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Built Environment and Property Crime in Seattle, 1998–2000: A Bayesian Analysis

Author

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  • Stephen A Matthews

    (Department of Sociology, Pennsylvania State University, University Park, PA 16802, USA)

  • Tse-Chuan Yang

    (Social Science Research Institute, Pennsylvania State University, University Park, PA 16802, USA)

  • Karen L Hayslett

    (School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, TX 75080, USA)

  • R Barry Ruback

    (Crime, Law and Justice Program, Department of Sociology, Pennsylvania State University, University Park, PA 16802, USA)

Abstract

The past decade has seen a rapid growth in the use of a spatial perspective in studies of crime. In part this growth has been driven by the availability of georeferenced data, and the tools to analyze and visualize them: geographic information systems, spatial analysis, and spatial statistics. In this paper we use exploratory spatial data analysis (ESDA) tools and Bayesian models to help better understand the spatial patterning and predictors of property crime in Seattle, Washington for 1998–2000, including a focus on built environment variables. We present results for aggregate property crime data as well as models for specific property crime types: residential burglary, nonresidential burglary, theft, auto theft, and arson. ESDA confirms the presence of spatial clustering of property crime and we seek to explain these patterns using spatial Poisson models implemented in WinBUGS. Our results indicate that built environment variables were significant predictors of property crime, especially the presence of a highway on auto theft and burglary.

Suggested Citation

  • Stephen A Matthews & Tse-Chuan Yang & Karen L Hayslett & R Barry Ruback, 2010. "Built Environment and Property Crime in Seattle, 1998–2000: A Bayesian Analysis," Environment and Planning A, , vol. 42(6), pages 1403-1420, June.
    • Handle: RePEc:sae:envira:v:42:y:2010:i:6:p:1403-1420
    DOI: 10.1068/a42393
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    References listed on IDEAS

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    1. Walsh, Jeffrey A. & Taylor, Ralph B., 2007. "Community structural predictors of spatially aggregated motor vehicle theft rates: Do they replicate?," Journal of Criminal Justice, Elsevier, vol. 35(3), pages 297-311.
    2. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    3. Ole F. Christensen & Rasmus Waagepetersen, 2002. "Bayesian Prediction of Spatial Count Data Using Generalized Linear Mixed Models," Biometrics, The International Biometric Society, vol. 58(2), pages 280-286, June.
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