The main objective of this study is to test and compare the prediction performance of three of the most common predictive policing methods. A near-repeat model, a supervised machine learning model, and a risk terrain model are tested and compared against each other using retrospective analysis of home burglary crime data from a Belgian city. Hotspot analysis is included as a baseline. Predictions are made for three different months (January, May and September 2017) to account for seasonal differences. Variations in spatial context (city center vs. suburbs) and the number of predicted risk locations are also tested. Prediction performance is measured using accuracy, near-hit rate, precision and F1-score. The results show that there are some notable differences in prediction performance between the model types across the tested variations. In general, the ensemble model tends to be the most consistent high performer across all tested variations. Also notable is that hotspot analysis is not clearly outperformed by the other methods. The different methods have their own strengths and weaknesses and optimal prediction performance crucially depends on the specific location context. More comparative analyses of predictive policing methods in different contexts are needed to gain a more complete picture. Future research could also focus on how combining methods can help improve crime prediction performance.

中文翻译：

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近重复，机器学习和风险地形建模用于犯罪时空预测的比较

这项研究的主要目的是测试和比较三种最常见的预测性警务方法的预测性能。使用来自比利时城市的家庭入室犯罪数据的回顾性分析，测试并比较了近重复模型，监督式机器学习模型和风险地形模型。热点分析作为基线包括在内。对三个不同月份（2017年1月，5月和9月）进行了预测，以说明季节差异。还测试了空间背景（市中心与郊区）的变化以及预测的风险位置的数量。预测性能是使用准确性，准命中率，准确性和F1得分来衡量的。结果表明，在经过测试的变体之间，模型类型之间的预测性能存在显着差异。通常，在所有测试的变体中，集成模型往往是最一致的高性能。同样值得注意的是，热点分析并没有明显优于其他方法。不同的方法各有优缺点，最佳预测性能关键取决于特定的位置上下文。为了获得更完整的图景，需要在不同情况下对预测性治安方法进行更多的比较分析。未来的研究也可能关注于结合方法如何帮助改善犯罪预测性能。不同的方法各有优缺点，最佳预测性能关键取决于特定的位置上下文。为了获得更完整的图景，需要在不同情况下对预测性治安方法进行更多的比较分析。未来的研究也可能关注于结合方法如何帮助改善犯罪预测性能。不同的方法各有优缺点，最佳预测性能关键取决于特定的位置上下文。为了获得更完整的图景，需要在不同情况下对预测性治安方法进行更多的比较分析。未来的研究也可能关注于结合方法如何帮助改善犯罪预测性能。