SOPHIA

The Iraqi Insurgency (2003–2011) has commonly been characterized as demonstrating the tendency for violence to cluster and diffuse at the local level. Recent research has demonstrated that insurgent attacks in Iraq cluster in time and space in a manner similar to that observed for the spread of a disease. The current study employs a variety of approaches common to the scientific study of criminal activities to advance our understanding of the correlates of observed patterns of the incidence and contagion of insurgent attacks. We hypothesize that the precise patterns will vary from one place to another, but that more attacks will occur in areas that are heavily populated, where coalition forces are active, and along road networks. To test these hypotheses, we use a fishnet to build a geographical model of Baghdad that disaggregates the city into more than 3000 grid cell locations. A number of logistic regression models with spatial and temporal lags are employed to explore patterns of local escalation and diffusion. These models demonstrate the validity of arguments under each of three models but suggest, overall, that risk heterogeneity arguments provide the most compelling and consistent account of the location of insurgency. In particular, the results demonstrate that violence is most likely at locations with greater population levels, higher density of roads, and military garrisons

'Location, Location, Location' : effects of neighborhood and house attributes on Burglars’ target selection

Objectives To empirically test whether offenders consider environmental features at multiple spatial scales when selecting a target and examine the simultaneous effect of neighborhood-level and residence-level attributes on residential burglars' choice of residence to burglarize. Methods We combine data on 679 burglaries by 577 burglars committed between 2005 and 2014 with data on approximately 138,000 residences in 193 residential neighborhoods in Ghent, Belgium. Using a discrete spatial choice approach, we estimate the combined effect of neighborhood-level and residence-level attributes on burglars' target choice in a conditional logit model. Results Burglars prefer burglarizing residences in neighborhoods with lower residential density. Burglars also favor burglarizing detached residences, residences in single-unit buildings, and renter-occupied residences. Furthermore, burglars are more likely to target residences in neighborhoods that they previously and recently targeted for burglary, and residences nearby their home. We find significant cross-level interactions between neighborhood and residence attributes in burglary target selection. Conclusions Both area-level and target-level attributes are found to affect burglars' target choices. Our results offer support for theoretical accounts of burglary target selection that characterize it as being informed both by attributes of individual properties and attributes of the environment as well as combinations thereof. This spatial decision-making model implies that environmental information at multiple and increasingly finer scales of spatial resolution informs crime site selection

Design, crime and the built environment

Crime Prevention through Environmental Design (CPTED) is a method of reducing crime through the design and manipulation of the built environment. Based upon the Opportunity Theories of crime, CPTED focuses upon blocking opportunities for criminal behaviour through subtle techniques to maximise informal surveillance, guardianship and maintenance, to minimise through movement and to set standards of physical security that are proportionate to crime risk. This chapter will discuss the principles of CPTED and the theories from which it evolved. It will explore the effectiveness of these principles, both individually and combined, in reducing crime, before exploring how CPTED is applied in practice

An epidemiological study of burglary offenders: trends and predictors of self-reported arrests for burglary in the United States, 2002-2013

Burglary is serious property crime with a relatively high incidence and has been shown to be variously associated with other forms of criminal behavior. Unfortunately, an epidemiological understanding of burglary and its correlates is largely missing from the literature. Using public-use data collected between 2002 and 2013 as part of the National Survey on Drug Use and Health (NSDUH), the current study compared those who self-reported burglary arrest in the prior 12 months with and without criminal history. The unadjusted prevalence estimates of self-reported burglary arrest were statistically different for those with a prior arrest history (4.7%) compared with those without an arrest history (0.02%) which is a 235-fold difference. Those with an arrest history were more likely to report lower educational attainment, to have lower income, to have moved more than 3 times in the past 5 years, and to use alcohol, tobacco, illicit drugs, and engage in binge drinking. Moreover, those with prior arrest histories were younger and more likely to be male. There is considerable heterogeneity among burglars with criminal history indicating substantially greater behavioral risk

(Looking) Back to the Future: using space-time patterns to better predict the location of street crime

Crime analysts attempt to identify regularities in police recorded crime data with a central view of disrupting the patterns found. One common method for doing so is hotspot mapping, focusing attention on spatial clustering as a route to crime reduction (Chainey & Ratcliffe, 2005; Clarke & Eck, 2003). Despite the widespread use of this analytical technique, evaluation tools to assess its ability to accurately predict spatial patterns have only recently become available to practitioners (Chainey, Tompson, & Uhlig, 2008). Crucially, none has examined this issue from a spatio-temporal standpoint. Given that the organisational nature of policing agencies is shift based, it is common-sensical to understand crime problems at this temporal sensitivity, so there is an opportunity for resources to be deployed swiftly in a manner that optimises prevention and detection. This paper tests whether hotspot forecasts can be enhanced when time-of-day information is incorporated into the analysis. Using street crime data, and employing an evaluative tool called the Predictive Accuracy Index (PAI), we found that the predictive accuracy can be enhanced for particular temporal shifts, and this is primarily influenced by the degree of spatial clustering present. Interestingly, when hotspots shrank (in comparison with the all-day hotspots), they became more concentrated, and subsequently more predictable. This is meaningful in practice; for if crime is more predictable during specific timeframes, then response resources can be used intelligently to reduce victimisation

Examining the Relationship Between Road Structure and Burglary Risk Via Quantitative Network Analysis

OBJECTIVES: To test the hypothesis that the spatial distribution of residential burglary is shaped by the configuration of the street network, as predicted by, for example, crime pattern theory. In particular, the study examines whether burglary risk is higher on street segments with higher usage potential. METHODS: Residential burglary data for Birmingham (UK) are examined at the street segment level using a hierarchical linear model. Estimates of the usage of street segments are derived from the graph theoretical metric of betweenness, which measures how frequently segments feature in the shortest paths (those most likely to be used) through the network. Several variants of betweenness are considered. The geometry of street segments is also incorporated—via a measure of their linearity—as are several socio-demographic factors. RESULTS: As anticipated by theory, the measure of betweenness was found to be a highly-significant predictor of the burglary victimization count at the street segment level for all but one of the variants considered. The non-significant result was found for the most localized measure of betweenness considered. More linear streets were generally found to be at lower risk of victimization. CONCLUSIONS: Betweenness offers a more granular and objective means of measuring the street network than categorical classifications previously used, and its meaning links more directly to theory. The results provide support for crime pattern theory, suggesting a higher risk of burglary for streets with more potential usage. The apparent negative effect of linearity suggests the need for further research into the visual component of target choice, and the role of guardianship

Understanding the deterrent effect of police patrol

The fact that crime clusters spatially has been known since at least the early 19th century. However, understanding of the extent and nature of this clustering at different areal units, and the fact that crime also clusters at different temporal scales is relatively new. Where previously the most at-risk areas (or `hot-spots') of crime were defined over areas the size of city districts and for periods of months if not years, the last decade has seen the focus shift to micro-places - areas of only a few hundred metres across - which are only `hot' for days or even hours. The notion that visible police presence in crime hot-spots can deter crime is not new and has been the basis of police patrols for two centuries. This deterrent effect has been well evidenced in many previous studies, both by academics and police practitioners. However, evaluations of these more recent micro-level hot-spot patrol strategies face significant analytic challenges and data quality concerns. They also often assume levels of police activity at the micro-area level (an `intention-to-treat' design) rather than measuring it directly. The aim of this thesis is to investigate the accuracy and precision of data that can be used to evaluate micro-level hot-spot patrol strategies and the implications this has for any analysis conducted using such data at these micro-level geographies. This thesis begins by outlining the relevant literature regarding place-based policing strategies and the current understanding of how crime clusters in both space and time. It continues by highlighting the data challenges associated with evaluating micro-level police interventions through the use of an illustrative analytic strategy before using a self-exciting point process model to evaluate the effects of police foot patrol in micro-level hot-spot under the assumption that the crime and patrol data being used are accurate. This is followed by two chapters which investigate the quality of the two datasets. Finally, the point-process evaluation is re-conducted using simulated data that takes account of the uncertainty of the datasets to demonstrate how data quality issues effect the result of such an evaluation and ultimately, the perceived efficacy of these highly-focussed policing strategies