The spatial configuration of urban crime environments and statistical modeling

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.