Geographical information systems have found successful applications to prediction and decision-making in several areas of vital importance to contemporary society. This article demonstrates how they can be combined with machine learning algorithms to create crime prediction models for urban areas. Selected point of interest (POI) layers from OpenStreetMap are used to derive attributes describing micro-areas, which are assigned crime risk classes based on police crime records. POI attributes then serve as input attributes for learning crime risk prediction models with classification learning algorithms. The experimental results obtained for four UK urban areas suggest that POI attributes have high predictive utility. Classification models using these attributes, without any form of location identification, exhibit good predictive performance when applied to new, previously unseen micro-areas. This makes them capable of crime risk prediction for newly developed or dynamically changing neighborhoods. The high dimensionality of the model input space can be considerably reduced without predictive performance loss by attribute selection or principal component analysis. Models trained on data from one area achieve a good level of prediction quality when applied to another area, which makes it possible to transfer or combine crime risk prediction models across different urban areas.

中文翻译：

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基于兴趣点数据的城市犯罪风险预测

地理信息系统已经成功地应用于对当代社会至关重要的几个领域的预测和决策。本文演示了如何将它们与机器学习算法结合以创建城市地区的犯罪预测模型。OpenStreetMap中的选定兴趣点（POI）层用于导出描述微区域的属性，这些属性根据警察犯罪记录分配了犯罪风险类别。然后，POI属性用作输入属性，用于使用分类学习算法学习犯罪风险预测模型。在英国四个城市地区获得的实验结果表明，POI属性具有较高的预测效用。使用这些属性的分类模型在没有任何形式的位置标识的情况下，当应用于新的，以前看不见的微区域时，表现出良好的预测性能。这使他们能够对新近发展或动态变化的社区进行犯罪风险预测。可以通过属性选择或主成分分析来显着减少模型输入空间的高维度，而不会造成预期的性能损失。