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Repeat burglary victimisation: a tale of two theories

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Abstract

Research consistently demonstrates that crime is spatially concentrated. Considering repeat burglary, studies conducted across a variety of countries and for different periods of time demonstrate that events also cluster in time. Two theories have been proposed to explain patterns of repeat victimisation. The first suggests that repeat victimisation is the consequence of a contagion-like process. If a home has been burgled on one occasion, the risk to the home is boosted, most likely because offenders will return to exploit good opportunities further (e.g. to steal replaced items or those left behind). In contrast, the second suggests that repeat victimisation may be explained by time-stable variation in risk across homes and a chance process. Different offenders independently target attractive locations for which risk is flagged. Understanding the contribution of the two explanations is important for both criminological understanding and crime reduction. Hitherto, research concerned with repeat victimisation has adopted a top-down methodology, analysing either victimisation or offender data. In this paper, results are reported for a simple micro-simulation experiment used to examine patterns of victimisation under conditions where the contributions of both theoretical mechanisms are varied. The findings suggest that increasing the heterogeneity of target attractiveness can generate spatial concentrations of crime not dissimilar to those discussed above, but that a contagion-like process is (also) required to generate the time course of repeat victimisation. The implications of the findings are discussed.

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Notes

  1. This is not to suggest that burglary exudes a bacillus, but that patterns of victimisation might suggest that it does.

  2. Lauritsen and Davis-Quinet (1995) also examined this, using longitudinal survey data collected as part of the US National Youth Survey. Although they did not consider burglary, they did examine patterns of victimisation for larceny, which included theft of or from cars and bicycles. There results were consistent with those of Wittebrood and Nieuwbeerta (2000).

  3. To examine this, Tseloni and Pease (2004) analysed data concerned with crimes against the person collected for the 1994 US National Crime Victim Survey. Their results suggested that both explained and unexplained heterogeneity contribute substantially to victimisation risk but do not alter the importance of event dependency. Although the data analysed did not include burglary, the findings are of clear theoretical relevance here.

  4. Where a series of events occur at a particular home on a chance basis, the average time elapsed between them will be inversely proportional to the number of events experienced.

  5. A range of other models was tested, but the results are excluded as they did not change the conclusions that follow. Results are available on request.

  6. In computing the area-level risks, a question that naturally arises relates to the interval of time used to generate the estimates. The decision taken was to use data for the entire period for which data were available. The reasons for this are as follows. First, if, as is suggested by risk heterogeneity theory, patterns of repeat victimisation are the consequence of time-stable variation in risk across homes, then the epoch sampled to estimate time-stable variation is unimportant. A series of sampling intervals should give the same or similar results. However, using only a sample of the data available could reduce the reliability of any estimates produced. Thus, whilst other possibilities are acknowledged, using data for the entire 4-year period seemed the most sensible approach.

  7. Exactly the same estimates were derived using the Poisson distribution.

  8. The mean values of the simulation model were greater than 1.96 standard deviations of the observed values.

  9. To ease interpretation, the risk to homes was initially scaled by a factor of 0.974, so that the overall volume of events generated by the boost models did not exceed that for the observed distribution.

  10. For this and all other models, results for models with other scaling factors are available upon request.

  11. Visual inspection of the results across all iterations generated the same conclusions.

  12. One way of examining the extent to which a model fits the data is to compute the sum of the squared errors (SSE), an index commonly used for evaluating time series models. To do this, for each data point, the value for the model is subtracted from that of the observed data, and the result is squared. The squared errors can then be summed to provide an indication of how well the model fits the data. The lower the value the better the model fits the data. In this case, the model that included only area-level heterogeneity had a lower SSE value (54,549) than the model with homogeneous risks (SSE = 75,337) or those that modelled area risks and differences in the number of security features (SSE = 111,098) or types of homes located in an area (SSE = 192,702).

  13. The SSE values for these models ranged from 6,725 (the temporal decay model with area level risks) to 29,223 (the random interval model with homogeneous risks).

  14. For example, in the study by Osborn and Tseloni (1998), the areas considered contained around 3,000 homes. Wittebrood and Nieuwbeerta (2000) included a variable to model urbanisation, which ranged from 0 to 4, but it is unclear how this variable was constructed and it is thus perhaps likely to have been a fairly coarse measure.

  15. A boost process could, of course, have a role to play in this context if the experience of victimisation shapes the victims subsequent behaviour in such a way that it exposes him or her to an elevated risk of victimisation.

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Acknowledgements

The author would like to thank the Ordnance Survey and Merseyside police for supplying the data used in this research. Thanks also go (in alphabetical order) to Wim Bernasco, Kate Bowers, Henk Elffers, John Eck, Ken Pease, Jerry Ratcliffe, Aiden Sidebottom, Mike Townsley and three anonymous reviewers for comments on an earlier version of this paper. This research was supported by a British Academy research grant (award number LRG-45507), an International Collaborative Network grant from the British Academy, and additional funding from UCL Futures, NSCR and the Research Incentive Fund at Temple University.

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  1. UCL Jill Dando Institute of Crime Science, University College London, Second Floor, Brook House, 2–16 Torrington Place, London, WC1E 7HN, UK

    Shane D. Johnson

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Correspondence to Shane D. Johnson.

Appendices

Appendix

Appendix 1 Data used

Recorded-crime data

Data for the crime of residential burglary were obtained for the 4-year period 1 April 1999 to 31 March 2003 for the administrative county of Merseyside (UK). For each event, the following fields of information were available: a unique crime reference number; the address of the offence, recorded in a free text format; the earliest and latest date on which the offence could have occurred; the geographical grid coordinate of the offence, accurate to a resolution of 1 m; and, the surname of the victim.

The data were comprehensively cleaned prior to analysis. Those for which the address was incomplete, or which represented duplicates of other records—identified by virtue of their having the same details—were excluded from analysis. Data stored as free text were also reformatted where necessary. For example, common abbreviations such as RD (instead of Road) or GR (instead of Grove) were replaced with full-text equivalents. After the data had been cleaned, there was a total of 50,691 burglaries available for analysis.

Ordnance survey address point data

To generate a simulated population of households, Ordnance Survey (OS) address point data for the administrative county of Merseyside were used. The data included the geographic coordinate of each residential address within the county, of which there were 590,856.

Census data

The census for England and Wales is conducted every 10 years by the Office for National Statistics (ONS). The census provides detailed information about all homes located within England and Wales, including the number of households, age of the population, and the types of homes owned. For the purposes of confidentiality, data are only available in an aggregated format. The level of aggregation at which data are available varies, and, in this study, data at the finest level of resolution, the output area (OA) geography, were used. There is a total of 175,000 OAs in England and Wales, and they each contain around 125 households. To generate the OA geography a procedure was used by ONS to ‘build’ the areas such that homes with similar characteristics were selected to form discrete OAs. The procedure thus aims to maximise homogeneity within OAs. For current purposes the variables of interest were the counts of housing per OA and the types of homes within them.

Appendix 2

Observed and mean number of homes burgled n times for the Flag models (SD standard deviation, d.p. decimal place, RV repeat victimisation)

Table 3
Full size table

Appendix 3

Observed and mean number of homes burgled n times for boost models with and without area-level variation in risk (SD standard deviation, d.p. decimal place, RV repeat victimisation)

Table 4
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Johnson, S.D. Repeat burglary victimisation: a tale of two theories. J Exp Criminol 4, 215–240 (2008). https://doi.org/10.1007/s11292-008-9055-3

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