Prospective crime mapping in operational context Final report

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The results for area 5 again demonstrate that the apparent risk to houses near to the burgledhome is particularly dramatic. However, in common with area 3 the risk of burglary appearsto communicate over a greater distance in this area.Table 2.21: Weekly Knox analysis for area 5 (values in bold are statistically significantaccording to the residual scores (not shown), N=311)WEEKS1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00100 5.29 1.14 0.86 0.71 0.71 0.67 0.83 0.29 0.71 1.17 0.50 0.33200 2.31 1.77 1.14 0.77 0.69 1.08 0.92 0.85 0.46 1.38 1.00 0.45300 2.20 1.21 0.80 0.86 1.07 0.62 0.36 1.14 0.64 1.29 1.67 0.92400 2.33 1.07 0.87 0.71 1.21 0.29 0.71 0.71 0.86 1.00 1.15 0.83500 1.64 1.50 1.43 0.69 0.86 0.54 1.38 1.15 0.77 1.08 0.58 1.08600 1.11 1.06 0.72 0.76 0.94 1.00 1.56 0.88 1.29 1.12 0.53 0.73700 1.53 0.72 0.63 0.89 0.72 0.88 1.24 0.72 1.00 1.12 0.38 1.06800 1.47 0.68 0.79 0.83 1.17 0.94 0.82 1.06 1.06 0.56 1.44 0.94900 1.32 1.11 1.32 0.89 1.00 1.06 1.59 1.06 0.78 0.89 0.81 0.631000 0.72 1.06 1.22 1.41 0.89 1.18 1.41 1.47 0.71 1.12 1.40 1.07Thus, although caution is required when interpreting the above results due to the samplesizes involved, the results suggest different profiles across the different areas. That said, forevery area, the largest Knox ratio (and residual) was for the shortest space-time interval,suggesting the ubiquity of this finding at all levels of geographic resolution.Temporal variation?For a number of reasons, it is possible that the profile of the communication of risk for eacharea may change over time. For instance, different offenders may adopt distinct foragingstrategies. Some may prefer to commit crimes very close to previously burgled homesimmediately afterwards, whereas others may prefer to commit them nearby after a shortinterval has elapsed. This may particularly affect the time-space patterning of burglary if newoffenders move into an area, were arrested or move elsewhere. Also, it is possible that thedevelopment or demolition of housing would change the availability of targets and hence thepatterns of crime.For this reason, Knox profiles were generated for Mansfield and ‘A’ Division for sequentialtime periods to determine whether or not the profiles do change, and if so in what way. To dothis, for each area a Knox profile was generated using twelve months worth of data. Next, anew profile was generated using twelve months of data but which, compared to the previousanalysis, included the next two weeks of data, and excluded the oldest two weeks of data.This was repeated 22 times for each area. Thus the analysis was completed using a movingwindow of data, which included new data on each iteration. The results of the analyses werethen animated and compared.The general pattern of results suggested that while the Knox profiles did vary a little from oneprofile to the next they tended to remain stable over time. This may suggest that, in general,offenders adopt similar foraging patterns or that a sufficiently small proportion of activeburglars were ever in custody to modify the overall picture. There is ubiquity of the findingthat burglary clusters in space and time, as evidenced by recent research that shows thatthese patterns are apparent in all other countries for which data have so far been analysedsuch as the UK, US, Netherlands, New Zealand and Australia (Johnson et al., 2006). Oneexplanation for the differences in the profiles observed for each area could relate todifferences in target density between the different areas. Thus, the risk of victimisation maycommunicate over greater distances in areas in which houses are more dispersed, whereasrisks may communicate over shorter distances where houses are spatially concentrated and17
hence available nearby targets plentiful. Considerations of land use provide the mostplausible accounts of such area variation in pattern. Other possibilities exist.Predicting the futureHaving demonstrated that the risk of burglary is indeed communicable in the East Midlands,the next task was to determine whether it is sufficiently predictable to suggest that theproduction of predictive mapping software would aid operational policing. Thus, research wasconducted to compare the effectiveness of:1. Promap;2. what the police are currently doing;3. a simple variant of retrospective hotspotting; and4. what might be expected if areas were prioritised for crime reductive attentionrandomly.At the time of the research, none of the areas currently used retrospective mappingtechniques per se. Instead, they generated pin maps on a fortnightly basis using two weeks’data. This simply involved generating a map which shows the locations of all burglaries thatoccurred within the previous two weeks. On the basis of these maps, operational resourcesmay be deployed. This is similar to retrospective hotspot mapping in that both methodssummarise historic data, and hence in what follows the authors will use retrospectivehotspotting as a proxy for what the analysts currently do. However, it should be noted thatproblems with pin mapping are well documented (e.g. Chainey & Ratcliffe, 2005) and thus theretrospective approach used here as an analogue is significantly superior to what the analystswere doing.Item 2 in the list above has thus been eliminated, being a sub-optimal type of retrospectivehotspotting. As is intuitively clear, random allocation of resources will be less efficient thaneither prospective or retrospective hotspotting, so the choice of method comes down toprospective or retrospective. While necessarily technical, the remainder of this section shouldbe read at least in a cursory way by the interested practitioner, since it identifies thecomputational differences between the two primary contending approaches.The technique most commonly used to identify hotspots involves the generation of a twodimensionallattice to represent the area of interest. As shown in Figure 2.2, a twodimensionallattice (Fig. 2.2(2)) is overlain upon a study area (Fig. 2.2(1)). This comprises aseries of (x*y) cells, each with identical proportions. The challenge of delineating a hotspotlies in the derivation of a set of values, one per cell, that reflects the intensity of crime risk ateach location. Thus, a methodology and mathematical algorithm is required that can generaterisk intensity values for every cell. One technique commonly used to do this is called the‘moving window’. Here, a circle with a predetermined radius (referred to as the bandwidth) isdrawn from the midpoint of each cell (Fig. 2.2(3)), and each of the events that falls within thecircle is used to generate the risk intensity value for that cell. The risk intensity value for eachcell is determined by the number of crime events (in Figure 2.2(1), four for the cellconsidered) that occurred within the circle and how far away they are located from themidpoint of the cell. Those closest to the midpoint are typically assigned a greater weightingthan those further away. To illustrate the method, three of the cells in Figure 2.2(3) have beenshaded to indicate the intensity of risk at those locations. Those shaded darkest exhibit thehighest risks. An example of a hotspot (quartic) function (Bailey & Gatrell, 1995) is describedby equation (1):223 ⎛ d ⎞( ) = 1 ⎟2 ⎜ −iλτs ∑(1)2di≤τπτ ⎝ τ ⎠Where, λτ(s)= risk intensity value for cell s τ = bandwidthd i = distance of each point (i) within the bandwidth from the centroid of the cell18
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 24 and 25: Table 2.14: Monte-Carlo results for
Page 26 and 27: Figure 2.1: The five policing areas
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
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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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