Objectives

Police-recorded crimes are used by police forces to document community differences in crime and design spatially targeted strategies. Nevertheless, crimes known to police are affected by selection biases driven by underreporting. This paper presents a simulation study to analyze if crime statistics aggregated at small spatial scales are affected by larger bias than maps produced for larger geographies.

Methods

Based on parameters obtained from the UK Census, we simulate a synthetic population consistent with the characteristics of Manchester. Then, based on parameters derived from the Crime Survey for England and Wales, we simulate crimes suffered by individuals, and their likelihood to be known to police. This allows comparing the difference between all crimes and police-recorded incidents at different scales.

Results

Measures of dispersion of the relative difference between all crimes and police-recorded crimes are larger when incidents are aggregated to small geographies. The percentage of crimes unknown to police varies widely across small areas, underestimating crime in certain places while overestimating it in others.

Conclusions

Micro-level crime analysis is affected by a larger risk of bias than crimes aggregated at larger scales. These results raise awareness about an important shortcoming of micro-level mapping, and further efforts are needed to improve crime estimates.

中文翻译：

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犯罪统计数据的准确性：评估警察数据偏向对地理犯罪分析的影响

目标

警察使用警察记录的犯罪来记录社区在犯罪方面的差异并设计针对空间的策略。但是，警方已知的犯罪受到举报不当所致的选择偏见的影响。本文提出了一项仿真研究，以分析在较小空间尺度上汇总的犯罪统计数据是否比针对较大地理区域生成的地图受到更大的偏差影响。

方法

基于从英国人口普查获得的参数，我们模拟了符合曼彻斯特特征的合成种群。然后，基于从英格兰和威尔士犯罪调查得出的参数，我们模拟个人遭受的犯罪及其被警察知晓的可能性。这样可以比较所有罪行和警察记录的事件在不同规模上的区别。

结果

当事件汇总到较小的地区时，分散所有犯罪和警察记录的犯罪之间的相对差异的措施就更大。警察未知的犯罪比例在小范围内差异很大，在某些地方低估了犯罪，而在其他地方则高估了。

结论

微观犯罪分析受到的偏见风险要大于大规模犯罪所带来的偏见风险。这些结果提高了人们对微观映射的重要缺陷的认识，需要进一步努力来改进犯罪估计。