Exposure to crime and violence can harm individuals' quality of life and the
economic growth of communities. In light of the rapid development in machine
learning, there is a rise in the need to explore automated solutions to prevent
crimes. With the increasing availability of both fine-grained urban and public
service data, there is a recent surge in fusing such cross-domain information
to facilitate crime prediction. By capturing the information about social
structure, environment, and crime trends, existing machine learning predictive
models have explored the dynamic crime patterns from different views. However,
these approaches mostly convert such multi-source knowledge into implicit and
latent representations (e.g., learned embeddings of districts), making it still
a challenge to investigate the impacts of explicit factors for the occurrences
of crimes behind the scenes. In this paper, we present a Spatial-Temporal
Metapath guided Explainable Crime prediction (STMEC) framework to capture
dynamic patterns of crime behaviours and explicitly characterize how the
environmental and social factors mutually interact to produce the forecasts.
Extensive experiments show the superiority of STMEC compared with other
advanced spatiotemporal models, especially in predicting felonies (e.g.,
robberies and assaults with dangerous weapons).

本文提出了一种空间时间元路径引导的可解释犯罪预测（STMEC）框架，通过捕捉社会结构、环境和犯罪趋势的信息，明确地表征环境和社会因素相互作用产生预测。大量实验表明 STMEC 在预测罪行（如抢劫和使用危险武器的攻击）方面优于其他先进的时空模型。