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Measuring UK crime gangs / Giles Oatley; Tom Crick

Proceedings of 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Pages: 253 - 256

Swansea University Author: Crick, Tom

DOI (Published version): 10.1109/ASONAM.2014.6921592

Abstract

This paper describes the output of a study to tackle the problem of gang-related crime in the UK; we present the intelligence and routinely gathered data available to a UK regional police force, and describe an initial social network analysis of gangs in the Greater Manchester area of the UK between...

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Published in: Proceedings of 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
ISBN: 978-1-4799-5877-1
Published: IEEE 2014
Online Access: https://ieeexplore.ieee.org/document/6921592/
URI: https://cronfa.swan.ac.uk/Record/cronfa43390
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Abstract: This paper describes the output of a study to tackle the problem of gang-related crime in the UK; we present the intelligence and routinely gathered data available to a UK regional police force, and describe an initial social network analysis of gangs in the Greater Manchester area of the UK between 2000-2006. By applying social network analysis techniques, we attempt to detect the birth of two new gangs based on local features (modularity, cliques) and global features (clustering coefficient). Thus for the future, identifying the changes in these can help us identify the possible birth of new gangs (sub-networks) in the social system. Furthermore, we study the dynamics of these networks globally and locally, and have identified the global characteristics that tell us that they are not random graphs - they are small world graphs - implying that the formation of gangs is not a random event. However, we are not yet able to conclude anything significant about scale-free characteristics due to insufficient sample size.
Item Description: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)
College: College of Science
Start Page: 253
End Page: 256