School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China.
Artifical Intelligence School, Wuchang University of Technology, Wuhan, 430223, China.
Sci Rep. 2020 Oct 14;10(1):17262. doi: 10.1038/s41598-020-74248-w.
Urban crime incidents always exhibit a structure of spatio-temporal dependence. Exploration of the spatio-temporal interactions of crime incidents is critical to understanding the occurrence mechanism and spatial transmission characteristics of crime occurrences, therefore facilitating the determination of policing practices. Although previous researches have repeatedly demonstrated that the crime incidents are spatially clustered, the anisotropic characteristics of spatial interaction has not been fully considered and the detailed spatial transmission of crime incidents has rarely been explored. To better understand the spatio-temporal interaction patterns of crime occurrence, this study proposes a new spatial association mining approach to discover significant spatial transmission routes and related high flow regions. First, all near repeat crime pairs are identified based on spatio-temporal proximity. Then, these links between close pairs are simplified by spatial aggregation on spatial grids. Based on that, measures of the spatio-temporal interactions are defined and a spatial association pattern mining approach is developed to discover significant spatial interaction patterns. Finally, the relationship between significant spatial transmission patterns and road network structure is analyzed. The experimental results demonstrate that our approach is able to effectively discover spatial transmission patterns from massive crime incidents data. Our results are expected to provide effective guidance for crime pattern analysis and even crime prevention.
城市犯罪事件总是呈现出时空相依的结构。探索犯罪事件的时空相互作用对于理解犯罪发生的发生机制和空间传播特征至关重要，从而有助于确定警务实践。尽管先前的研究反复表明犯罪事件在空间上是聚集的，但空间相互作用的各向异性特征尚未得到充分考虑，犯罪事件的详细空间传播也很少被探索。为了更好地理解犯罪发生的时空相互作用模式，本研究提出了一种新的空间关联挖掘方法，以发现显著的空间传输路径和相关的高流量区域。首先，根据时空接近性识别所有近重复犯罪对。然后，通过在空间网格上进行空间聚合来简化这些近距离对之间的联系。在此基础上，定义时空相互作用的度量，并开发空间关联模式挖掘方法来发现显著的空间相互作用模式。最后，分析显著的空间传输模式与道路网络结构之间的关系。实验结果表明，我们的方法能够有效地从大量犯罪事件数据中发现空间传输模式。我们的研究结果有望为犯罪模式分析甚至预防犯罪提供有效的指导。
