ABSTRACT Accurate crime prediction can help allocate police resources for crime reduction and prevention. There are two popular approaches to predict criminal activities: one is based on historical crime, and the other is based on environmental variables correlated with criminal patterns. Previous research on geo-statistical modeling mainly considered one type of data in space-time domain, and few sought to blend multi-source data. In this research, we proposed a spatio-temporal Cokriging algorithm to integrate historical crime data and urban transitional zones for more accurate crime prediction. Time-series historical crime data were used as the primary variable, while urban transitional zones identified from the VIIRS nightlight imagery were used as the secondary co-variable. The algorithm has been applied to predict weekly-based street crime and hotspots in Cincinnati, Ohio. Statistical tests and Predictive Accuracy Index (PAI) and Predictive Efficiency Index (PEI) tests were used to validate predictions in comparison with those of the control group without using the co-variable. The validation results demonstrate that the proposed algorithm with historical crime data and urban transitional zones increased the correlation coefficient by 5.4% for weekdays and by 12.3% for weekends in statistical tests, and gained higher hit rates measured by PAI/PEI in the hotspots test.

中文翻译：

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使用历史犯罪数据和从夜光图像中识别的过渡区域进行犯罪预测的时空方法

摘要 准确的犯罪预测有助于分配警察资源以减少和预防犯罪。有两种流行的方法来预测犯罪活动：一种基于历史犯罪，另一种基于与犯罪模式相关的环境变量。以往地统计建模的研究主要考虑时空域中的一种数据，很少寻求融合多源数据。在这项研究中，我们提出了一种时空 Cokriging 算法来整合历史犯罪数据和城市过渡区，以实现更准确的犯罪预测。时间序列历史犯罪数据被用作主要变量，而从 VIIRS 夜光图像中识别出的城市过渡区被用作次要协变量。该算法已应用于预测俄亥俄州辛辛那提市每周的街头犯罪和热点。使用统计检验和预测准确度指数 (PAI) 和预测效率指数 (PEI) 检验来验证预测，与不使用协变量的对照组进行比较。验证结果表明，所提出的算法与历史犯罪数据和城市过渡区在统计测试中平日相关系数提高了 5.4%，周末相关系数提高了 12.3%，并在热点测试中获得了更高的 PAI/PEI 测量命中率。