Poisson-based regression analysis of aggregate crime rates

This article introduces the use of regression models based on the Poisson distribution as a tool for resolving common problems in analyzing aggregate crime rates. When the population size of:an aggregate unit is small relative to the offense rate, crime rates must be computed from a small number of offenses. Such data are iii-suited to least-squarer analysis. Poisson-based regression models of counts of offenses are preferable because they are built on assumptions about error distributions that are consistent with the nature of event counts. A simple elaboration transforms the Poisson model of offense counts to a model of per capita offense rates. To demonstrate the use and advantages of this method, this article presents analyses of juvenile an-est rates for robbery in 264 nonmetropolitan counties in four states. The negative binomial variant of Poisson regression effectively resolved difficulties that arise in ordinary least-squares analyses.