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Poisson-Based Regression Analysis of Aggregate Crime Rates

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Abstract

This article introduces the use of regression models based on the Poissondistribution as a tool for resolving common problems in analyzing aggregatecrime rates. When the population size of an aggregate unit is small relativeto the offense rate, crime rates must be computed from a small number ofoffenses. Such data are ill-suited to least-squares analysis. Poisson-basedregression models of counts of offenses are preferable because they arebuilt on assumptions about error distributions that are consistent withthe nature of event counts. A simple elaboration transforms the Poissonmodel of offense counts to a model of per capita offense rates. Todemonstrate the use and advantages of this method, this article presentsanalyses of juvenile arrest rates for robbery in 264 nonmetropolitancounties in four states. The negative binomial variant of Poisson regressioneffectively resolved difficulties that arise in ordinary least-squaresanalyses.

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Authors and Affiliations

  1. Crime, Law and Justice Program, Department of Sociology, Pennsylvania State University, 201 Oswald Tower, University Park, Pennsylvania, 16802

    D. Wayne Osgood

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  1. D. Wayne Osgood
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Osgood, D.W. Poisson-Based Regression Analysis of Aggregate Crime Rates. Journal of Quantitative Criminology 16, 21–43 (2000). https://doi.org/10.1023/A:1007521427059

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