New Insights Into the Spatial and Temporal Distribution of Repeat Victimization

Johnson, Stephen D.; Bowers, Kate; Hirschfield, Alex

doi:10.1093/oxfordjournals.bjc.a014156

Cited by 160 publications

(145 citation statements)

References 4 publications

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“…Beyond housing type, previous research indicates that households located in the most disadvantaged areas tend to experience higher rates of repeat burglary victimiza tion ( Trickett et al , 1992 ;Johnson et al , 1997 ;Ratcliffe and McCullagh, 1999 ). In this vein, Bowers et al (2005) also explore whether the infl uence of housing type on revictimization risk is mediated by the area in which properties are located.…”

Section: Previous Research On Repeat Burglary Victimizationmentioning

confidence: 99%

“…The British Crime Survey estimates that around 40 per cent of domestic burglaries are repeat offences ( Budd, 1999 ). Using police recorded crime data the proportion of repeat offences is reported to range from 13 per cent in Merseyside, England ( Johnson et al , 1997 ) to 32 per cent in Beenleigh, Australia ( Townsley et al , 2000 ).…”

Section: Previous Research On Repeat Burglary Victimizationmentioning

confidence: 99%

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Repeat burglary victimization in Malawi and the influence of housing type and area-level affluence

Sidebottom

2011

Secur J

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There is little research on repeat victimization in developing countries. This article reports the fi rst study of repeat domestic burglary victimization in Malawi. Secondary analysis of data from a large multipurpose survey fi nds burglary to be highly concentrated. The infl uence of housing type -measured here as the building materials used -and area-level affl uence is explored. Homes constructed of permanent materials, indicative of greater wealth, experienced higher rates of repeat victimization than those made of mud and thatch. This pattern is most pronounced in less affl uent areas, although the variation in risk is less than that reported in comparable studies. Implications for preventing repeat burglaries in resource-limited settings are discussed.

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Section: Previous Research On Repeat Burglary Victimizationmentioning

confidence: 99%

Section: Previous Research On Repeat Burglary Victimizationmentioning

confidence: 99%

Repeat burglary victimization in Malawi and the influence of housing type and area-level affluence

Sidebottom

2011

Secur J

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“…For example, a burglar may discover an abundance of items that could be targeted in a subsequent burglary, or they may simply prefer to return to a location where they know that their entry methods are guaranteed to work again, amongst other possible reasons (Farrell et al 1995). In addition, it has been suggested that this elevated risk may spread to neighboring homes as well (Johnson et al 1997;Townsley et al 2003;Sagovsky and Johnson 2007), especially in areas where nearby homes are similar in layout and type of inhabitant. Notice, however, that in the case of event dependence, burglary risk is not persistent throughout time, but may change as the burglar's preferences, skills, and exposure to other opportunities change (Farrell et al 1995).…”

mentioning

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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“…Since such risk increases with each subsequent crime, a small proportion of targets progress to become chronically victimized supertargets. This offers an account of why repeat victimization occurs disproportionately in high crime areas, as found elsewhere (Trickett et al 1992;Johnson, Bowers and Hirschfield 1997).…”

Section: Mechanism-based Theorymentioning

confidence: 69%

Crime concentration theory

Farrell

2015

Crime Prev Community Saf

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New Insights Into the Spatial and Temporal Distribution of Repeat Victimization

Cited by 160 publications

References 4 publications

Repeat burglary victimization in Malawi and the influence of housing type and area-level affluence

Repeat burglary victimization in Malawi and the influence of housing type and area-level affluence

Measuring and Modeling Repeat and Near-Repeat Burglary Effects

Crime concentration theory

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