Prospective crime mapping in operational context Final report

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Table 2.14: Monte-Carlo results for ‘A’ Division (values in bold are statisticallysignificant, N=1703)MONTHS1 2 3 4 5 6100 1.36 0.93 0.93 0.94 0.85 0.79200 1.27 0.95 0.91 0.93 0.93 0.93300 1.20 0.99 0.95 0.95 0.82 0.90400 1.18 0.96 0.98 0.93 0.85 0.95500 1.08 0.96 0.93 0.95 0.86 1.02600 1.08 1.01 0.97 0.95 0.88 0.99700 1.08 0.97 0.96 0.94 0.88 1.04800 1.06 1.01 0.95 0.98 0.98 0.96900 1.16 1.03 0.97 0.92 0.92 0.891000 1.04 1.05 0.96 1.02 0.86 0.92To examine the pattern in more detail, a weekly analysis was conducted. The results, shownas Table 2.15, suggest a similar pattern but that again the elevated risks endure for more thanone month in some cases (e.g. 101-200m), less in others (e.g. 601-700m).Table 2.15: Weekly Knox ratios for ‘A’ Division (values in bold are statisticallysignificant according to the residual scores (not shown), N=1703)WEEKS1 2 3 4 5 6 7 8 9 10 11 12100 2.38 1.77 1.54 1.40 1.08 0.97 0.96 0.96 0.96 0.90 0.92 0.85200 1.63 1.41 1.38 1.30 1.15 1.10 0.99 0.98 0.96 0.96 0.94 0.92300 1.38 1.35 1.24 1.21 1.11 1.05 1.02 0.99 0.97 0.97 0.99 0.96400 1.26 1.22 1.21 1.18 1.16 1.11 1.06 0.97 0.94 0.91 0.93 0.95500 1.05 1.10 1.08 1.07 1.04 1.03 1.00 0.97 0.96 0.94 0.93 0.91600 1.14 1.13 1.09 1.08 1.04 1.02 1.03 1.01 1.03 1.02 0.97 0.98700 1.18 1.09 1.06 1.06 1.04 1.06 1.05 1.00 0.98 0.94 0.93 0.95800 1.15 1.11 1.08 1.06 1.02 1.02 1.03 1.04 1.03 0.97 0.93 0.94900 1.21 1.20 1.17 1.16 1.12 1.10 1.10 1.09 1.05 1.00 0.99 0.951000 1.20 1.05 1.04 1.03 1.02 1.04 1.04 1.05 1.04 1.03 1.03 0.99Summary of the patterns so farThe above results demonstrate that for all areas analysed, there was clear evidence that therisk of burglary is communicable, although the distances over which this occurred varied byarea. In relation to the time over which this elevated risk endured, this tended to be at leastone month, but in some cases extended over longer intervals. One way of summarising thepatterns observed is presented as Table 2.16. Each cell of the table is shaded to indicate forhow many areas the particular cell had an over-representation of burglary pairs. This allows aquick comparison to see for how many areas the risk of burglary communicated up to say100m and one month, 201-300m and two months, and so on. It is clear that the mostcommon pattern was for the risk of burglary to extend up to 400m and for one month,although for some areas the risk communicated over greater distances, even up to 1,000m.Considering the unexplained results that were not anticipated by the theory (e.g. 200m andthree months for Corby), it is clear that there was no regularity in these results, suggestingthat they probably reflect spurious correlations or statistical artefacts.Each cell also contains the average Knox ratio for that cell, calculated across the five datasets. Not surprisingly the results correlate with the colour coding, with those cells shaded in13
the darkest colours having the highest values. It is also apparent that in general there is apattern of distance decay, such that the highest Knox ratios are for the shortest spatialintervals.Table 2.16: Summary or the analyses concerned with the communicability of risk (cellvalues are Knox ratios generated using Monte-Carlo simulation)MONTHS1 2 3 4 5 6100 1.26 1.00 1.01 0.99 0.97 0.87200 1.20 0.97 0.99 0.98 0.95 0.94300 1.17 0.98 0.95 1.02 0.92 1.01400 1.18 0.97 1.01 1.05 0.97 0.99500 1.04 1.02 0.98 1.02 0.99 1.03600 1.08 1.05 1.01 1.04 0.97 0.99700 1.08 0.99 1.02 1.00 0.93 0.98800 1.04 1.02 1.01 0.98 1.01 1.03900 1.03 1.01 1.03 0.96 1.00 0.991000 1.04 1.00 1.02 1.02 1.01 0.97Key 4-5 2-3 0-1Thus, it would appear that as a general rule the risk of burglary is communicable up to adistance of around 400m for at least one month. There was no area for which this was not thecase, hence universal application of a 400 metres one month rule is in no case inappropriate,merely that it excludes a proportion of other high-risk homes. The sensitivity analysesconducted to examine the duration of the elevation in risk suggested that this extendedbeyond one month in most cases, up to around eight weeks. This will subsequently bereferred to this as the classic profile.Spatial variation?Compared to the other areas considered, ‘A’ Division covers a large geographic area, thisbeing approximately 150 square miles. For this reason, additional analyses were conductedfor areas nested within the BCU. In so doing, the analyses control for another example ofSimpson’s paradox (Simpson, 1969), commonly referred to as the Modifiable Areal UnitProblem within the research literature (e.g. Openshaw, 1995). In this case, the problem isthat it is possible, even likely, that patterns evident at the aggregate level (i.e. across all fiveareas) differ from those that would be apparent at the local level. Thus, further analyses wereconducted to examine the patterns at the local level for five smaller areas within ‘A’ Division.To do this, analyses were conducted for each of the five policing sections within ‘A’ Division.However, one problem with conducting such analysis is that the geographical policingboundaries used are usually artificial and hence if the data are analysed for one area at atime using the existing boundaries, this can create what is known as an edge effect. Theproblem that arises is that patterns which occur across and around the boundary can remainundetected. Where boundaries are defined by natural features such as rivers or othertopological barriers this of course is not a problem.To reduce the likelihood of encountering this problem, a 1km buffer was generated aroundeach area and the data within the larger area analysed. In this way, any patterns of offendingat the edges of the policing boundaries should be detected. The five different policing areasand the buffer zones used are shown in Figure 2.1.14
Page 2 and 3: 1. UCL JILL DANDO INSTITUTE OF CRIM
Page 4 and 5: ContentsAcknowledgementsExecutive s
Page 6 and 7: 2.5 Illustration of a simple neares
Page 8 and 9: Project outcomesPatterns of burglar
Page 10 and 11: those that involved collaboration w
Page 12 and 13: 1. IntroductionThis report represen
Page 14 and 15: optimally calibrated system, the go
Page 16 and 17: e ij = n .j x n i.nWhere, e ij is t
Page 18 and 19: Table 2.2: Knox ratios for Mansfiel
Page 20 and 21: Table 2.6: Monte-Carlo results for
Page 22 and 23: Table 2.10: Weekly Knox ratios 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 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
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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
Page 111:
Produced by the Research Developmen
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