6. Changes <strong>in</strong> patterns of burglaryKey to robust evaluation is the notion of effect signatures. This concerns the pattern of resultswhich reflect mechanism – just as signatures bespeak identity – and equally important, whichfail to reflect cherished but erroneous ideas about mechanism. With respect to the currentproject, a number of signatures would be anticipated if reductions <strong>in</strong> <strong>crime</strong> were achieved as aconsequence of Promap- <strong>in</strong>fluenced <strong>in</strong>tervention. For example, reductions <strong>in</strong> <strong>crime</strong> would beexpected to co<strong>in</strong>cide with the tim<strong>in</strong>g and <strong>in</strong>tensity of implementation. However, changes <strong>in</strong>implementation <strong>in</strong>tensity can be measured <strong>in</strong> a number of ways. In a project such as this,where the deployment of police resources varies by time of day as well as day of the year,analyses should consider variation <strong>in</strong> <strong>crime</strong> for different <strong>in</strong>tervals of the day as well as byweek or month of year.With respect to changes <strong>in</strong> patterns of <strong>crime</strong> <strong>in</strong> space, particular a-priori expectations mayalso be expressed. For example, one would expect dist<strong>in</strong>ct changes <strong>in</strong> the spatialconcentration of burglary follow<strong>in</strong>g <strong>in</strong>tervention. In the extreme, if an <strong>in</strong>tervention had theeffect of prevent<strong>in</strong>g all burglaries subject to prediction (those that conform to an identifiedregularity), the spatial distribution of <strong>crime</strong> would appear random follow<strong>in</strong>g <strong>in</strong>tervention. Ofcourse, this scenario is unlikely but dist<strong>in</strong>ct changes <strong>in</strong> the spatial concentration of <strong>crime</strong>should be observed where an <strong>in</strong>tervention has an effect.F<strong>in</strong>ally, <strong>in</strong> addition to expect<strong>in</strong>g changes <strong>in</strong> the temporal and spatial distribution of <strong>crime</strong>,distortions <strong>in</strong> the space-time cluster<strong>in</strong>g of <strong>crime</strong> would be expected. To illustrate, considerthat spatial hotspots of <strong>crime</strong> are def<strong>in</strong>ed by a series of <strong>crime</strong>s that occur dur<strong>in</strong>g some<strong>in</strong>terval. The precise tim<strong>in</strong>g of the events is unimportant, with a spatial hotspot be<strong>in</strong>g def<strong>in</strong>edonly by virtue of a cluster<strong>in</strong>g of events <strong>in</strong> the spatial dimension. An alternative signature is aseries of <strong>crime</strong>s that cluster <strong>in</strong> both space and time; a localised spate. Where police<strong>in</strong>tervention is designed to anticipate such activity, as was the case here, one <strong>in</strong>dication ofsuccess would be a truncation <strong>in</strong> the duration of patterns that might suggest such activity.Consequently, a series of novel analytic techniques were developed to detect signatures ofthe type discussed. However, as is illustrated <strong>in</strong> earlier sections, implementation of the pilotwas <strong>in</strong>sufficient to facilitate an appropriate test of the potential <strong>crime</strong> reductive impact of thepredictive approach. As such, it is suggested that presentation of a sophisticated statisticalanalysis here would be unwise. Instead, <strong>in</strong> the sections that follow, simple analyses arepresented to exam<strong>in</strong>e the changes <strong>in</strong> the patterns of burglary observed before and dur<strong>in</strong>g thepilot project. The reader <strong>in</strong>terested <strong>in</strong> the more detailed analytic approaches used is referredto Appendix 3 for an illustration of the analyses and a more detailed discussion of theirrationale.The basic approach adopted <strong>in</strong> most evaluations is to compare the change <strong>in</strong> the volume of<strong>crime</strong> before and after <strong>in</strong>tervention <strong>in</strong> both an action and comparison area. If a reduction isobserved <strong>in</strong> the action but not comparator, or the reduction <strong>in</strong> the former exceeds that <strong>in</strong> thelatter then a positive <strong>in</strong>ference may be drawn. Figure 6.1 shows the change <strong>in</strong> the volume ofburglary <strong>in</strong> the action area, and a comparison area, Derbyshire ‘C’ Division. The latter wasselected partly because the trends <strong>in</strong> the two areas followed similar patterns prior to<strong>in</strong>tervention but also because discussions with the Command Team suggested that bothDivisions employed similar approaches to <strong>operational</strong> polic<strong>in</strong>g, not least because they werelocated with<strong>in</strong> the same police force, Derbyshire. It is evident from the time-series graph,which shows the patterns for two years prior to the pilot and thereafter, that there was areduction <strong>in</strong> burglary <strong>in</strong> both areas over time. Prior to <strong>in</strong>tervention, which of the two areas hadthe higher volume of burglary each month varied. For this period, the mean monthly count of<strong>crime</strong> was 112 (SD=31.6, N=24) <strong>in</strong> the pilot area, 116 (SD=38.2, N=24) <strong>in</strong> the comparator.59
A simple time-series analysis (see Appendix 3) confirmed that the two areas followed asimilar trend and experienced a similar volume of burglary for the two years before the pilot.At the start of the pilot (August-September) the volume of burglary rose <strong>in</strong> both areas, afterwhich it rema<strong>in</strong>ed somewhat stable <strong>in</strong> the comparison area but fell <strong>in</strong> the pilot area. For themonths of January and February 2006 the volume of burglary <strong>in</strong> the pilot area was the lowestit had been for at least the last five years, be<strong>in</strong>g less than half the volume for the equivalentperiod of time <strong>in</strong> the previous year. 7 For reference, also shown <strong>in</strong> Figure 6.1 are the times atwhich the major implementation outputs of the pilot began.At this po<strong>in</strong>t, it is perhaps useful to provide the reader with a little more <strong>context</strong>ual <strong>in</strong>formationregard<strong>in</strong>g other polic<strong>in</strong>g <strong>in</strong>itiatives implemented <strong>in</strong> ‘A’ Division before or around the time of thepilot. Interviews with the Command Team, LIOs and the analysts for the Division, suggestedthat the only <strong>in</strong>tervention implemented across the Division was the prioritisation of prolific andpriority offenders (PPOs). This began around February 2005 and is ongo<strong>in</strong>g. The aim of the<strong>in</strong>tervention is to target prolific offenders, those who commit the bulk of offences, across theentire BCU with the aim of detect<strong>in</strong>g and consequently reduc<strong>in</strong>g <strong>crime</strong>.Given the focus of this <strong>in</strong>tervention, it is plausible that this could have impacted upon the<strong>in</strong>cidence of burglary before and dur<strong>in</strong>g the pilot period. To see if any changes <strong>in</strong> burglaryoffences observed <strong>in</strong> ‘A’ Division were likely to be attributed to this strategy, the number ofdetections recorded for the seven-month periods before and dur<strong>in</strong>g the pilot were consideredand compared to those <strong>in</strong> ‘C’ Division (which also focused on PPOs). This analysis revealedthat <strong>in</strong> ‘A’ Division the number of detections per 1,000 burglaries <strong>in</strong>creased slightly over time,but less so than it did <strong>in</strong> ‘C’ Division for the same period of time. Moreover, <strong>in</strong> the pilot areathe rate of detections was a little lower for both periods than it was for the same period of timeone year earlier, whereas for the comparison area the reverse was true. This pattern ofresults would suggest that changes <strong>in</strong> the pilot area over time are unlikely to be attributable tothe target<strong>in</strong>g of prolific offenders.Complicated analyses could be conducted to attempt to determ<strong>in</strong>e whether the reduction <strong>in</strong>the <strong>in</strong>cidence of burglary observed was statistically significant. Readers <strong>in</strong>terested <strong>in</strong> whatsuch analyses might show are directed to Appendix 3 of this <strong>report</strong>. However, as alreadydiscussed it is proposed that the <strong>in</strong>terpretation of such analyses would be unclear asimplementation of the pilot on the ground was so limited. Thus, <strong>in</strong> this section a simplemeasure is presented as a guide to the changes observed. The metric computed, an oddsratio, merely contrasts the change <strong>in</strong> the <strong>in</strong>tervention and comparison areas before and after<strong>in</strong>tervention. An odds ratio of one <strong>in</strong>dicates that the changes <strong>in</strong> the two areas werecommensurate, suggest<strong>in</strong>g no change <strong>in</strong> the pilot area. An odds ratio of greater (less) thanone suggests a reduction (<strong>in</strong>crease) <strong>in</strong> the <strong>in</strong>tervention area relative to the change observed<strong>in</strong> the comparison area. The statistical significance of the odds ratio can also be computed(see Lipsey and Wilson, 2001) by estimat<strong>in</strong>g the standard error of the value derived.This technique, which is readily <strong>in</strong>terpretable, has been frequently used <strong>in</strong> researchconcerned with what works <strong>in</strong> reduc<strong>in</strong>g <strong>crime</strong> (for examples, see Welsh and Farr<strong>in</strong>gton, 2006;Gill and Spriggs, 2005), but is not without it critics, particularly for analyses conducted at thesmall area level (for which fluctuations over time may occur even <strong>in</strong> the absence of<strong>in</strong>tervention: Marchant, 2005). However, the problems articulated about this approach arelikely to be less problematic for analyses conducted at the BCU level, for which the variationover time is less of an issue than for smaller areas (see Farr<strong>in</strong>gton and Welsh, 2006). Thus,the approach is used here because it provides a simple assessment of how th<strong>in</strong>gs changed <strong>in</strong>the pilot area relative to the comparator.7 Perhaps ironically, it was at this po<strong>in</strong>t <strong>in</strong> time, dur<strong>in</strong>g which the burglary rate had rema<strong>in</strong>ed stable for the last year,that ‘A’ Division was selected as the pilot location.60
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1. UCL JILL DANDO INSTITUTE OF CRIM
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ContentsAcknowledgementsExecutive s
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2.5 Illustration of a simple neares
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Project outcomesPatterns of burglar
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those that involved collaboration w
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1. IntroductionThis report represen
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optimally calibrated system, the go
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e ij = n .j x n i.nWhere, e ij is t
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Table 2.2: Knox ratios for Mansfiel
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