Discovering Spatial-Temporal Indication of Crime Association (STICA)
Discovering Spatial-Temporal Indication of Crime Association (STICA)
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DOI：
10.3390/ijgi10020067
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
Chao Jiang;Lin Liu;Xiaoxing Qin;Suhong Zhou;Kai Liu
中科院分区：
文献类型：
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作者：
Chao Jiang;Lin Liu;Xiaoxing Qin;Suhong Zhou;Kai Liu
近年来，空间和时间因素相结合的重要性日益得到认识，但基于地点的犯罪研究中的相关模式分析方法仍需进一步发展，以明确表明相关因素影响范围的时空位置。本文提出了一种时空关联(STIA)的方法，以便于识别在不同时空尺度上运行的犯罪的主要促成因素。该方法的基本原理是逐步识别具有不同时间犯罪模式的空间区域。结合核密度估计、k中值中心聚类和专题地图，将STICA方法的一个具体实施应用于理解城市半岛中国的入室盗窃事件。实证结果包括：(1)基于STICA结果，犯罪的主要时间稳定性和时变性因素都可以用不同空间区域的时间犯罪模式的差异来指示。(2)这些因素的空间范围可以启发对产生犯罪模式的相互作用的理解，特别是关于时间上的瞬变和空间上的全球因素如何通过稳定因素的中介来产生局部犯罪多发区的理解。(3)STICA结果可以揭示稳定因素的空间背景效应，这对改进犯罪模式建模具有重要价值。正如所证明的那样，STICA方法在探索犯罪的成因方面是有效的，并显示出在以地点为基础的犯罪研究中提供新视野的巨大潜力。
The importance of combining spatial and temporal aspects has been increasingly recognized over recent years, yet pertinent pattern analysis methods in place-based crime research still need further development to explicitly indicate spatial-temporal localities of pertinent factors’ influence ranges. This paper proposes an approach, Spatial-Temporal Indication of Crime Association (STICA), to facilitate identifying the main contributing factors of crime, which are operated at diverse spatial-temporal scales. The method’s rationale is to progressively discern the spatial zones with diverse temporal crime patterns. A specific implementation of the STICA approach, by combining kernel density estimation, k-median-centers clustering, and thematic mapping, is applied to understand the burglary in an urban peninsula, China. The empirical findings include: (1) both the main time-stable and time-varying factors of crime can be indicated with the disparities of temporal crime patterns for different spatial zones based on the STICA results. (2) The spatial range of these factors can enlighten the understanding of interactions for generating crime patterns, especially with regards to how temporally transient and spatially global factors can produce a locally crime-ridden zone through the mediation of stable factors. (3) The STICA results can reveal the spatially contextual effects of stable factors, which are of great value to improve modeling crime patterns. As demonstrated, the STICA approach is effective in exploring contributing factors of crime and has shown great potential for providing a new vision in place-based crime research.