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The Utility of Hotspot Mapping for Predicting Spatial Patterns of Crime

机译：热点映射用于预测犯罪的空间格局的实用程序

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Hotspot mapping is a popular analytical technique that is used to help identify where to target police and crime reduction resources. In essence, hotspot mapping is used as a basic form of crime prediction, relying on retrospective data to identify the areas of high concentrations of crime and where policing and other crime reduction resources should be deployed. A number of different mapping techniques are used for identifying hotspots of crime - point mapping, thematic mapping of geographic areas (e.g. Census areas), spatial ellipses, grid thematic mapping and kernel density estimation (KDE). Several research studies have discussed the use of these methods for identifying hotspots of crime, usually based on their ease of use and ability to spatially interpret the location, size, shape and orientation of clusters of crime incidents. Yet surprising, very little research has compared how hotspot mapping techniques can accurately predict where crimes will occur in the future. This research uses crime data for a period before a fixed date (that has already passed) to generate hotspot maps, and test their accuracy for predicting where crimes will occur next. Hotspot mapping accuracy is compared in relation to the mapping technique that is used to identify concentrations of crime events (thematic mapping of Census Output Areas, spatial ellipses, grid thematic mapping, and KDE) and by crime type - four crime types are compared (burglary, street crime, theft from vehicles and theft of vehicles). The results from this research indicate that crime hotspot mapping prediction abilities differ between the different techniques and differ by crime type. KDE was the technique that consistently outperformed the others, while street crime hotspot maps were consistently better at predicting where future street crime would occur when compared to results for the hotspot maps of different crime types. The research offers the opportunity to benchmark comparative research of other techniques and other crime types, including comparisons between advanced spatial analysis techniques and prediction mapping methods. Understanding how hotspot mapping can predict spatial patterns of crime and how different mapping methods compare will help to better inform their application in practice.
机译：热点映射是一种流行的分析技术，用于帮助确定将目标对准警察和减少犯罪的资源。本质上，热点映射是犯罪预测的基本形式，它依赖于回顾性数据来确定犯罪高度集中的区域以及应在哪里部署警务和其他减少犯罪的资源。多种不同的制图技术用于识别犯罪热点-点制图，地理区域（例如人口普查区域）的主题制图，空间椭圆，网格主题制图和核密度估计（KDE）。几项研究已经讨论了使用这些方法识别犯罪热点的方法，通常是基于其易用性以及在空间上解释犯罪事件群的位置，大小，形状和方向的能力。然而，令人惊讶的是，很少有研究比较热点地图绘制技术如何准确预测将来的犯罪发生地。这项研究使用固定日期之前（已经过去）的一段时间内的犯罪数据来生成热点地图，并测试其准确性以预测下一次犯罪发生的地方。比较热点映射的准确性和用于确定犯罪事件集中程度的映射技术（人口普查输出区域，空间椭圆，网格主题映射和KDE的主题映射），并按犯罪类型分类-比较了四种犯罪类型（盗窃） ，街头犯罪，车辆盗窃和车辆盗窃）。这项研究的结果表明，犯罪热点映射的预测能力在不同技术之间有所不同，并且因犯罪类型而异。与不同犯罪类型的热点地图的结果相比，KDE是始终领先于其他技术的技术，而街头犯罪热点地图始终能够更好地预测未来的街头犯罪将发生在哪里。该研究提供了对其他技术和其他犯罪类型的比较研究进行基准测试的机会，其中包括先进空间分析技术和预测映射方法之间的比较。了解热点映射如何可以预测犯罪的空间格局以及如何比较不同的映射方法将有助于更好地指导其在实践中的应用。

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