Objectives

Crime counts are sensitive to granularity choice. There is an increasing interest in analyzing crime at very fine granularities, such as street segments, with one of the reasons being that coarse granularities mask hot spots of crime. However, if granularities are too fine, counts may become unstable and unrepresentative. In this paper, we develop a method for determining a granularity that provides a compromise between these two criteria.

Methods

Our method starts by estimating internal uniformity and robustness to error for different granularities, then deciding on the granularity offering the best balance between the two. Internal uniformity is measured as the proportion of areal units that pass a test of complete spatial randomness for their internal crime distribution. Robustness to error is measured based on the average of the estimated coefficient of variation for each crime count.

Results

Our method was tested for burglaries, robberies and homicides in the city of Belo Horizonte, Brazil. Estimated “optimal” granularities were coarser than street segments but finer than neighborhoods. The proportion of units concentrating 50% of all crime was between 11% and 23%.

Conclusions

By balancing internal uniformity and robustness to error, our method is capable of producing more reliable crime maps. Our methodology shows that finer is not necessarily better in the micro-analysis of crime, and that units coarser than street segments might be better for this type of study. Finally, the observed crime clustering in our study was less intense than the expected from the law of crime concentration.

中文翻译：

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太好了不能成为好人？空间犯罪分析中的粒度，均匀性和错误问题

目标

犯罪计数对粒度选择很敏感。人们对以非常精细的粒度（例如街道段）分析犯罪的兴趣日益浓厚，原因之一是粗粒度掩盖了犯罪的热点。但是，如果粒度太细，计数可能会变得不稳定且无法代表。在本文中，我们开发了一种确定粒度的方法，该粒度在这两个条件之间提供了折衷方案。

方法

我们的方法首先估算不同粒度的内部一致性和对错误的鲁棒性，然后确定粒度在两者之间提供最佳平衡。内部统一性是指通过内部犯罪分布的完全空间随机性测试的单位面积所占的比例。基于每个犯罪计数的估计变异系数的平均值来衡量错误的鲁棒性。

结果

我们的方法已经在巴西贝洛奥里藏特市进行了盗窃，抢劫和杀人罪的测试。估计的“最佳”粒度比街道段粗，但比邻域细。集中所有犯罪的50％的单位所占比例在11％和23％之间。

结论

通过平衡内部一致性和对错误的鲁棒性，我们的方法能够生成更可靠的犯罪图。我们的方法论表明，在犯罪的微观分析中，更好的方法不一定更好，对于这种类型的研究，比街道小得多的单位可能更好。最后，在我们的研究中观察到的犯罪聚类强度没有犯罪集中定律所预期的强。