Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction

TL;DR

This paper introduces a novel self-supervised learning framework using spatial-temporal hypergraphs to improve citywide crime prediction, especially in data-sparse scenarios, by capturing complex dependencies across regions and time.

Contribution

It proposes a cross-region hypergraph structure and a dual-stage self-supervised paradigm to enhance crime prediction accuracy beyond existing supervised methods.

Findings

Significantly outperforms state-of-the-art baselines on real datasets.

Effectively captures local and global crime patterns.

Improves representation of sparse crime data.

Abstract

Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence. Accurate crime prediction results are vital for the beforehand decision-making of government to alleviate the increasing concern about the public safety. While many efforts have been devoted to proposing various spatial-temporal forecasting techniques to explore dependence across locations and time periods, most of them follow a supervised learning manner, which limits their spatial-temporal representation ability on sparse crime data. Inspired by the recent success in self-supervised learning, this work proposes a Spatial-Temporal Hypergraph Self-Supervised Learning framework (ST-HSL) to tackle the label scarcity issue in crime prediction. Specifically, we propose the cross-region hypergraph structure learning to encode region-wise crime dependency under the entire urban space. Furthermore, we design the dual-stage self-supervised learning paradigm, to not only jointly capture local- and global-level spatial-temporal crime patterns, but also supplement the sparse crime representation by augmenting region self-discrimination. We perform extensive experiments on two real-life crime datasets. Evaluation results show that our ST-HSL significantly outperforms state-of-the-art baselines. Further analysis provides insights into the superiority of our ST-HSL method in the representation of spatial-temporal crime patterns. The implementation code is available at https://github.com/LZH-YS1998/STHSL.