多尺度探查偷车案件的犯罪时空热点

Detecting spatiotemporal hotspots for vehicle thefts by multi-scale analysis

犯罪热点探查逐渐成为被动式转向主动式警务工作的关键.现有许多研究提出多种犯罪分析方法,得到较好成果.本文关注时空分析中的尺度问题,以某区偷盗"三车"案件为例,提出分析时空热点的两种多尺度方法,并据此分析,为警务实务提供参考.这两种方法与结论是:①结合尺度法与传统的严格法整合时间数据,在单尺度数据不足时也能探查到时间热点.分析得到研究区偷车案件存在较显著的30 d周期和极显著的20 h周期,警务工作可借此调整轮班时间.②以30 d周期作分析,在DBSCAN算法中设置评估函数(凸包案件密度中值),自适应选择最优尺度探查空间最优热点分布,分布的最优性由主流的PAI指数验证.分析得到研究区的某些地铁站与居民区需要重点防护.

The detection of crime hot spots has become increasingly prominent in the conversion from reactive to active policing. There exist many crime analysis methods with good results. This paper focuses on scale effects in analysis. We proposed two multi-scale methods to detect temporal and spatial hotspots for vehicle thefts in a district,whose results were used for policing references. These two methods and their results are stated as follows： ①a scaling method is proposed and combined with a rigid process to aggregate temporal data. Through this combination, temporal hotspots can be detected when data are not sufficient under mono-scale. Results showed that daily hot spots（30 days） and hourly hot spots（20 hours） of vehicle thefts are significant at the study site, on which the rearrangement of shift intervals can be based; ② on the basis of daily hot spots, we set a median case density of a convex hull as the evaluation function when applying DBSCAN.The optimal scale, verified by the popular Prediction Accuracy Index, was adaptively chosen. We found that several metro line stations and residence zones need key protection.