Elsevier

Applied Geography

Volume 60, June 2015, Pages 120-129
Applied Geography

Learning where to offend: Effects of past on future burglary locations

https://doi.org/10.1016/j.apgeog.2015.03.014Get rights and content

Highlights

  • We analyze 3337 detected burglaries in the UK.

  • Burglars are more likely to offend in areas they have targeted before.

  • The elevated risk decays over time.

  • The elevated risk decays over space.

Abstract

Informed by a growing literature on space-time patterns of repeat and near repeat burglary victimization, a crime location choice model was used to test whether burglars are attracted to areas they previously targeted. Using data in 3337 detected burglaries from one UK police force, and accounting for the distance to the offender's residence, and for other factors that make target areas attractive to burglars, it was demonstrated that burglars were more likely to commit a burglary in an area they had targeted before. This was particularly the case if the prior burglary was (very) recent. Areas near to those in which burglaries had been committed were also more likely to be selected.

Introduction

Human behavior in space is remarkably regular and predictable. For example, Song, Qu, Blumm, and Barabasi (2010) showed that knowing a person's recent locations greatly helps to predict their future whereabouts. If such spatial consistency also applies to burglars, locations of their past offenses should inform predictions of future ones. There is pending evidence that this is indeed the case.

More than two decades of research shows that past burglary victimization best predicts future burglary victimization risk (Pease, 1998). This repeat victimization risk peaks immediately after the initial burglary, and usually returns to a baseline level within a few months (Polvi, Looman, Humphries, & Pease, 1991). In most repeat burglary victimizations the same offenders perpetrate both the initial burglary and the follow-up, particularly if the period between both events is short (Everson & Pease, 2001). In other words, repeat burglary victimization typically involves burglars returning to their prior targets.

More recent research shows a phenomenon labeled as ‘near repeat’ (Bowers and Johnson, 2004, Townsley et al., 2003), ‘communicability of risk’ (Johnson & Bowers, 2004a), ‘self-exiting point process’ (Mohler, Short, Brantingham, Schoenberg, & Tita, 2011) and ‘spatio–temporal interaction’ (Grubesic & Mack, 2008). In the wake of a residential burglary, the victimization risk is temporarily increased not only for the initially burgled property, but for nearby homes too. The risk decays both in space and time, typically extending up to a few hundred meters, and persisting for a month or two (Johnson et al., 2007, Ratcliffe and McCullagh, 1998). As with repeat victimization, the same offenders are often involved in both initial and ‘near repeat’ burglaries (Bernasco, 2008, Johnson et al., 2009). Apparently, burglars tend to return to their prior target areas. Furthermore, research on the behavioral consistency of serial offenders (aimed to improve crime linkage techniques in criminal investigations) has shown that the two most consistent and distinctive aspects of the modus operandi of serial burglars are where and when they offend (Markson et al., 2010, Tonkin et al., 2012, Tonkin et al., 2011).

Simultaneously, a formal model of burglary location choice has been developed and has been tested in empirical studies (Bernasco, 2006, Bernasco, 2010a, Bernasco, 2010b, Bernasco and Nieuwbeerta, 2005, Clare et al., 2009, Townsley et al., 2013). The main question that these studies address is how —based on which criteria— offenders decide where to commit a burglary. Applying random utility maximization theory (McFadden, 2001) and discrete choice models (Ben-Akiva & Lerman, 1985), the effects are tested of variables that signal —from an offender perspective—benefits, costs and risks of potential burglary locations.

None of these burglary location choice studies, however, has explored how a burglar's previous burglaries might affect where he strikes next.1 Instead, most studies analyzed the burglaries as isolated events that were unconnected to the past criminal endeavors and experiences of the offenders involved. This might be a serious omission, because the (near) repeat victimization studies cited above suggest that burglars often return to prior targets and target areas to commit subsequent burglaries. Common sense, learning theories, and perspectives from behavioral ecology such as optimal foraging theory (see, Johnson, 2014) suggest that offenders should learn from their criminal experiences. For example, one would expect burglars to return to target areas where they have been successful. One would also expect them to avoid alternatives where they failed in the past —where they did not achieve to enter the property, could not steal any valuable items, or where they were sighted or arrested.

Research on (near) repeat victimization has included theoretical and empirical (e.g., Bernasco, 2008; Johnson, 2014; Johnson et al., 2009) work intended to explain why offenders return to previously victimized locations, but a broader theoretical framework that connects the two lines of enquiry has remained underdeveloped. For example, if the same offenders are responsible for the initial and the subsequent burglary, what made them choose the target in the first place? Under what circumstances will they not return? What other criteria guide their choices? In other words, what is missing from the cited literature is an explicit theoretical model of (repeated) burglary location choice.

A recent study in the Netherlands (Lammers, Menting, Ruiter, & Bernasco, 2015) addressed this issue, using a sample of detected crimes (of any type) and the offenders involved. The areas where offenders had committed prior offenses were included in a model of their subsequent crime location choices. The findings showed that offenders tend to return to previous target areas, in particular to those of recent crimes and especially if the previous crimes were of the same crime type.

The study reported here aims to further explore questions of repeat crime location choices and differs in three key aspects from the study by Lammers et al. (2015). First, it used data from another country (United Kingdom). Second, it used a smaller spatial unit of analysis. Whereas Lammers et al. (2015) used areas with an average population of 7000 residents, the areas used here have an average population of only 1,500, potentially providing for more homogenous spatial units and enhanced ecological validity. Third, and most importantly, whereas Lammers et al. (2015) studied all crime types combined, the present study focused on burglary only. This focus allows for more specificity in the literature review, the theory development, the selection of variables, and in the interpretation of empirical findings.

In sum, we develop explicit hypotheses on how the time and location of past burglaries affect a burglar's subsequent decisions of where to commit burglaries, and integrate the theory that motivates these into a more general model of burglary location choice. Using police-recorded burglary data we rigorously test three hypotheses using discrete spatial choice modeling techniques.

The remainder of the paper is structured as follows. Drawing on the criminological literature on burglary, Section 2 reviews existing theory on how burglars decide where to offend, and develops new theory about how previous burglary locations influence that decision. Section 3 addresses data and method, Section 4 describes the findings, and Section 5 reflects on the results and suggests avenues for future research.

Section snippets

Where to commit burglary

How do offenders decide where to commit a burglary, and how do times, places and other characteristics of their prior burglaries enter into that decision? We start by reviewing the criminological literature, which applies rational choice theory to this topic. We subsequently discuss how crime pattern theory suggests that burglars are most likely to offend within their awareness space, which includes places they have visited in the recent past. Finally, we provide arguments why they are likely

Data and methods

Data were acquired for all detected residential burglaries committed between January 2007 and December 2012 in the West Midlands, a large metropolitan UK police force (see Fig. 1). A residential burglary is the actual or attempted illegal entry into a dwelling with the intent to steal (Bernasco, 2014). A ‘detected’ burglary is one that has been reported to the police, and where at least one person has been charged with committing it. The following variables were available for analysis for each

Findings

The estimated model parameters (odds ratios, confidence intervals, and significance level indicators) are listed in Table A1 in the Appendix. For improved interpretation, this information is presented graphically in Fig. 2. Pseudo R2 values for the discrete choice model are expected to be much lower than those for ordinary least squares models. In this case, according to McFadden's guidance, the observed pseudo R2 of .31 represents an excellent fit to the data (McFadden, 1978: 307).

In Fig. 2,

Discussion

This paper addressed the question of whether having burgled in a particular area, a burglar is more likely to target the same area again, and if so, whether this effect depends on the recency of the prior burglaries and whether it spills over to nearby areas.

Inspired by crime pattern theory it was hypothesized that burglars are more likely to target an area if (1) they have burgled in the area before, (2) the prior burglary was more recently committed, and (3) the area is in the proximity of an

Acknowledgments

This research was supported under Australian Research Council's Discovery Projects funding scheme (project number DP110100100, Understanding How Criminals Decide Where and When to Offend) and under the Netherlands Organization for Scientific Research's Innovational Research Incentives Scheme Vidi (project number 452–12–004). The authors would like to thank West Midlands police, and in particular Chief Superintendent Alex Murray, for providing the data analyzed in this paper, and the Editor and

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