  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.
