Prospective crime mapping in operational context Final report

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