Prospective crime mapping in operational context Final report

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prior to intervention, or specific events unrelated to the intervention occur that might impact upon theincidence of crime.However, one limitation of the type of analysis discussed above is that it is insensitive to more generalchanges that might occur. Consider that there may be changes in policy that would have a genericeffect on the rate of burglary. For example, a county-wide policy introduced on a particular date wouldbe expected to impact upon burglary across the entire area. Unless such a policy were identified itcould not be modelled using the type of analytic design discussed above. Changes in recordingpractices, such as those introduced as part of the National Crime Recording Standard, may also affectthe time-series.Thus, a variant of the above approach for which changes in a comparison area are used to de-trendthe time-series in the first step of the analysis are here used to increase the validity of the analysis. Bydoing so, this means that any changes in the action area that may be explained by factors such asthose already discussed can be estimated and removed from the time-series.For the current evaluation a fairly long pre-intervention time series facilitated a reliable diagnosticanalysis of the pre-intervention time-series. However, many researchers recommend that wherepossible it is wise to employ a range of statistical approaches to the analysis of data. The advantageof so doing is that where the results of the analysis converge, one can be more confident thatconclusions drawn are not based on the specific biases of a particular statistical procedure. For thisreason, in this report analyses are conducted using both the odds ratio approach discussed above anda variant of the approach to the analysis of short time-series proposed by Allison & Gorman (1993).Odds ratio analysisTo calculate the odds ratios, the count of crime for the seven months before and during the pilot werecontrasted. The standard errors were computed in the usual way (see Lipsey and Wilson, 2001) aswell as using monthly variation as suggested by Gill and Spriggs (2005). Table A3.1 shows the countof burglary before and during the pilot along with the OR, and associated confidence intervals and z-score. The confidence intervals shown, calculated using the traditional approach indicate the upperand lower estimates of the odds ratios. These suggest that the true odds ratio lies somewherebetween 0.99 and 1.34. The z-score provides an indication of the likely statistical significance of theOR. For a two-tailed test, the z-score is required to exceed 1.96. On the basis of these results, theanalysis would suggest that the reduction in burglary observed in ‘A’ Division exceeded that in thecomparison area, but that the trend was marginally non-significant.Table A3.1: Change in the volume of burglary and odds-ratio statisticsBefore After GrosschangeOddsratioConfidenceintervalsz-scorePilot 663 554 109 1.15 0.99-1.34 1.80Comparisonarea684 659 2585
Time-series analysisFor the purposes of illustration, Figure 6.1 (in the main body of the report) shows a time-series graphof the change in the count of burglary in the action and control areas for a period of two years beforeintervention up until the end of the evaluation period. As a first step, an OLS analysis, with themonthly count of burglary in the action area for the period prior to implementation was regressedagainst that in the comparator. Also included in the model was an (independent) variable whichindicated the time point (see above). This analysis indicated that changes in the comparison area, ‘C’Division, were significantly associated with those in the action area (ß=0.33, ß(s.e)=0.09, t(51)=3.90,p
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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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Table 2.6: Monte-Carlo results for
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Table 2.10: Weekly Knox ratios for
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Table 2.14: Monte-Carlo results for
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Figure 2.1: The five policing areas
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The results for area 5 again demons
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The bandwidth used to generate the
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a densely populated urban area this
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Table 2.24: Average number of crime
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Patrolling efficiencyAs discussed e
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3. Tactical options and selecting a
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Selecting a pilot siteThe decision
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Table 3.2: Tactical options matrixT
Page 45 and 46: Type ofinterventionStudyUse ofintel
Page 47 and 48: Other potential tactical optionsAt
Page 49 and 50: 4. System development and evolution
Page 51 and 52: the same time of day as each other
Page 53 and 54: unfortunately, implementation or us
Page 55 and 56: any tactical options were employed
Page 57 and 58: the end of the pilot. In addition t
Page 59 and 60: Figure 5.1: Promap dissemination pr
Page 61 and 62: the busy schedule of the new Divisi
Page 63 and 64: Tactical deliveryCommand Team daily
Page 65 and 66: Table 5.3: Number of respondents wh
Page 67 and 68: permitted, up to four plain clothed
Page 69 and 70: observation made by those who used
Page 71 and 72: A simple time-series analysis (see
Page 73 and 74: Two approaches were used to compute
Page 75 and 76: Figure 6.3: Changes in the proporti
Page 77 and 78: Figure 6.5: Changes in the proporti
Page 79 and 80: With respect to implementation real
Page 81 and 82: ReferencesAggresti, A. (1996) An In
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Page 85 and 86: Appendix 1. The information technol
Page 87 and 88: Figure A1.2: Stand-alone applicatio
Page 89 and 90: Recommendations that may be realise
Page 91 and 92: Section 1: knowledge and understand
Page 93 and 94: Extra Comments (please outline any
Page 95: In relation to the evaluation of in
Page 99 and 100: Figure A3.1: Changes in the spatial
Page 101 and 102: Figure A3.2: Lorenz curves showing
Page 103 and 104: To recapitulate and elaborate, the
Page 105 and 106: Concluding comments on methodThe te
Page 107 and 108: Figure A5.2: An enlargement of the
Page 109 and 110: Figure A5.6: Prospective map magnif
Page 111: Produced by the Research Developmen
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Prospective crime mapping in operational context Final report

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