Crime rates per capita are used virtually everywhere to rank and compare cities. However, their usage relies on a strong linear assumption that crime increases at the same pace as the number of people in a region. In this paper, we demonstrate that using per capita rates to rank cities can produce substantially different rankings from rankings adjusted for population size. We analyze the population–crime relationship in cities across 12 countries and assess the impact of per capita measurements on crime analyses, depending on the type of offense. In most countries, we find that theft increases superlinearly with population size, whereas burglary increases linearly. Our results reveal that per capita rankings can differ from population-adjusted rankings such that they disagree in approximately half of the top 10 most dangerous cities in the data analyzed here. Hence, we advise caution when using crime rates per capita to rank cities and recommend evaluating the linear plausibility before doing so.

人均犯罪率几乎无处不在用于对城市进行排名和比较。然而，它们的使用依赖于一个强大的线性假设，即犯罪与一个地区的人口数量以相同的速度增长。在本文中，我们证明使用人均比率对城市进行排名可以产生与根据人口规模调整的排名截然不同的排名。我们分析了 12 个国家/地区城市的人口与犯罪关系，并根据犯罪类型评估了人均测量值对犯罪分析的影响。在大多数国家，我们发现盗窃随着人口规模呈超线性增长，而入室盗窃则呈线性增长。我们的结果表明，人均排名可能与人口调整后的排名不同，因此在此处分析的数据中排名前 10 位的最危险城市中，大约有一半的人不同意。因此，我们建议在使用人均犯罪率对城市进行排名时要谨慎，并建议在这样做之前评估线性合理性。