Not again : repeat and near-repeat burglary in a mid-size city

The Impact of “Strike Hard” on Repeat and Near-Repeat Residential Burglary in Beijing

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9030150 ◽

2020 ◽

Vol 9 (3) ◽

pp. 150 ◽

Cited By ~ 2

Author(s):

Peng Chen ◽

Justin Kurland

Keyword(s):

Law Enforcement ◽

New Technique ◽

Residential Burglary ◽

Spatiotemporal Clustering ◽

A New Technique ◽

Repeat Burglary ◽

Near Repeat ◽

The Impact ◽

Repeat Pattern ◽

Nominal Evidence

“Strike Hard” is an enhanced law-enforcement strategy in China that aims to suppress crime, but measurement of the crime-reducing effect and potential changes in the spatiotemporal concentration of crime associated with “Strike Hard” remain unknown. This paper seeks to examine the impact, if any, of “Strike Hard” on the spatiotemporal clustering of burglary incidents. Two and half years of residential burglary incidents from Chaoyang, Beijing are used to examine repeat and near-repeat burglary incidents before, during, and after the “Strike Hard” intervention and a new technique that enables the comparison of repeat and near repeat patterns across different temporal periods is introduced to achieve this. The results demonstrate the intervention disrupted the repeat pattern during the “Strike Hard” period reducing the observed ratio of single-day repeat burglaries by 155%; however, these same single-day repeat burglary events increased by 41% after the cessation of the intervention. Findings with respect to near repeats are less remarkable with nominal evidence to support that the intervention produced a significant decrease, but coupled with other results, suggest that spatiotemporal displacement may have been an undesired by-product of “Strike Hard”. This study from a non-Western setting provides further evidence of the generalizability of findings related to repeat and near repeat patterns of burglary and further highlights the limited preventative effect that the “Strike Hard” enhanced law enforcement campaign had on burglary.

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Quantifying Crime Prevention Potential of Near-Repeat Burglary

Police Quarterly ◽

10.1177/1098611119828052 ◽

2019 ◽

Vol 22 (3) ◽

pp. 330-359 ◽

Cited By ~ 3

Author(s):

Elizabeth Groff ◽

Travis Taniguchi

Keyword(s):

Crime Prevention ◽

Prevention Potential ◽

Repeat Burglary ◽

Near Repeat

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Measuring and Modeling Repeat and Near-Repeat Burglary Effects

Journal of Quantitative Criminology ◽

10.1007/s10940-009-9068-8 ◽

2009 ◽

Vol 25 (3) ◽

pp. 325-339 ◽

Cited By ~ 110

Author(s):

M. B. Short ◽

M. R. D’Orsogna ◽

P. J. Brantingham ◽

G. E. Tita

Keyword(s):

Repeat Burglary ◽

Near Repeat

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Near-repeat burglary patterns in Malmö: Stability and change over time

European Journal of Criminology ◽

10.1177/1477370817751382 ◽

2018 ◽

Vol 16 (1) ◽

pp. 3-17 ◽

Cited By ~ 9

Author(s):

Laura Hoppe ◽

Manne Gerell

Keyword(s):

Time Frame ◽

Repeat Victimization ◽

Full Dataset ◽

Local Police ◽

Complete Dataset ◽

Close Proximity ◽

Private And Public ◽

Repeat Burglary ◽

Near Repeat ◽

Individual Dataset

It is well established that previous crime events are valuable indicators for the prediction of future crime. Near-repeat burglaries are incidents that occur in close proximity in space and time to an initial burglary. The current study analyses near-repeat victimization patterns in Malmö, Sweden’s third-largest city. The data, provided by the local police, cover a six-year time frame from 2009 to 2014. The complete dataset, as well as each year’s individual dataset, was analysed using Ratcliffe’s Near Repeat Calculator version 1.3. Results reveal significant near-repeat victimization patterns. For the full dataset, an observed/expected ratio of 2.83 was identified for the first week after an initial incident and an area of 100 metres surrounding the original burglary. Separate analyses of each individual year reveal both similarities and differences between years. Some years manifest near-repeat patterns at longer spatial and temporal distances, indicating a need for further studies on the variability of near repeats. Preventive strategies that include both private and public actors need to be intensified and focused on the first two weeks after a burglary.

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The Near-Repeat Burglary Phenomenon

Crime Mapping Case Studies ◽

10.1002/9780470987193.ch15 ◽

2008 ◽

pp. 121-132

Author(s):

Derek Johnson

Keyword(s):

Repeat Burglary ◽

Near Repeat

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Repeat Burglary Victimisation: Spatial and Temporal Patterns

Australian & New Zealand Journal of Criminology ◽

10.1177/000486580003300104 ◽

2000 ◽

Vol 33 (1) ◽

pp. 37-63 ◽

Cited By ~ 75

Author(s):

Michael Townsley ◽

Ross Homel ◽

Janet Chaseling

Keyword(s):

Hot Spots ◽

Moving Average ◽

Spatial And Temporal Patterns ◽

Repeat Victimisation ◽

Small Areas ◽

Calls For Service ◽

High Crime ◽

The Uk ◽

Repeat Burglary ◽

Near Repeat

To date there has been little Australian research on repeat victimisation. This is a study of repeat burglary in an area of Brisbane using police calls for service data. We demonstrate: (a) the prevalence of residential repeat victim addresses (‘hot dot’) is of a similar magnitude to that found in studies in the United Kingdom; (b) the time distributions of revictimisation are identical with those found in studies in the UK and elsewhere; (c) ‘hot spots’ (small areas with high crime density) can be identified by statistical analyses of spatial concentrations of incidents; (d) unstable hot spots tend to be temporary aggregations of hot dots, whereas stable hot spots seem to reflect more the social and physical characteristics of certain localities; and (e) the overall incidence of burglary could be reduced by at least 25 per cent if all repeat victimisation could be eliminated. There are a number of areas where concepts and techniques for repeat victim research could potentially be strengthened: (a) clarifying the connections between hot dots and hot spots, particularly through exploration of the concept of a ‘near repeat address’; (b) applying survival analysis to the data on the time periods between victimisations; and (c) using moving average techniques to examine changes in the spatial distributions of burglary over time.

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