The subject of this thesis is policing at a local level with regard to how performance can be measured and compared between locations to make the police more accountable to the public with the aim of increasing the public’s confidence in the police. The research is carried out within the discipline of Geographic Information Science using multidimensional, multivariable clustering and classification techniques used to create geodemographic classifications to create geo-policing classifications. The scientific study of policing that these geo-policing classifications facilitate means that the research carried out in this thesis can also be regarded as falling within the realm of the science of policing.
The research explores the causes of the mismatch of public perception of the level of crime and the information in official crime statistics that was termed the Reassurance Gap. This led to a radical shift of resources into council ward based neighbourhood policing to reduce the fear of crime and increase the public’s confidence in the police. Subsequent research has shown that the reduction in the fear of crime and the increase of the public’s confidence in the police are not directly linked. The most effective method for police to increase public confidence in themselves appears to be demonstrating that they properly understand the policing problems in a neighbourhood and that they are efficiently, effectively and fairly tackling those problems.
The research examines the use of crime maps for the public to improve police accountability and public’s confidence in the police. The research concludes that official crime statistics are not fit for purpose for local police accountability and makes the case for the use of police incident data instead. The thesis shows the utility of police incident data and creates a framework that allows policing itself rather than just the outcomes of policing to be assessed.