Space–Time Patterns of Risk: A Cross National Assessment of Residential Burglary Victimization

Johnson, Shane D.; Bernasco, Wim; Bowers, Kate; Elffers, Henk; Ratcliffe, Jerry H.; Rengert, George F.; Townsley, Michael

doi:10.1007/s10940-007-9025-3

Cited by 297 publications

(297 citation statements)

References 38 publications

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“…Johnson et al 2007), whereby crime is seen to cluster in both space and time. The activity patterns on which much of the preceding theory is based will, of course, vary temporally, and therefore the effect of the network might be expected to differ by time of day.…”

Section: Discussionmentioning

confidence: 99%

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

2014

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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 highlysignificant 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.Conclusion 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.

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Section: Discussionmentioning

confidence: 99%

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

2014

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“…It is easy therefore to map a burglary strategy or set of cognitive scripts from one house to another (Wright and Decker 1994). Moreover, the expectation is that the risk of near-repeat burglary will spread more readily where housing types and the everyday routines of residents are spatially more homogeneous (Townsley et al 2003;Johnson et al 2007).…”

Section: Discussionmentioning

confidence: 99%

Measuring and Modeling Repeat and Near-Repeat Burglary Effects

Short¹,

D’Orsogna²,

et al. 2009

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We develop a mathematical framework aimed at analyzing repeat and nearrepeat effects in crime data. Parsing burglary data from Long Beach, CA according to different counting methods, we determine the probability distribution functions for the time interval s between repeat offenses. We then compare these observed distributions to theoretically derived distributions in which the repeat effects are due solely to persistent risk heterogeneity. We find that risk heterogeneity alone cannot explain the observed distributions, while a form of event dependence (boosts) can. Using this information, we model repeat victimization as a series of random events, the likelihood of which changes each time an offense occurs. We are able to estimate typical time scales for repeat burglary events in Long Beach by fitting our data to this model. Computer simulations of this model using these observed parameters agree with the empirical data.

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“…The two parameters should be set sufficiently large that there is no persistent boost effect at longer temporal or spatial distances. Throughout this study, we take Δt max = 120 days and Δd max = 500 metres, which are both greater than empirically-obtained estimates in dense urban locations (Johnson et al 2007).…”

Section: The Self-exciting Point Processmentioning

confidence: 99%

Improving the Robustness and Accuracy of Crime Prediction with the Self-Exciting Point Process Through Isotropic Triggering

Rosser

Cheng

2016

Appl. Spatial Analysis

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The self-exciting point process (SEPP) is a model of the spread of crime in space and time, incorporating background and triggering processes. It shows promising predictive performance and forms the basis of a popular commercial software package, however few detailed case studies describing the application of the SEPP to crime data exist in the scientific literature. Using open crime data from the City of Chicago, USA, we apply the SEPP to crime prediction of assaults and burglaries in nine distinct geographical regions of the city. The results indicate that the algorithm is not robust to certain features of the data, generating unrealistic triggering functions in various cases. A simulation study is used to demonstrate that this outcome is associated with a reduction in predictive accuracy. Analysing the second-order spatial properties of the data demonstrates that the failures in the algorithm are correlated with anisotropy. A modified version of the SEPP model is developed in which triggering is non-directional. We show that this provides improved robustness, both in terms of the triggering structure and the predictive accuracy.

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Space–Time Patterns of Risk: A Cross National Assessment of Residential Burglary Victimization

Cited by 297 publications

References 38 publications

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

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

Measuring and Modeling Repeat and Near-Repeat Burglary Effects

Improving the Robustness and Accuracy of Crime Prediction with the Self-Exciting Point Process Through Isotropic Triggering

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