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Discovering significant situational profiles of crime occurrence by modeling complex spatial interactions
Spatial Statistics ( IF 2.3 ) Pub Date : 2020-07-06 , DOI: 10.1016/j.spasta.2020.100463
Zhanjun He , Zhong Xie , Liang Wu , Liufeng Tao

The joint influence of different facilities plays an important role in understanding situational profiles of crime incidents. While spatial conjunctive analysis of case configurations (CACC) is widely used to explore situational profiles of crime, complex spatial interactions (e.g., spatial autocorrelation of crime incidents, spatial interactions among multiple facilities) have not been fully considered. Drawing on previous environmental criminology research, this study extends the CACC by modeling complex spatial interactions between crime and facilities. First, the spatial interaction range between single facility and crime incidents is quantitatively measured by the cross-type pair-correlation function. Then, the relationship between different situational profiles is modeled, and a “bottom-up” strategy is applied to generate potential situational profiles. Finally, significant situational profiles are selected via Monte Carlo testing by modeling the complex structure of crime incidents. The effectiveness of the proposed approach is evaluated by both a synthetic and real dataset. The experimental result manifests that the proposed approach can effectively eliminate the influence of independent facilities and more accurately identify those significant situation profiles. The discovered significant situational profiles have a positive guiding effect in understanding the spatial context for crime occurrence, thereby facilitating crime prevention.



中文翻译:

通过对复杂的空间互动进行建模来发现犯罪发生的重要情境概况

不同机构的共同影响在理解犯罪事件的情境方面起着重要作用。尽管案例配置的空间合取分析(CACC)被广泛用于探究犯罪的情境概况,但尚未充分考虑复杂的空间相互作用(例如,犯罪事件的空间自相关,多种设施之间的空间相互作用)。该研究借鉴先前的环境犯罪学研究,通过对犯罪与设施之间的复杂空间相互作用进行建模来扩展CACC。首先,通过交叉类型对相关函数来定量测量单个设施与犯罪事件之间的空间相互作用范围。然后,对不同情况档案之间的关系进行建模,然后采用“自下而上”的策略来生成潜在的情况简介。最后,通过对犯罪事件的复杂结构进行建模,通过蒙特卡洛测试选择重要的情境概况。所提出的方法的有效性通过综合数据集和实际数据集进行评估。实验结果表明,所提出的方法可以有效消除独立设施的影响,更准确地识别那些重要的情况。发现的重要情况概况对了解犯罪发生的空间背景具有积极的指导作用,从而有助于预防犯罪。所提出的方法的有效性通过综合数据集和实际数据集进行评估。实验结果表明,所提出的方法可以有效消除独立设施的影响,更准确地识别那些重要的情况。发现的重要情况概况对了解犯罪发生的空间背景具有积极的指导作用,从而有助于预防犯罪。所提出的方法的有效性通过综合数据集和实际数据集进行评估。实验结果表明,所提出的方法可以有效消除独立设施的影响,更准确地识别那些重要的情况。发现的重要情况简介对了解犯罪发生的空间背景具有积极的指导作用,从而有助于预防犯罪。

更新日期:2020-07-06
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