Poisson-Based Regression Analysis of Aggregate Crime Rates
Author: Osgood D.W.1
Source: Journal of Quantitative Criminology, Volume 16, Number 1, March 2000 , pp. 21-43(23)
Publisher: Springer
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Abstract:
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 ill-suited to least-squares 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 arrest 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.
Keywords: Poisson; negative binomial; crime rates; aggregate analysis
Language: English
Document Type: Regular paper
Affiliations: 1: Crime, Law and Justice Program, Department of Sociology, 201 Oswald Tower, Pennsylvania State University, University Park, Pennsylvania 16802 wosgood@psu.edu
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