80/20 crime (again)

Usually when the 80/20 figure for crime is quoted it refers 80% of crime is committed by 20% of people (which incidently is wildly inaccurate for most types of crime - more like 99.9% of crime committed by 0.01% of people). But there is a new use.

The above is a quote from the newly published report by the HMIC entitled "Taking time for crime". It publication date of 27th September 2012 missed my thesis submission date by a couple of weeks so I could not include it. It would have been nice to do so because it supports one of the thesis' central arguments. That is that measuring police performance using crime data (where I assume the 20% comes from) gives only a partial view. The more complete view is obtained from the police incident data set where I know from the report that 80% comes from.

The report can be found here. The is a very important report because it is attempting get police forces to change focus yet again. I will comment further in future post.

Geo-policing of Neighbourhoods in Greater London

Oh yes, decided on a title in the last week. "Geo-policing of Neighbourhoods in Greater London". Here is the abstract.

Abstract

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.

Update and thanks

Just to update anyone interested in what I am doing. I have finished writing my thesis and submitted it for for examination. One of my examiners is busy until Christmas so it looks like I am going to have my viva in January. I am actually quite pleased with it.

In the mean time I have got myself a job as lecturer in GIS at Kingston (Surrey) University. I am just trying to get my head around what is required for that.

It is not always easy to show people who you have thanked in the Acknowledgements of a thesis so I thought I would publish it here.

Acknowledgements

The research was funded by the Economic and Social Research Council Collaborative Award for Science and Engineering (ESRC CASE) with the Metropolitan Police Service (MPS), my former employer, acting as co-sponsor. The research was made possible by data from MPS information systems facilitated by Trevor Adams. Detective Superintendent Neil Wilson, a former colleague, assisted with obtaining London Borough of Camden crime and disorder data, which was integral to the research. I am grateful to both.

I have been supported and guided throughout my research by a team of academics within the University College London Geography Department and the Centre for Advanced Spatial Analysis. Typical of this was being provided with data regarding licensed premises and other commercial premises in London by Dr Duncan Smith.

I particularly want to thank Professor Paul Longley, my principal supervisor, who has stretched and challenged me to produce better throughout the research. His guidance and friendship have been unwavering.

I have also appreciated the support and help of fellow students, most notably Daniel Lewis and James Cheshire who eased me into the world of Geographical Information System software.

I am particularly grateful to the Semeion Research Institute in Rome Italy and its Director, Professor Massimo Buscema, who introduced me to multi-dimensional, multivariable clustering techniques when I managed a project whilst I was still in the MPS. We renewed our acquaintance with during this research. The Self Organising Map (SOM) software to perform the clustering processes in Chapter 7 was written by Dr Giulia Massini; I am grateful to her and for the loan of the software.

I have friends and family to thank as well. Firstly, Janet Smith and Monica McKinnie, who became Facebook friends through our joint connection of attending the same junior school in the Cameron Highlands of Malaysia and who diligently proofread, pointing out my typos and more bazaar sentence constructions.  They are amazing. My father and mother (especially my father, a professor himself and supervisor of many PhD’s) for their gentle coaxing. Thanks to my youngest sister Megan, who carried out her PhD research in parallel with me but finished before me, for not gloating. And finally I am very grateful to my wife, Vivien, who has shown unreasonable confidence in my abilities from the start and given me plenty of space and time to do my research and kept me going.