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.

翻译：准确的犯罪预测结果对于政府事先决策至关重要,以缓解对公共安全的日益关切。虽然许多努力都致力于提出各种空间时空预测技术,以探讨不同地点和不同时期的依赖性,但大多数犯罪都遵循监督学习方式,这限制了其在稀有犯罪数据方面的空间时空代表性能力。由于最近自我监督学习的成功,这项工作提议建立一个空间-时空超常自检学习框架(ST-HSL),以解决犯罪预测中的标签短缺问题。具体地说,我们建议跨区域的超时空结构学习在整个城市空间内对区域犯罪依赖性进行编码。此外,我们设计了双阶段自我监督学习模式,不仅限制了其在稀有犯罪数据方面的空间-时空代表性,而且还通过扩大区域自我歧视来补充稀少的犯罪代表性。我们在两个真实生活中对犯罪数据进行广泛的实验。ST-HS-HS-S-S-S-Servical-Servical-Servical-resserview reformal-ressal-hismal-hismal-hal-hismal-hal-hisal-hismal-hismal-hismal-hismal-hismal-s-hismal-hal-smastrismal-s-s-sal-smaisl-s-s-s-hisal-s-smaxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx