论文标题
预测犯罪热点映射和评估的时空内核密度估计框架
A Spatio-Temporal Kernel Density Estimation Framework for Predictive Crime Hotspot Mapping and Evaluation
论文作者
论文摘要
预测热点映射在热点策略中起着至关重要的作用。现有的方法，例如流行的内核密度估计（KDE），不考虑犯罪的时间维度。在相关领域的最新作品的基础上，本文提出了一个时空框架，用于预测性热点映射和评估。与此范围中的现有工作相比，提出的框架具有四个主要特征：（1）适用时空内核密度估计（STKDE）方法，将预测性热点映射中的时间组件包括在内，（2）数据驱动的优化技术，可能是适用于适当的频率，可用于选择适当的频率，以选择适当的量子，以选择依次（3）。提出了一个新的度量标准，即预测精度指数（PAI）曲线，提出了一个新的度量标准，以评估多个面积尺度的预测热点。该框架在2011年在路易斯安那州巴吞鲁日的住宅盗窃案的案例研究中进行了说明，结果验证了其效用。
Predictive hotspot mapping plays a critical role in hotspot policing. Existing methods such as the popular kernel density estimation (KDE) do not consider the temporal dimension of crime. Building upon recent works in related fields, this article proposes a spatio-temporal framework for predictive hotspot mapping and evaluation. Comparing to existing work in this scope, the proposed framework has four major features: (1) a spatio-temporal kernel density estimation (STKDE) method is applied to include the temporal component in predictive hotspot mapping, (2) a data-driven optimization technique, the likelihood cross-validation, is used to select the most appropriate bandwidths, (3) a statistical significance test is designed to filter out false positives in the density estimates, and (4) a new metric, the predictive accuracy index (PAI) curve, is proposed to evaluate predictive hotspots at multiple areal scales. The framework is illustrated in a case study of residential burglaries in Baton Rouge, Louisiana in 2011, and the results validate its utility.
