Neighborhood Effects and Spatial Patterns in Urban Crime: A Case Study of the City of Dallas, Texas
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The effects of neighborhood structure on urban crime have been investigated widely. Much prior literature has examined the association between socio-economic variables which can reflect critical neighborhood structure and crime rates in the context of regression analysis. However, the spatial dynamics of urban crime have not been accurately considered in a traditional urban crime analysis. This paper argues that it is an important issue to take into account spatial aspects of crime data because the lack of consideration for spatial aspects may result in the bias of parameter estimation and inefficient standard errors for the parameters in a regression analysis. This paper examines the spatial patterns of two different types of urban crime (violent and property crime) which occurred in the City of Dallas, Texas in 2002. Then, neighborhood effects on those crime rates are investigated through three different regression models which are OLS, SAR, and CAR models.
Abstract
The effects of neighborhood structure on urban crime have been investigated widely. Much prior literature has examined the association between socio-economic variables which can reflect critical neighborhood structure and crime rates in the context of regression analysis. However, the spatial dynamics of urban crime have not been accurately considered in a traditional urban crime analysis. This paper argues that it is an important issue to take into account spatial aspects of crime data because the lack of consideration for spatial aspects may result in the bias of parameter estimation and inefficient standard errors for the parameters in a regression analysis. This paper examines the spatial patterns of two different types of urban crime (violent and property crime) which occurred in the City of Dallas, Texas in 2002. Then, neighborhood effects on those crime rates are investigated through three different regression models which are OLS, SAR, and CAR models.
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