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

## TL;DR

This paper introduces a new method for predicting urban crime by combining data from different crime types to improve accuracy, especially when data is limited.

## Contribution

A novel transfer learning approach that leverages cross-type crime co-occurrence to enhance spatio-temporal crime prediction.

## Key findings

- The model improves predictive performance and robustness, especially for crime types with sparse data.
- Incorporating environmental features like POIs and weather further enhances prediction accuracy.
- Experiments on real-world data demonstrate the effectiveness of the proposed method.

## 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.