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 analysed here. Hence, we advise caution when using crime rates per capita to rank cities and recommend evaluating the linear plausibility before doing so.
翻译:几乎在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,其使用依赖于一个强烈的线性假设,即犯罪增加的速度与一个区域的人口数量相同。在本文中,我们表明,使用人均犯罪率对城市排名进行排名可以产生与根据人口规模进行调整的排名有很大差异的排名。我们分析了12个国家城市的人口与犯罪之间的关系,并根据犯罪类型评估了人均犯罪测量对犯罪分析的影响。在大多数国家,我们发现盗窃随着人口规模的超线性增加,而入室盗窃则线性增加。我们的结果显示,人均排名可能不同于人口调整排名,因此,在此处分析的数据中,在十大最危险的城市中,约有半数城市的排名不同。因此,我们建议谨慎使用人均犯罪率对城市排名,并建议在这样做之前对线性概率进行评估。