Objectives:

In his 2014 Sutherland address to the American Society of Criminology, David Weisburd demonstrated that the share of crime that is accounted for by the most crime-ridden street segments is notably high and strikingly similar across cities, an empirical regularity referred to as the “law of crime concentration.” In the large literature that has since proliferated, there remains considerable debate as to how crime concentration should be measured empirically. We suggest a measure of crime concentration that is simple, accurate and easily interpreted.

Methods:

Using data from three of the largest cities in the United States, we compare observed crime concentration to a counterfactual distribution of crimes generated by randomizing crimes to street segments. We show that this method avoids a key pitfall that causes a popular method of measuring crime concentration to considerably overstate the degree of crime concentration in a city.

Results:

While crime is significantly concentrated in a statistical sense and while some crimes are substantively concentrated among hot spots, the precise relationship is considerably weaker than has been documented in the empirical literature.

Conclusions:

The method we propose is simple and easily interpretable and compliments recent advances which use the Gini coefficient to measure crime concentration.

中文翻译：

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衡量边际犯罪集中度：旧问题的新解决方案

目标：

大卫·魏斯伯德（David Weisburd）在2014年萨瑟兰（Sutherland）对美国犯罪学协会的讲话中证明，犯罪最多的街道地区所占犯罪比例显着较高，并且在各城市之间惊人地相似，这种经验规律被称为“法律犯罪集中。” 此后大量文献中，关于应如何以经验方式衡量犯罪集中度的争论仍然很多。我们建议一种简单，准确且易于解释的犯罪集中度度量。

方法：

使用来自美国三个最大城市的数据，我们将观察到的犯罪集中度与通过将犯罪随机分配到路段产生的犯罪事实分布进行了比较。我们表明，这种方法避免了一个关键陷阱，该陷阱导致一种流行的犯罪集中度测量方法大大夸大了城市中犯罪集中度。

结果：

虽然犯罪在统计意义上非常集中，而某些犯罪基本上集中在热点地区，但确切的关系比经验文献中记载的要弱得多。

结论：

我们提出的方法简单易懂，并补充了使用基尼系数来衡量犯罪集中度的最新进展。