Prospective crime mapping in operational context Final report

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61MonthAug-03Sep-03Oct-03Nov-03Dec-03Jan-04Feb-04Mar-04Apr-04May-04Jun-04Jul-04Aug-04Sep-04Oct-04Nov-04Dec-04Jan-05050Burglary count100150200250Figure 6.1: Time-series graph of the count of burglary before and during pilotFeb-05Mar-05Apr-05May-05Jun-05Jul-05Aug-05Sep-05Oct-05Nov-05Dec-05Burglary preventionpack distributionTargeted patrolsControlPilotJan-06Feb-06
Two approaches were used to compute the standard errors (since these are critical in determining thesignificance of the effect-size derived), one used by Farrington and colleagues (see Welsh andFarrington, 2006), the other by Gill and Spriggs (2005), 8 Both approaches converged on similarestimates and hence only the former are reported here.To calculate the odds ratios (OR), the count of crime for the same period of time before and during thepilot were contrasted. 9 The standard errors were computed in the usual way (see Lipsey and Wilson,2001) as well as using monthly variation as suggested by Gill and Spriggs (2005). Table 6.1 showsthe count of burglary for the periods before and during the pilot phase along with the OR, andassociated confidence intervals and z-score. The confidence intervals shown, calculated using thetraditional approach indicates the upper and lower estimates of the odds ratios. These suggest thatthe true odds ratio lies somewhere between 0.99 and 1.34. The z-score provides an indication of thelikely statistical significance of the OR. For a two-tailed test, the z-score is required to exceed 1.96.On the basis of these results, the analysis would suggest that the reduction in burglary observed in ‘A’Division exceeded that in the comparison area, but that the trend was marginally non-significant.Table 6.1: Change in the volume of burglary and odds-ratio statisticsBefore After GrosschangeOddsratioConfidenceintervalz-scorePilot 663 554 109 1.15 0.99-1.34 1.80Comparisonarea684 659 25Given the duration of the pilot and the implementation issues experienced the only surprising featureof this result is that it came so close to statistical reliability. The above analysis considers only generalchanges in burglary over time and thus in the next section changes in the time of day when burglariesoccurred will be examined.Change in the time of day burglaries were committedAs noted in the implementation section of the main report, there was a consensus of opinion that thepredictive maps were more frequently used during the evenings, when reactive policing demands onpatrolling officer time were typically less acute. Thus, one expectation would be that if the predictivemaps were used more frequently for resource allocation during the evenings, there should beobserved a greater reduction in the number of burglaries that occurred during this time of day followingthe inception of the pilot.To explore this, rather than analysing changes in the rate of burglary for each hour of the day, theapproach adopted in an earlier section of the report was used. That is, the shift during which everyburglary occurred was identified (by computing the mid-point of the earliest and latest times the eventcould have occurred) and the monthly patterns summarised. Only burglaries for which the window ofreporting was less than eight hours were included in the analysis. Consequently, there was someattrition in the volume of data analysed (50% of events occurred within an interval of eight hours). Afurther analysis (not shown), conducted using a window of fifteen hours to increase the sample size(64% of events occurred within a fifteen-hour reporting window), revealed the same pattern of results.Figure 6.2 shows the change over time in the proportion of all burglaries committed within ‘A’ Divisionthat occurred during the evening shift, before and after the start of the pilot. A trend line is included toillustrate the pattern prior to the start of the scheme. The figure illustrates that there was some8 Gill and Spriggs (2005) use a slightly different approach to calculate the standard by considering the monthly fluctuation in thevolume of crime to reduce a problem known as over-dispersion.9 The pilot started in the middle of August but in the analyses that follow, for simplicity 1 August is taken as the start date. Theeffect of so doing is to make the analyses more conservative.62
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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
Page 22 and 23: Table 2.10: Weekly Knox ratios for
Page 24 and 25: Table 2.14: Monte-Carlo results for
Page 26 and 27: Figure 2.1: The five policing areas
Page 28 and 29: The results for area 5 again demons
Page 30 and 31: The bandwidth used to generate the
Page 32: a densely populated urban area this
Page 35 and 36: Table 2.24: Average number of crime
Page 37 and 38: Patrolling efficiencyAs discussed e
Page 39 and 40: 3. Tactical options and selecting a
Page 41 and 42: Selecting a pilot siteThe decision
Page 43 and 44: 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: A simple time-series analysis (see
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
Page 83 and 84: Johnson, S.D., Summers, L., and Pea
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 and 96: In relation to the evaluation of in
Page 97 and 98: Time-series analysisFor the purpose
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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