Spatial Regression Models in Criminology: Modeling Social Processes in the Spatial Weights Matrix

  • George E. Tita
  • Steven M. Radil


A decade ago, Jacqueline Cohen and George Tita served as guest editors for a special volume of the Journal of Quantitative Criminology (Vol 15, #4, 1999) that was dedicated to the study of the diffusion of homicide. In their Editor’s Introduction (Cohen and Tita 1999a), they concluded that the results presented in special volume,1 along with recent work by Morenoff and Sampson (1997), clearly demonstrated that the observed patterns of violence were consistent with patterns one might expect if violence does, in fact, diffuse over space. That is, levels of violence are not randomly distributed; instead, similar rates of violence cluster together in space (i.e., violence exhibits positive spatial autocorrelation.) Furthermore, a growing number of studies began to demonstrate that even after controlling for the ecological features known to be associated with high levels of crime (e.g., poverty, population density, male joblessness, female-headed households, etc), the clustering of high values could not be explained away. These early spatial studies of diffusion helped to establish the existence of an unobserved “neighborhood effect” that seemed to be responsible for spatially concentrated high-crime areas.


Spatial Autocorrelation Spatial Dependence Homicide Rate Spatial Weight Matrix Spatial Error Model 
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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • George E. Tita
    • 1
  • Steven M. Radil
    • 2
  1. 1.Department of Criminology, Law and SocietyUniversity of California, IrvineIrvineUSA
  2. 2.Department of GeographyUniversity of Illinois at Urbana-ChampaignChampaignUSA

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