Prospective crime mapping in operational context Final report

Project outcomesPatterns of burglaryUsing a modification of an approach developed in the field of epidemiology, analyses wereconducted to see if burglary does cluster in space and time and if the patterns vary acrosslocation. Briefly, to do this, for each area each burglary event is compared to every other andthe space-time distance between them recorded. The pattern of results observed is thencompared with what would be expected on the basis of chance, determined using what isknown as a Monte-Carlo simulation. Burglary was considered to be communicable if moreevents occurred near to each other in both space and time than would be expected on thebasis of chance.Without exception in the East Midlands areas studied, the risk of burglary is communicable upto a distance of around 400m for at least one month. This finding was used as a general rulein the construction of basic prospective maps. Sensitivity analyses were conducted toexamine the duration of the elevation in risk. These suggested that risk (albeit diminishing)extended beyond one month and in most cases up to around eight weeks.The patterns were to some extent specific to the time of day considered. For example, if aburglary occurred at one location during the afternoon, a further burglary was more likelynearby soon after and at a similar time of day. In supplementary work not reported here, theftfrom (but not theft of) vehicles was also found to be communicable.Developing the predictive system and measuring its accuracyOn the basis of the above findings, a method of prospective mapping (hereafter, Promap),based on the authors’ previous work, was developed. Across the different areas studied inthe initial phase of the research, police analysts were already using descriptive hotspotmapping. Promap was tested against an optimised version of the ‘retrospective’ hotspottingmethod in current use. The optimised versions were substantially more predictive than mapswhich resemble those typically used in crime analysis. Promap outperformed the optimised‘retrospective’ hotspot mapping system. It did this in three ways.• Promap correctly predicted more burglaries than other methods. For example, the finalversion of Promap could identify the locations of 78 per cent burglaries that occurredwithin the next seven days of a forecast, whereas for the same interval theretrospective model could identify only 51 per cent.• Relative to the retrospective maps, it yielded hotspots which formed more solid,coalescent, hence readily patrollable, areas.• The final version of Promap accurately predicted more crime while identifying a smallerarea than other methods. For example, the same fraction of burglaries occurring withintwo to seven days of a prediction could be forecasted by identifying patrolling areas halfthe size of those generated by retrospective mapsImplications for operational policingResearch conducted by the authors and others suggests that police officers’ perceptions ofwhere burglary hotspots form are often imperfect. This is particularly true for recent ratherthan enduring problems. The fluidity of burglary risk provides an explanation for this. Crimemoves and patterns evolve. Why should police officers be able to anticipate such changes?They should not, but computerised support systems that can assist in the crime reductionenterprise should.The advantages of Promap are that it could act to facilitate more targeted crime reductioninterventions, increasing the likelihood that resources are deployed to the right places at theright time, rather than where they were needed last week or last month (as with conventionalhotspot methods). Risky areas can be better defined so that patrols could move throughpriority areas more efficiently, spending less time in places with lower risk. The maps can alsobe produced for specific shifts to ensure their continued relevance over the course of the day.vi