Crime and the social mosaic

The very existence of geospatial data enables us to ask much broader questions about the context in which crime occurs. For example, rather than just identifying some areas as having higher crime than others we are able to move the discussion forward to ask questions about what types of area have high crime. Are they socially disadvantaged? Do they have high concentrations of particular types of housing? Are they student areas?

But geospatial data sets also enables us to ask questions about what is next to a particular area and what effect that might have on crime within an area. For example, do poorer areas have higher crime if they are also  surrounded by poorer areas? Do they have lower crime if they are next to affluent areas ? Do  affluent areas surrounded by disadvantaged areas have higher crime than affluent areas bordering middle or high income areas?. Geospatial data gives us the opportunity for the first time to explore the extent to which an area’s crime rate is affected not just by the crime risks within the area (i.e .the housing, natural surveillance, offender population and community cohesion) but by the characteristics of its neighbouring areas. This truly is breaking new ground!