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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Using the UK general offender database as a means to measure and analyse organised crime
AU - Kirby, Stuart
AU - Francis, Brian
AU - Humphreys, Leslie
AU - Soothill, Keith Leonard
N1 - This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited.
PY - 2016/4/11
Y1 - 2016/4/11
N2 - Organised Crime is notoriously difficult to identify and measure, resulting in limited empirical evidence to inform policy makers and practitioners. This study explores the feasibility of identifying a greater number of organized crime offenders, currently captured but invisible, within existing national general crime databases. All 2.1million recorded offenders, captured over a four year period on the UK Police National Computer (PNC), were filtered across three criteria associated with organized crime (co-offending, commission of specific offences, three years imprisonment or more). The 4109 ‘organized crime’ offenders, identified by the process, were compared with ‘general’ and ‘serious’ offender control groups across a variety of personal and demographic variables. Organized crime prosecutions are not random but concentrate in specific geographic areas and constitute 0.2% of the offender population. Offenders can be differentiated from general crime offenders on such measures as: diversity of nationality and ethnicity, onset age, offence type and criminal recidivism. Using an offence based methodology, rather than relying on offenders identified through police proactive investigations, can provide empirical information from existing data sets, across a diverse range of legislative areas and cultures. This allows academics to enhance their analysis of organized crime, generating richer evidence on which policy makers and practitioners can more effectively deliver preventative and disruptive tactics.
AB - Organised Crime is notoriously difficult to identify and measure, resulting in limited empirical evidence to inform policy makers and practitioners. This study explores the feasibility of identifying a greater number of organized crime offenders, currently captured but invisible, within existing national general crime databases. All 2.1million recorded offenders, captured over a four year period on the UK Police National Computer (PNC), were filtered across three criteria associated with organized crime (co-offending, commission of specific offences, three years imprisonment or more). The 4109 ‘organized crime’ offenders, identified by the process, were compared with ‘general’ and ‘serious’ offender control groups across a variety of personal and demographic variables. Organized crime prosecutions are not random but concentrate in specific geographic areas and constitute 0.2% of the offender population. Offenders can be differentiated from general crime offenders on such measures as: diversity of nationality and ethnicity, onset age, offence type and criminal recidivism. Using an offence based methodology, rather than relying on offenders identified through police proactive investigations, can provide empirical information from existing data sets, across a diverse range of legislative areas and cultures. This allows academics to enhance their analysis of organized crime, generating richer evidence on which policy makers and practitioners can more effectively deliver preventative and disruptive tactics.
KW - Orgainsed crime
KW - Police National Computer
KW - Criminal careers
U2 - 10.1108/PIJPSM-03-2015-0024
DO - 10.1108/PIJPSM-03-2015-0024
M3 - Journal article
VL - 39
SP - 78
EP - 94
JO - Policing: An International Journal of Police Strategies and Management
JF - Policing: An International Journal of Police Strategies and Management
SN - 1363-951X
IS - 1
ER -