  Crime prediction is crucial for public safety and resource optimization, yet
is very challenging due to two aspects: i) the dynamics of criminal patterns
across time and space, crime events are distributed unevenly on both spatial
and temporal domains; ii) time-evolving dependencies between different types of
crimes (e.g., Theft, Robbery, Assault, Damage) which reveal fine-grained
semantics of crimes. To tackle these challenges, we propose Spatial-Temporal
Sequential Hypergraph Network (ST-SHN) to collectively encode complex crime
spatial-temporal patterns as well as the underlying category-wise crime
semantic relationships. In specific, to handle spatial-temporal dynamics under
the long-range and global context, we design a graph-structured message passing
architecture with the integration of the hypergraph learning paradigm. To
capture category-wise crime heterogeneous relations in a dynamic environment,
we introduce a multi-channel routing mechanism to learn the time-evolving
structural dependency across crime types. We conduct extensive experiments on
two real-world datasets, showing that our proposed ST-SHN framework can
significantly improve the prediction performance as compared to various
state-of-the-art baselines. The source code is available at:
https://github.com/akaxlh/ST-SHN.
