This thesis presents a new neighbourhood classification, the Leeds Classification for Community Safety (LCCS). This is used to demonstrate the usefulness of area classifications for providing area context information to crime analysis, and for identifying neighbourhoods with atypical crime profiles - given their neighbourhood type. The work can be seen as a development of the classifications\ud produced by the Home Office for comparative performance purposes, but at a smaller, neighbourhood scale. There has\ud been a recent trend among practitioners to use commercial geodemographic products for this task, but these tools are primarily designed for consumer segmentation applications and little is revealed about the way in which these classifications are constructed, or their ability to discriminate geographies of crime and disorder. The research presented in this thesis discusses critically both these issues.\ud \ud The research draws upon academic and policy literature on the geography of crime, environmental criminology and community safety policy, and describes the types of task undertaken by community safety analysts. Existing knowledge about the causes and motivations for crime are used to select variables from new national and local sources. The final partition was created using the fuzzy c-means\ud clustering technique, but alternative techniques were also employed and levels of agreement between the different results were measured. The design process also involved measuring the ability of different partitions to discriminate neighbourhood crime rates.\ud \ud Numeric comparisons were made between the LCCS and existing general purpose classifications, and these show that the task-specific approach was better overall at discriminating crime rates. Practical applications of the LCCS are also demonstrated using recorded crime data for criminal damage\ud and domestic burglary. Furthermore, variations in response to burglary target hardening are analysed using the LCCS, and the cost benefit to neighbourhoods of different types is shown. These practical demonstrations of the LCCS go to reinforce the assertion that area classification can be useful, practical tool to aid in the analysis and understanding of spatial patterns of crime and disorder.\u
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.