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.

几乎所有地方都使用人均犯罪率来对城市进行排名和比较。然而，它们的使用依赖于一个强有力的线性假设，即犯罪率的增长速度与地区人口数量的增长速度相同。在本文中，我们证明，使用人均比率对城市进行排名可能会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 个国家城市的人口与犯罪关系，并根据犯罪类型评估人均测量对犯罪分析的影响。在大多数国家，我们发现盗窃行为随人口规模呈超线性增长，而入室盗窃则呈线性增长。我们的结果显示，人均排名可能与人口调整排名不同，因此在此处分析的数据中，前 10 个最危险城市中大约有一半的人均排名不一致。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，并建议在此之前评估线性合理性。