuracy and a 6% improvement in STPA over the best baseline method. Additionally, ablation experiments further indicate that each module plays a critical role in improving overall performance. Despite the model’s high computational complexity when handling large-scale heterogeneous data and the limited coverage of the datasets, GATPNet still demonstrates its broad application potential in public safety event prediction and management, offering effective technical support for social governance and emergency management.