Understanding the evolving discipline of crime situations is a long-standing but significant problem. Former methods prefer the stochastic modeling of the crime phenomenon in physics or statistical equations, which are elegant in theoretical explanations but less efficient in real applications. Recently, some data-driven models, especially neural network models, are illustrating promising performance in capturing dynamics of the complex phenomenon, and available massive dataset enables the task-beneficial information utilization. However, there exist several difficulties in regional crime situation awareness, including the high dimensionality, the intractable correlations as well as information redundancies in spatio-temporal dataset. To achieve efficient information processing and disentangle relationships from a recent crime dataset of fifteen years, we construct the crime situation awareness network (CSAN) as a new benchmark forecasting model via integrating structures of variational auto-encoders and context-based sequence generative neural network. Final experiments demonstrate that CSAN mostly outperforms other commonly-used spatio-temporal forecasting algorithms, such as Conv-LSTM, in regional multi-type crime frequency prediction.

了解犯罪情况的发展纪律是一个长期但重要的问题。以前的方法更喜欢在物理学或统计方程中对犯罪现象进行随机建模，这在理论解释上很优雅，但在实际应用中效率较低。最近，一些数据驱动的模型（尤其是神经网络模型）在捕获复杂现象的动态方面显示出令人鼓舞的性能，而可用的大量数据集可实现有益于任务的信息利用。但是，区域犯罪态势感知存在一些困难，包括时空数据集的高维度，难解的相关性以及信息冗余。为了从十五年来的最新犯罪数据集中实现高效的信息处理和纠缠关系，我们通过整合变分自动编码器和基于上下文的序列生成神经网络的结构，将犯罪情况意识网络（CSAN）构建为新的基准预测模型。最终实验表明，在区域多类型犯罪频率预测中，CSAN在大多数情况下优于其他常用的时空预测算法，例如Conv-LSTM。