Prospective crime mapping in operational context Final report

prior to intervention, or specific events unrelated to the intervention occur that might impact upon theincidence of crime.However, one limitation of the type of analysis discussed above is that it is insensitive to more generalchanges that might occur. Consider that there may be changes in policy that would have a genericeffect on the rate of burglary. For example, a county-wide policy introduced on a particular date wouldbe expected to impact upon burglary across the entire area. Unless such a policy were identified itcould not be modelled using the type of analytic design discussed above. Changes in recordingpractices, such as those introduced as part of the National Crime Recording Standard, may also affectthe time-series.Thus, a variant of the above approach for which changes in a comparison area are used to de-trendthe time-series in the first step of the analysis are here used to increase the validity of the analysis. Bydoing so, this means that any changes in the action area that may be explained by factors such asthose already discussed can be estimated and removed from the time-series.For the current evaluation a fairly long pre-intervention time series facilitated a reliable diagnosticanalysis of the pre-intervention time-series. However, many researchers recommend that wherepossible it is wise to employ a range of statistical approaches to the analysis of data. The advantageof so doing is that where the results of the analysis converge, one can be more confident thatconclusions drawn are not based on the specific biases of a particular statistical procedure. For thisreason, in this report analyses are conducted using both the odds ratio approach discussed above anda variant of the approach to the analysis of short time-series proposed by Allison & Gorman (1993).Odds ratio analysisTo calculate the odds ratios, the count of crime for the seven months before and during the pilot werecontrasted. The standard errors were computed in the usual way (see Lipsey and Wilson, 2001) aswell as using monthly variation as suggested by Gill and Spriggs (2005). Table A3.1 shows the countof burglary before and during the pilot along with the OR, and associated confidence intervals and z-score. The confidence intervals shown, calculated using the traditional approach indicate the upperand lower estimates of the odds ratios. These suggest that the true odds ratio lies somewherebetween 0.99 and 1.34. The z-score provides an indication of the likely statistical significance of theOR. For a two-tailed test, the z-score is required to exceed 1.96. On the basis of these results, theanalysis would suggest that the reduction in burglary observed in ‘A’ Division exceeded that in thecomparison area, but that the trend was marginally non-significant.Table A3.1: Change in the volume of burglary and odds-ratio statisticsBefore After GrosschangeOddsratioConfidenceintervalsz-scorePilot 663 554 109 1.15 0.99-1.34 1.80Comparisonarea684 659 2585