Real-time crime hot spot forecasting presents challenges to policing. There is a high volume of hot spot misclassifications and a lack of theoretical support for forecasting algorithms, especially in disciplines outside the fields of criminology and criminal justice. Transparency is particularly important as most hot spot forecasting models do not provide their underlying mechanisms. To address these challenges, we operationalize two different theories in our algorithm to forecast crime hot spots over Portland and Cincinnati. First, we use a population heterogeneity framework to find places that are consistent hot spots. Second, we use a state dependence model of the number of crimes in the time periods prior to the predicted month. This algorithm is implemented in Excel, making it extremely simple to apply and completely transparent. Our forecasting models show high accuracy and high efficiency in hot spot forecasting in both Portland and Cincinnati context. We suggest previously developed hot spot forecasting models need to be reconsidered.

中文翻译：

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一种理论驱动的实时犯罪热点预测算法

实时犯罪热点预测给警务带来挑战。热点错误分类的数量很多，并且缺乏对预测算法的理论支持，尤其是在犯罪学和刑事司法领域以外的学科中。透明度特别重要，因为大多数热点预测模型都没有提供其潜在机制。为了应对这些挑战，我们在算法中运用了两种不同的理论来预测波特兰和辛辛那提的犯罪热点。首先，我们使用人口异质性框架来寻找一致的热点地区。其次，我们使用预测月份前一段时间内的犯罪数量的状态依赖性模型。该算法在Excel中实现，因此应用极其简单且完全透明。我们的预测模型在波特兰和辛辛那提的环境中都显示出高精度和高效率的热点预测。我们建议需要重新考虑以前开发的热点预测模型。