Alazawi Mohammed A, Jiang Shiguo, Messner Steven F
Department of Information Science, University at Albany, State University of New York, Albany, NY, United States of America.
Department of Geography and Planning, University at Albany, State University of New York, Albany, NY, United States of America.
PLoS One. 2022 Feb 28;17(2):e0264718. doi: 10.1371/journal.pone.0264718. eCollection 2022.
A key issue in the spatial and temporal analysis of residential burglary is the choice of scale: spatial patterns might differ appreciably for different time periods and vary across geographic units of analysis. Based on point pattern analysis of burglary incidents in Columbus, Ohio during a 9-year period, this study develops an empirical framework to identify a useful spatial scale and its dependence on temporal aggregation. Our analysis reveals that residential burglary in Columbus clusters at a characteristic scale of 2.2 km. An ANOVA test shows no significant impact of temporal aggregation on spatial scale of clustering. This study demonstrates the value of point pattern analysis in identifying a scale for the analysis of crime patterns. Furthermore, the characteristic scale of clustering determined using our method has great potential applications: (1) it can reflect the spatial environment of criminogenic processes and thus be used to define the spatial boundary for place-based policing; (2) it can serve as a candidate for the bandwidth (search radius) for hot spot policing; (3) its independence of temporal aggregation implies that police officials need not be concerned about the shifting sizes of risk-areas depending on the time of the year.
不同时间段的空间模式可能存在显著差异，并且会因分析的地理单元不同而有所变化。基于俄亥俄州哥伦布市9年期间入室盗窃事件的点模式分析，本研究构建了一个实证框架，以确定一个有用的空间尺度及其对时间聚合的依赖性。我们的分析表明，哥伦布市的住宅入室盗窃在2.2公里的特征尺度上聚集。方差分析表明，时间聚合对聚集的空间尺度没有显著影响。本研究证明了点模式分析在确定犯罪模式分析尺度方面的价值。此外，使用我们的方法确定的聚集特征尺度具有很大的潜在应用价值：（1）它可以反映犯罪发生过程的空间环境，从而用于定义基于地点的警务的空间边界；（2）它可以作为热点警务带宽（搜索半径）的候选值；（3）其对时间聚合的独立性意味着警察官员无需担心风险区域的大小会因一年中的时间而变化。
