Prospective crime mapping in operational context Final report

Figure A3.4: The proportion of events belonging to different k-event series before and duringthe pilot40% of events that are the last in a k- event (ormore) series3530252015105AfterBefore02 3 4 5 6 7 8 9 10 11 12Chain length (k -events)Considering the patterns for the periods before and during the pilot, there are some differences. First,the longest series (12 events, of which there were three examples) identified occurred in the periodbefore intervention. More generally, longer series were identified in the period before the pilot, thanduring it. Conversely, during the pilot there was a slightly higher proportion of shorter series (two orthree events). On the face of it, this finding is in line with what would be expected if the pilot had beensuccessful. However, the reader should be mindful of two things.a. For the period during the pilot, a smaller number of burglaries were committed, whichmay have affected the pattern of results. For what it is worth, sampling only the samenumber of events for the period before (the first 554 events) as that after generatedexactly the same pattern of results. Consequently, differences in sample sizes wouldnot appear to explain the results observed.b. Perhaps more importantly, the effect observed was very subtle. This is notunexpected given that implementation, or rather the use of the system, was at bestonly minimally realised. With such a small effect size, and under the circumstances ofpartial implementation, it is inappropriate to conduct inferential statistical tests. 13Thus, this finding is interpreted as being inconclusive, but the analytic approachderived suggested a useful method for future research concerned with the stability ofclusters of crime.13 Such a test would involve comparing the observed distribution of k-event series with what would be expected if the timing andlocation of events were random. Thus, a variant of the Monte-Carlo approach used elsewhere in this report would seemappropriate.93