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.

[1]  Benjamin Engel.  Crime Mapping And Spatial Aspects Of Crime , 2016 .

[2]  Richard F. Gunst,et al.  Applied Regression Analysis , 1999, Technometrics.

[3]  Eric R. Ziegel,et al.  Generalized Linear Models , 2002, Technometrics.

[4]  George E. Tita,et al.  Making Space for Theory: The Challenges of Theorizing Space and Place for Spatial Analysis in Criminology , 2010 .

[5]  Shane D. Johnson,et al.  Examining the Relationship Between Road Structure and Burglary Risk Via Quantitative Network Analysis , 2015 .

[6]  Shane D. Johnson,et al.  Permeability and Burglary Risk: Are Cul-de-Sacs Safer? , 2010 .

[7]  Alasdair Turner,et al.  From Axial to Road-Centre Lines: A New Representation for Space Syntax and a New Model of Route Choice for Transport Network Analysis , 2007 .

[8]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[9]  Matteo Corsi,et al.  A Multi-indicator Approach for Smart Security Policy Making , 2015 .

[10]  J. Jacobs.  The Death and Life of Great American Cities , 1962 .

[11]  J. Besag,et al.  Bayesian image restoration, with two applications in spatial statistics , 1991 .

[12]  Stephen A Matthews,et al.  Built Environment and Property Crime in Seattle, 1998–2000: A Bayesian Analysis , 2010, Environment & planning A.

[13]  George E. Tita,et al.  Spatial Regression Models in Criminology: Modeling Social Processes in the Spatial Weights Matrix , 2010 .

[14]  H. Rue,et al.  Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations , 2009 .

[15]  Jane Law,et al.  Bayesian Spatio-Temporal Modeling for Analysing Local Patterns of Crime Over Time at the Small-Area Level , 2014 .

[16]  Steven F. Messner,et al.  IMMIGRATION AND THE RECENT VIOLENT CRIME DROP IN THE UNITED STATES: A POOLED, CROSS‐SECTIONAL TIME‐SERIES ANALYSIS OF METROPOLITAN AREAS* , 2009 .

[17]  John Eck,et al.  Crime Places in Crime Theory , 2015 .

[18]  Peter Green,et al.  Markov chain Monte Carlo in Practice , 1996 .

[19]  Michael R. Gottfredson,et al.  Age and the Explanation of Crime , 1983, American Journal of Sociology.

[20]  Roger Th. A. J. Leenders,et al.  Modeling social influence through network autocorrelation: constructing the weight matrix , 2002, Soc. Networks.

[21]  Robert J. Sampson,et al.  Socioeconomic Achievement in the Life Course of Disadvantaged Men: Military Service as a Turning Point, Circa 1940-1965 , 1996 .

[22]  Lawrence E. Cohen,et al.  Human ecology and crime: A routine activity approach , 1980 .

[23]  Alan Penn,et al.  Natural Movement: Or, Configuration and Attraction in Urban Pedestrian Movement , 1993 .

[24]  Jane Law,et al.  Exploring links between juvenile offenders and social disorganization at a large map scale: a Bayesian spatial modeling approach , 2013, J. Geogr. Syst..

[25]  Barak Ariel,et al.  Are Restorative Justice Conferences Effective in Reducing Repeat Offending? Findings from a Campbell Systematic Review , 2015 .

[26]  P. Brantingham,et al.  Criminality of place , 1995 .

[27]  O. Newman,et al.  Defensible Space; Crime Prevention Through Urban Design. , 1973 .

[28]  Felix Famoye,et al.  Zero-Inflated Generalized Poisson Regression Model with an Application to Domestic Violence Data , 2021, Journal of Data Science.

[29]  B. Hillier,et al.  The Social Logic of Space , 1984 .

[30]  Christophe Claramunt,et al.  Topological Analysis of Urban Street Networks , 2004 .

[31]  J. Wakefield,et al.  Bayesian approaches to disease mapping , 2001 .

[32]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[33]  Robert Brame,et al.  MULTIPLE ROUTES TO DELINQUENCY? A TEST OF DEVELOPMENTAL AND GENERAL THEORIES OF CRIME , 1997 .

[34]  Nick Dalton,et al.  Fractional Configurational Analysis And a solution to the Manhattan problem , 2001 .

[35]  L. Anselin.  Spatial Econometrics: Methods and Models , 1988 .

[36]  Robert J. Sampson,et al.  Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence , 2001 .

[37]  Teresa C. Lagrange,et al.  The Impact of Neighborhoods, Schools, and Malls on the Spatial Distribution of Property Damage , 1999 .

[38]  R. Clarke.  Situational Crime Prevention: Its Theoretical Basis and Practical Scope , 1983 .

[39]  Bill Hillier,et al.  Network effects and psychological effects: a theory of urban movement , 2005 .

[40]  V. Latora,et al.  Centrality measures in spatial networks of urban streets. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[41]  Alex R. Piquero,et al.  How Well Do Criminologists Explain Crime? Statistical Modeling in Published Studies , 2008, Crime and Justice.

[42]  Ralph B. Taylor.  Social Order and Disorder of Street Blocks and Neighborhoods: Ecology, Microecology, and the Systemic Model of Social Disorganization , 1997 .

[43]  David Banister,et al.  Configurational Modelling of Urban Movement Networks , 1998 .

[44]  Robert J. Sampson,et al.  Neighborhood and Crime: The Structural Determinants of Personal Victimization , 1985 .

[45]  Richard Block,et al.  WHERE OFFENDERS CHOOSE TO ATTACK: A DISCRETE CHOICE MODEL OF ROBBERIES IN CHICAGO* , 2009 .

[46]  D. Osgood.  Poisson-Based Regression Analysis of Aggregate Crime Rates , 2000 .

[47]  Alberto Sillitti,et al.  Smart Security: Integrated Systems for Security Policies in Urban Environments , 2014 .

[48]  J A F Teklenburg,et al.  Space Syntax: Standardised Integration Measures and Some Simulations , 1993 .

[49]  W. Bernasco,et al.  Go where the money is: modeling street robbers’ location choices , 2013 .