Analysing crime patterns

The British Crime Survey reveals that nationally, areas with council flats, high unemployment and persons living alone or with many lone parent families, and areas with furnished flats and bedsits housing young single people are among the most at-risk of being burgled. Over the past 12 years, I have been involved in a number of Home Office projects, as well as for local police forces, analysing crime patterns to help tackle crime and disorder in local areas, by developing and applying Spatial Ecological Analysis (SEA) to crime data. SEA is a highly refined research methodology for analysing the relationship between, in this case, area crime rates and the social class, socio-economic status and demographic characteristics of area populations as well as the geography of offenders and victims of crimes. With my colleagues, I analyse crime in localities defined by the Census, such as areas each containing on average 125 households that are similar to each other in tenure and type of dwelling. Crime data for these analyses come from the police's own recorded crime database so that the accuracy of any analysis depends on the accuracy and completeness of these records. UK National Census and local authority data at the area level on household composition, living arrangements, household tenure, accommodation type, population turnover, economic circumstances and social and ethnic composition figure prominently in helping to explain geographical differences in particular crime rates. Selecting areas for burglary Rational choice theory suggests two stages to burglary: First the burglar selects the area to commit the crime in, and second the burglar selects the particular target (household) within that chosen area. Choice of area is a balance of risk against reward, while taking into account the effort involved. A burglar selects the area according to the potential reward in which case affluent areas might be favoured over less affluent and deprived areas. But any area where households are expected to have high value and easy to steal goods are attractive. Some areas have fewer formal (eg police) or informal (eg neighbourhood watch) guardians than others. Affluent neighbourhoods where residents are absent for extended periods during a day or at weekends and areas with low levels of collective responsibility are particularly attractive to burglars as the risk of getting caught may be low. Burglars often choose areas that they know well but where they will not be recognised, in which case an area near the burglar's own residence or work place can be suitable. Familiarity with the area and minimising the effort required are also part of the burglar's equation. Within this ecological framework, the choice of which particular household to burgle is often opportunistic. Intelligence-led policing Cartographic visualisation often using Geographic Information Systems (GIS), mathematical modelling and spatial data analysis are familiar to geographers, but they are also valuable tools for the police's operational procedures. Intelligence-led policing underlies the UK's National Intelligence Model for operational policing and it is driven by a national crime profile where there is a high level of individual and geographical repeat victimisation and where a large proportion of crime is committed by a comparatively small number of offenders. Analysing crime in small areas and for specific Police Force Areas (PFA) is exceedingly important as the police are territorial in their organisation and one aspect of resource allocation for them is by geographical area. Studies that focus on particular PFA provide additional local insight that supplements the evidence from national surveys. Spatial ecological analysis of crime has potential to provide the police with highly detailed information on local crime, which can be used to identify priority areas for police resources. Our figures demonstrate that most burglars live within a 2km radius of their target areas. Professor Robert Haining is in University of Cambridge's Department of Geography. This is an extract from a presentation he is giving on Wednesday 7 July at the fourth ESRC research methods festival