Prospective crime mapping in operational context Final report

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A still further approach to exploring the stability in burglary risk is to compute the correlation betweenthe count of burglary across a series of areas encapsulated by the BCU before and during the pilot. Alarge correlation would suggest that the distribution of risk was stable, a small coefficient that it wasfluid. To do this, areas at different levels of geographical resolution could be used. For example, onecould examine the correlation at the level of a town or for a grid of smaller regular sized cells.Obviously, the larger the area considered, the more stable the patterns would be expected to be.Thus, analyses were conducted at two levels of geographic resolution, first at the census output arealevel, and second for a series of 1kmX1km cells. There are a total of 29 census output areas in ‘A’Division and at this level of resolution the rank-order correlation of .89 (p
Figure A3.2: Lorenz curves showing the distribution of burglary riskCumulative percentage of burglary100908070605040302010Cumulative % afterCumulative % before00 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100Cumulative percentage of 1km areasThe general pattern for both periods of time confirms what the authors already knew, that burglary ishighly concentrated in space. It also shows that there was little change in the spatial concentration ofburglary following the start of the pilot. This is slightly disappointing. With fuller implementation, itwould become worthwhile to compare Lorenz curves for pilot and comparison areas, since there maybe a general tendency for crimes as they become rarer also to have a very different distribution.Changes in patterns in space and timeThe burglary pattern which gives prospective mapping its superiority over retrospective mapping is itsspatio-temporal clustering. The traditional definition of a geographical hotspot is an area for whichcrime is particularly concentrated in space for a period of time such as one year or six months. Thus,hotspots are essentially the accumulation of pairs of crimes which occur close to each other within thetime period of interest. In consequence, the analysis of hotspots does not consider the timing 12 ofevents, other than requiring them to have occurred within the interval of interest, say over the last sixmonths. The analyses so far presented have looked at distributions in time and space separately. Analternative and more sensitive approach to the examination of spatio-temporal patterns of crime wouldbe to identify clusters or series of event pairs that occur not only close in space but also in time. Puttoo simply, one would expect prospective mapping to reduce the length of series of burglaries close intime and space.The identification of such clusters and their length would have useful implications for crime reductionin at least two ways. First, this would provide the police with an idea of the tempo of localisedincreases in burglary risk. For example, if the longest clusters that could be identified consisted ofonly two events, this would suggest that the elevated risk in an area had a fairly slow tempo, whereasif clusters of ten events were routinely identified this would suggest a higher frequency of space-timeclustering. This form of analysis chimes with the Promap approach. It is also novel. The reader isinvited to think of what follows in this light, namely as an exploratory approach which no doubt needsrefinement but is more suited to the technique it aspires to understand and evaluate. This type ofanalysis is useful for evaluation research by enabling the detection of qualitative changes in spatiotemporalpatterns of crime. It is perhaps easier to illustrate this point by starting with a special case of12 By timing, the authors mean the time between events rather than the regularity of the time of day that crimes occur. The latterhas been usefully explored by Ratcliffe (2002).90
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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
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Type ofinterventionStudyUse ofintel
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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
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: Figure A3.1: Changes in the spatial
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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