Prospective crime mapping in operational context Final report

space-time clustering: repeat victimisation. Considering evaluations of area-based interventions, it ispossible to not only measure changes in burglary incidence (the rate per 1,000 households) followingan intervention but also how concentrated on individual properties crime is. For example, for anintervention aimed at reducing repeat victimisation, simply examining the change in the incidence ofburglary may be insufficient to detect the full impact of a scheme, or the potential crime reductionmechanisms through which any change was realised. Instead, a more sensitive analysis (seeForrester et al., 1990) would be to examine the rate of repeat victimisation. A reduction in this type ofvictimisation would suggest an impact of the scheme, although a reduction in incidence would berequired to demonstrate that target-switch displacement did not occur. On the other hand, a reductionin incidence within an area that was unaccompanied by a reduction in repeat victimisation wouldprovide less convincing evidence that any reduction could reasonably be attributed to the scheme.In the same way, where strategies are employed to suppress emerging or enduring hotspots of crime,as was the case in the current pilot, one desired outcome of the scheme would be to truncate thelength of space-time clusters of burglary. That is, if resources are directed to the right places at theright times in a way that anticipates where the next event in a cluster is most likely to occur, then itwould be hoped that emerging spatio-temporal clusters of crime would be targeted before they have achance to propagate.To examine this issue, software was developed to identify series of events that occurred close in bothspace and time, ranging from two events (pairs) onwards. The approach allowed the frequency ofseries of different (k-event) lengths (e.g. pairs, triples, quads and so on) to be summarised andcompared for the periods before and during the pilot.The identification and summary of series of events can be done in a variety of ways. Here, for everyburglary (the reference) event, any antecedent burglary that occurred within a critical distance andtime of it was identified and added to the series. Using this approach only burglaries that occurredwithin the critical time and distance of the reference event could be identified. This precludes theidentification of other burglaries that might be linked to the others in a cluster. This problem isillustrated conceptually in Figure A3.3. In the top and bottom of Figure 6.8, there are four burglariesthat occurred within a few days of each other. In the first chain, all burglaries occur within the criticalspatial distance of the reference event (the leftmost event) and thus one identifies a chain of fourburglaries. For the second series, for the same reference event one would identify a chain of onlythree burglaries as the final event occurred further away from the reference event than the criticaldistance. However, it occurred within the critical distance of other events in the chain and henceshould be included in the series.Figure A3.3: An illustration of a triple (bottom) and quad chain (top)Critical distanceAn alternative approach would be to include in a cluster all burglaries that are within the criticaldistance and time of one or more events already identified as part of that series. Using this approach,both series shown in Figure A3.3 would be classified as being a four event series. In what follows thecritical distance and time used were 400m and one-week, respectively. Other definitions could beused, and other analyses (not shown) using different definitions revealed a similar pattern of results.91