The spatial configuration of urban crime environments and statistical modeling

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.

Land Use Influencing the Spatial Distribution of Urban Crime: A Case Study of Szczecin, Poland

“…This interesting approach provides a new perspective on incidents like predatory crime, arson, or criminal damage, occurring in the open space. The very specific street pattern of the downtown area of Szczecin consisting of many star-shaped places and streets that are often not parallel to each other, may be a good study area for verifying the results obtained by Bella di et al [42,43].…”

“…The results from the POSAC model seem to have the most advantages regarding the requirements of public administration. In another study, Bella di et al [43] develop a set of statistical measures called Median Line Angular Segment Analysis (MLASA) based on the configuration concept of space proposed by Hillier [29]. This interesting approach provides a new perspective on incidents like predatory crime, arson, or criminal damage, occurring in the open space.…”

Time-varying relationships between land use and crime: A spatio-temporal analysis of small-area seasonal property crime trends

“…Eight sociodemographic variables were tested to account for neighbourhood disadvantage: residential population, five-year residential mobility, percent of immigrant residents, index of ethnic heterogeneity, percent of lone-parent families, percent of low-income families, median income, and percent young adult population (Table 2) (Craglia et al, 2005;Law and Quick, 2013). Residential population was analyzed as an explanatory variable because property crime may be concentrated in small-areas with mostly non-residential land use (i.e., residential population is not a representative population at risk) and because residential population is a proxy for the number of potential offenders in the routine activity theory (di Bella et al, 2015).…”

Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

“…Another limitation of the study is that we did not account for the role of the spatial configuration in explaining crime risks. Recent research has shown that spatial configuration can be a substantial factor in creating crime risks and in revealing the sources of spatial autocorrelation in crime data [71].…”