Dynamic Transfer Learning with Co-occurrence-guided Multi-source Fusion for Urban Spatio-temporal Crime Prediction

Chen Cui^1*Ziwan Zheng^1*Hao Du¹Wen Wang²

• ¹Zhejiang Police College, Hangzhou, China
• ²Zhejiang Supcon Technology Co Ltd, Hangzhou, China

Spatio-temporal crime prediction is crucial for optimizing police resource allocation but faces challenges including data sparsity, which hinders models from extracting effective patterns and limits generalization robustness—and the underutilization of cross-type crime co-occurrence correlations. To address these issues, we propose a transfer learning approach that explores underlying cross-type relationships, enabling the sharing of spatio-temporal features across crime types and alleviating data sparsity. An adaptive weight updating mechanism is incorporated to strengthen the perception of distinct crime categories, while the impacts of POIs, meteorological factors, and other features are also analyzed. Experiments on real-world data from a Chinese city in J Province, China, show that our model comprehensively captures latent features across crime types, thereby enhancing predictive performance accuracy and generalization. and robustness, particularly for crime types with sparse data. Moreover, it effectively incorporates environmental features, further improves crime prediction performance.