1. IntroductionThis <strong>report</strong> represents a summary of a project concerned with short-term burglary prediction.The work was completed <strong>in</strong> three stages, a short <strong>in</strong>itial research phase to develop andestablish the accuracy of the method, a short development phase dur<strong>in</strong>g which the systemwas ref<strong>in</strong>ed for use <strong>in</strong> one police Basic Command Unit (BCU) with tactical implicationsidentified and discussed, and f<strong>in</strong>ally a six-month field trial to test whether the system could beused <strong>in</strong> an <strong>operational</strong> <strong>context</strong> and how the police engaged with it.The structure of this <strong>report</strong> largely follows the chronology of the research, thereby provid<strong>in</strong>gthe reader with an impression of substance and sequence. The first section is a review of theliterature that <strong>in</strong>formed the project, and the second the empirical research, <strong>in</strong>itial developmentand test<strong>in</strong>g of the system. The third section conta<strong>in</strong>s a discussion of the types of tacticaloption that were orig<strong>in</strong>ally identified as hav<strong>in</strong>g the potential to be used with the system, or atailored variant of it, and the wider ideology of the approach. The fourth section discussesf<strong>in</strong>al ref<strong>in</strong>ements to the system used <strong>in</strong> the field trial, and then reviews how the system waseventually used and police officers’ perceptions of its utility. Lessons learned regard<strong>in</strong>gimplementation are also highlighted and discussed. In the penultimate section, changes <strong>in</strong>patterns of <strong>crime</strong> co<strong>in</strong>cident with the work are explored, and <strong>in</strong> the f<strong>in</strong>al section futuredirections and recommendations are discussed.Background and past researchCrim<strong>in</strong>ological research has demonstrated that <strong>crime</strong> is concentrated. For all <strong>crime</strong> typesanalysed, a small number of victims are repeatedly victimised and hence experience a largeproportion of <strong>crime</strong> (for reviews, see Pease, 1998; Farrell, 2005); a large proportion of <strong>crime</strong>occurs <strong>in</strong> a small number of areas; and a small number of offenders commit a large proportionof <strong>crime</strong> (e.g. Spelman, 1994). In relation to the geographical distribution of <strong>crime</strong>, thismanifests itself as spatial cluster<strong>in</strong>g, with ‘hotspots’ of <strong>crime</strong> such as burglary be<strong>in</strong>g a typicalcharacteristic of deprived areas (e.g. Johnson et al., 1997). These f<strong>in</strong>d<strong>in</strong>gs conform to what ismore generally known as the 80:20 rule. This pattern is not conf<strong>in</strong>ed to <strong>crime</strong> but is a moregeneral phenomenon. For <strong>in</strong>stance, a small proportion of the earth’s surface holds themajority of life on the planet, and a small proportion of earthquakes account for the majority ofdamage caused by them (Clarke and Eck, 2003).For burglary, the focus of the current research, the relationship between different types ofconcentration has also been studied. Specifically, are <strong>in</strong>cidents of repeat burglaryvictimisation the work of a common offender, or do different offenders simply exploit the sameopportunities for <strong>crime</strong>? These explanations have been referred to with<strong>in</strong> the literature as theboost and flag hypotheses, respectively (Pease, 1998). A number of approaches to<strong>in</strong>vestigat<strong>in</strong>g these hypotheses exist, but perhaps the most direct is to exam<strong>in</strong>e data fordetected offences. In their analysis of a sample of data for offenders detected for burglaryoffences, Everson and Pease (2001) demonstrate that 86 per cent of the <strong>in</strong>cidents of repeatvictimisation were committed by the same offenders (see also Everson, 2003). Furthercorroborative evidence comes from studies <strong>in</strong> which offenders have been <strong>in</strong>terviewedregard<strong>in</strong>g their offend<strong>in</strong>g behaviour. Typical f<strong>in</strong>d<strong>in</strong>gs illustrate that around one <strong>in</strong> threeburglars admit to return<strong>in</strong>g to the same property to commit a further offence (Gill andMathews, 1994; Ashton et al. 1998) and their reasons for so do<strong>in</strong>g <strong>in</strong>clude the follow<strong>in</strong>g:“the house was associated with low risk …., they were familiar with the features of thehouse …., to get th<strong>in</strong>gs left beh<strong>in</strong>d or replaced goods.”Ericsson, 1995Perhaps the most succ<strong>in</strong>ct account was given by a Scottish burglar to Mandy Shaw. Uponasked why he returned, he replied “Big house, small van”. Thus, whilst recognis<strong>in</strong>g that somerepeat offences may be committed by unrelated offenders, a consensus of op<strong>in</strong>ion isemerg<strong>in</strong>g that repeat victimisation is largely the work of the same offenders. A further f<strong>in</strong>d<strong>in</strong>gthat supports this conclusion and which has immediate <strong>crime</strong> prevention implications is the1
time course of repeat victimisation (RV). Research consistently demonstrates that when RVoccurs it does so swiftly offer<strong>in</strong>g a limited but precise w<strong>in</strong>dow of opportunity for <strong>in</strong>tervention(e.g. Polvi et al., 1991). Risk is unstable. Thus, repeat victimisation may be said to be aspecial case of space-time cluster<strong>in</strong>g, events tend<strong>in</strong>g to occur swiftly at the same locations.Inspired by the precepts of optimal forag<strong>in</strong>g theory, the authors have recently exam<strong>in</strong>edwhether RV is part of a more general forag<strong>in</strong>g pattern (Johnson and Bowers, 2004a). Thetheory, borrowed from behavioural ecology, is that when search<strong>in</strong>g for resources, offenderswill aim to limit the time spent search<strong>in</strong>g for suitable targets, whilst simultaneously seek<strong>in</strong>g tomaximise the rewards acquired thereby m<strong>in</strong>imis<strong>in</strong>g the associated risks. RV is arguably anexample of optimal forag<strong>in</strong>g. A conjecture from Farrell et al. (1995) illustrates this. Farrell etal. suggest that:“a burglar walk<strong>in</strong>g down a street where he has never burgled before sees two k<strong>in</strong>ds ofhouse – those presumed suitable and those presumed unsuitable. (The latteridentified by d<strong>in</strong>t of an alarm, by occupancy, the presence of a bark<strong>in</strong>g dog, and soon). He burgles one of the houses he presumes suitable, and he is successful. Nexttime he walks down the street, he sees three k<strong>in</strong>ds of house – the presumedunsuitable, the presumed suitable, and the known suitable. It would <strong>in</strong>volve the leasteffort to burgle the house known to be suitable.”Farrell et al. (1995)Thus, offenders target those properties with which they are most familiar, and which comb<strong>in</strong>egood rewards and acceptable risks. A natural extension of this strategy would be to target notonly those previously burgled and known to be suitable but also those houses that are mostsimilar to them, <strong>in</strong> terms of the likely risks and rewards and the effort <strong>in</strong>volved <strong>in</strong> burgl<strong>in</strong>gthem. The first law of geography states that th<strong>in</strong>gs which are closest to each other <strong>in</strong> spaceare the most similar. It follows that homes nearest to burgled houses may represent the nextbesttargets. For this reason, us<strong>in</strong>g data for the county of Merseyside and methodsdeveloped <strong>in</strong> the field of epidemiology (Knox, 1964), the authors conducted a series of studiesto exam<strong>in</strong>e whether the risk of burglary clusters <strong>in</strong> space and time more generally. That is,does the risk of burglary appear to be communicated from one property to another <strong>in</strong> muchthe same way as the behaviour of a disease? 1A series of confirmatory f<strong>in</strong>d<strong>in</strong>gs followed. In particular, for the area studied, the researchdemonstrated that the risk of burglary was communicated over a distance of about 400m andthis elevated risk endured for around one month (Johnson and Bowers, 2004a), after which itappeared to move to other nearby areas (Johnson and Bowers, 2004b). Additionally, adisproportionate <strong>in</strong>crease <strong>in</strong> risk for those on the same side of the street as the burgled homewas evident. The communicability of risk varied by area, with risk appear<strong>in</strong>g to be mostcommunicable <strong>in</strong> the most affluent of areas (Bowers and Johnson, 2005a), though somedegree of communicability was well-nigh universal.The practical implications of this programme of research are clear: <strong>crime</strong> reductive actionshould be directed towards the burgled home, and also to those nearby. However, oneconcern raised was the practicability of implement<strong>in</strong>g such a strategy on a large scale.Consider that the implementation of a strategy for which every burgled household andneighbours with<strong>in</strong> 400m received <strong>crime</strong> reduction attention would require substantialresources if implemented across an area such as a police Basic Command Unit. For obviousreasons, such a strategy is unlikely to generate much enthusiasm.What is required is a more precise method of generat<strong>in</strong>g reliably accurate predictions ofwhere <strong>crime</strong> will most likely next occur. Such a method should enable the efficientdeployment of resources. The rough location of a high concentration of <strong>crime</strong> could easily bepredicted by simply identify<strong>in</strong>g a large urban area, but this would be of little <strong>operational</strong> value.The challenge, then, is to identify where a high concentration of <strong>crime</strong> will occur for arelatively small area. Consider<strong>in</strong>g the f<strong>in</strong>d<strong>in</strong>gs <strong>in</strong> relation to <strong>crime</strong> concentration, for an1 The authors do not suggest that burglary exudes a bacillus but that the cluster<strong>in</strong>g of events <strong>in</strong> space and time mightsuggest that it does so.2
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- Page 4 and 5: ContentsAcknowledgementsExecutive s
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- Page 8 and 9: Project outcomesPatterns of burglar
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- Page 18 and 19: Table 2.2: Knox ratios for Mansfiel
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- Page 41 and 42: Selecting a pilot siteThe decision
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- Page 45 and 46: Type ofinterventionStudyUse ofintel
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Tactical deliveryCommand Team daily
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Table 5.3: Number of respondents wh
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permitted, up to four plain clothed
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observation made by those who used
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A simple time-series analysis (see
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Two approaches were used to compute
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Figure 6.3: Changes in the proporti
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Figure 6.5: Changes in the proporti
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With respect to implementation real
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ReferencesAggresti, A. (1996) An In
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Johnson, S.D., Summers, L., and Pea
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Appendix 1. The information technol
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Figure A1.2: Stand-alone applicatio
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Recommendations that may be realise
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Section 1: knowledge and understand
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Extra Comments (please outline any
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In relation to the evaluation of in
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Time-series analysisFor the purpose
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Figure A3.1: Changes in the spatial
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Figure A3.2: Lorenz curves showing
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To recapitulate and elaborate, the
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Concluding comments on methodThe te
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Figure A5.2: An enlargement of the
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Figure A5.6: Prospective map magnif
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