Work

Dynamic transfer learning with co-occurrence-guided multi-source fusion for urban spatio-temporal crime prediction

Year: 2026

Type: article

Abstract: 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 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 enhance the perception of distinct crime categories, while the impacts of points of interest (POIs), meteorological factors, and other features are also analyzed. Experiments on real-world data from a Chinese city show that our model comprehensively captures latent features across crime types, thereby enhancing predictive performance and robustness, particularly for crime types with sparse data. Moreover, it effectively incorporates environmental features, further improving crime prediction performance.

Source: Frontiers in Big Data

DOI: 10.3389/fdata.2026.1697392

Authors: Chen Cui, Ziwan Zheng, Hao Du, Wen Wang

Institution: Hangzhou Dianzi University

Language: English

Topic: Crime Patterns and Interventions

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

SDGs: Sustainable cities and communities, Peace, justice, and strong institutions

Open Access status: gold

Estimated APC paid: $1,418