Prospective crime mapping in operational context Final report

Patrolling efficiencyAs discussed elsewhere (Bowers et al., 2004), even though a map may predict a largevolume of crime, it may be of little utility in an operational context if it is made of a largenumber of dispersed hotspots. The best map would perhaps be one with a relatively smallnumber of clearly defined hotspots. One way of measuring this is to count the number ofhotspots generated. Another way, which is slightly more sophisticated is to conduct a nearestneighbour analysis.Nearest neighbour analysis is a test of spatial randomness. The nearest neighbour index(nni) in particular measures how clustered points or cells are relative to what would beexpected on the basis of chance. Here, the authors consider the application of this to theanalysis of hot cells - the ten per cent of cells with the highest risk intensity values. How closetogether are they? In this context, a value of one would indicate that the hot cells wererandomly distributed across the area. The lower the value of the nni, the more the hot cellscoalesce to form coherent (and hence policeable) hotspots. The index can be computed forthe nearest high-risk neighbour for each cell, which will often be the adjacent cell. It can alsobe computed for the next nearest neighbour, the next, and so on, up to k-orders. The value ofk is specified by the researcher. Thus, the k-order parameter describes which neighbour isbeing analysed, the nearest (1st order), the next nearest (2nd order) and so on (up to the kthorder). To illustrate, consider Figure 2.5. The nni for the two examples would be the samefor the first order nearest neighbours. However, for the data on the left the second order nniwould be lower than that for the data on the right, thereby indicating that the hot cells in theformer are more spatially clustered than the latter. The most efficient hotspots would perhapsbe those with a low nni for the nearest neighbour, for the next, but particularly for the higherorders. This is because the greater the number of orders for which the nni remains low is anindication of a lower number of hotspots that are more coalescent. A series of dispersedhotspots would have a low distance for the first neighbour, but the distance would increase foreach successive order. A patrolling police officer would then have to spend more time movingthrough low risk areas.Figure 2.5: Illustration of a simple nearest neighbour analysis for two data sets● ● ● ●●●●●Readers may be aware that this type of analysis is traditionally used to examine the degree towhich crime is clustered in space, but as should be evident from the above rationale it is ofclear analytic value here, albeit a novel application of the test. To recapitulate, this type ofanalysis can be used as one index of patrolling efficiency. The lower the nni for higher k-orders, the more efficient the map in this respect. Figure 2.6 shows an example analysis ofthis kind for ‘A’ Division for both a retrospective hotspot and for a prospective map. Theresults are clear. The prospective map has a low nni across all orders, whereas for theretrospective map whilst the nni is initially low, at around order ten it starts to increase.Analyses for other maps generated for different days revealed the same pattern of results. Itis difficult to overstate the importance of this result for the applicability of Promap to patroldeployment.26