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The spatial configuration of urban crime environments and statistical modeling

Author

Listed:
  • Enrico di Bella
  • Matteo Corsi
  • Lucia Leporatti
  • Luca Persico

Abstract

The aim of this paper is to discuss the representation of space in statistical models of urban crime. We argue that some important information represented by the properties of space is either lost or hardly interpretable if those properties are not explicitly introduced in the model as regressors. We illustrate the issue commenting on the shortcomings of the two standard approaches to modeling the dispersion of crime in a city: using local attributes of places as regressors, and defining a catch-all spatial component to neutralize the effect of latent spatial factors from the model. As an alternative to the current methods, the metrics of spatial configuration, including those devised by the technique called Space Syntax Analysis, provide useful variables that can be introduced as regressors. Such regressors offer interpretable information on space, behavior, and their interactions, that would otherwise be lost. We therefore consider a set of three configurational variables that represent different forms of centrality and that are thought to have influence on a wide range of human activities. We propose an innovative procedure to adapt these variables to most urban graphs and then, using data from a large area in the city of Genoa (Italy), we show that the three variables are well defined, consistent, noncollinear indicators, with evident spatial meanings. Then we build two sets of Hierarchical Bayesian count models of different urban crime types (“property crime†and “arson and criminal damage†) around some known covariates of crime and we show that the overall quality of the models is improved (with the size of improvement depending on the type of crime) when the three configurational variables are included. Furthermore, we show that what the three variables explain of the overall variability of crime is a sizeable part of what would be the spatial error term of a traditional spatial model of urban crime. While the configurational variables alone cannot provide a goodness of fit as high as the one obtained with a generic spatial term, they have a relevant role for the interpretation of the results, which is ultimately the objective of urban crime modeling.

Suggested Citation

  • Enrico di Bella & Matteo Corsi & Lucia Leporatti & Luca Persico, 2017. "The spatial configuration of urban crime environments and statistical modeling," Environment and Planning B, , vol. 44(4), pages 647-667, July.
    • Handle: RePEc:sae:envirb:v:44:y:2017:i:4:p:647-667
    DOI: 10.1177/0265813515624686
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    References listed on IDEAS

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    1. Enrico di Bella & Matteo Corsi & Lucia Leporatti, 2015. "A Multi-indicator Approach for Smart Security Policy Making," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 122(3), pages 653-675, July.
    2. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    3. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    4. Enrico Bella & Francesca Odone & Matteo Corsi & Alberto Sillitti & Ruth Breu, 2014. "Smart Security: Integrated Systems for Security Policies in Urban Environments," Progress in IS, in: Renata Paola Dameri & Camille Rosenthal-Sabroux (ed.), Smart City, edition 127, pages 193-219, Springer.
    5. Wim Bernasco & Richard Block & Stijn Ruiter, 2013. "Go where the money is: modeling street robbers' location choices," Journal of Economic Geography, Oxford University Press, vol. 13(1), pages 119-143, January.
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    Cited by:

    1. Yicheng Tang & Xinyan Zhu & Wei Guo & Xinyue Ye & Tao Hu & Yaxin Fan & Faming Zhang, 2017. "Non-Homogeneous Diffusion of Residential Crime in Urban China," Sustainability, MDPI, vol. 9(6), pages 1-17, June.
    2. Shino Shiode & Narushige Shiode, 2022. "Network-Based Space-Time Scan Statistics for Detecting Micro-Scale Hotspots," Sustainability, MDPI, vol. 14(24), pages 1-20, December.

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