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Examining how structural characteristics and the physical environment simultaneously impact crime in neighborhoods: Using a semi-parametric strategy

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  • Kim, Young-An
  • Hipp, John R.

Abstract

This study examines the associations between various social and physical environmental characteristics and their interrelated influence on neighborhood crime. Using Kernel Regularized Least Squares (KRLS), we estimate the marginal effects of each independent variable at each datapoint by providing pointwise estimates of partial derivatives. Then we regress the derivative values for each independent variable on each other variable in the model to examine whether these derivative estimates (marginal effects) vary by other variables in the model. We found that the effects of the physical environment on different types of crime in neighborhoods vary by different levels of social structural characteristics. We simultaneously assess how the two different types of neighborhood environments can work together in a semiparametric way, theoretically integrate both social disorganization and criminal opportunity perspectives, and thus provide a more comprehensive as well as nuanced explanation of neighborhood crime.

Suggested Citation

  • Kim, Young-An & Hipp, John R., 2025. "Examining how structural characteristics and the physical environment simultaneously impact crime in neighborhoods: Using a semi-parametric strategy," Journal of Criminal Justice, Elsevier, vol. 99(C).
    • Handle: RePEc:eee:jcjust:v:99:y:2025:i:c:s004723522500131x
    DOI: 10.1016/j.jcrimjus.2025.102482
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    References listed on IDEAS

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    1. Wouter Steenbeek & Veronique Schutjens, 2014. "The willingness to intervene in problematic neighbourhood situations: A comparison of local entrepreneurs and (un-)employed residents," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 105(3), pages 349-357, July.
    2. Joscha Legewie, 2018. "Living on the Edge: Neighborhood Boundaries and the Spatial Dynamics of Violent Crime," Demography, Springer;Population Association of America (PAA), vol. 55(5), pages 1957-1977, October.
    3. Hainmueller, Jens & Hazlett, Chad, 2014. "Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach," Political Analysis, Cambridge University Press, vol. 22(2), pages 143-168, April.
    4. Ferwerda, Jeremy & Hainmueller, Jens & Hazlett, Chad J., 2017. "Kernel-Based Regularized Least Squares in R (KRLS) and Stata (krls)," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i03).
    5. Kim, Young-An & Hipp, John R., 2021. "Density, diversity, and design: Three measures of the built environment and the spatial patterns of crime in street segments," Journal of Criminal Justice, Elsevier, vol. 77(C).
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