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Influencing operational policing strategy by predictive service analytics
conference contribution
posted on 2017-10-19, 13:47 authored by Lisa JacksonLisa Jackson, Melanie-Jane Stoneman, Heather CallaghanHeather Callaghan, Hanjing Zhang, Christina Latsou, Sarah DunnettSarah Dunnett, Lei MaoEveryday there are growing pressures to ensure that services are delivered efficiently, with high levels of quality and with acceptability of regulatory standards. For the Police Force, their service requirement is to the public, with the police officer presence being the most visible product of this criminal justice provision. Using historical data from over 10 years of operation, this research demonstrates the benefits of using data mining methods for knowledge discovery in regards to the crime and incident related elements which impact on the Police Force service provision. In the UK, a Force operates over a designated region (macro-level), which is further subdivided into Beats (micro-level). This research also demonstrates differences between the outputs of micro-level and macro-level analytics, where the lower level analysis enables adaptation of the operational Policing strategy. The evidence base provided through the analysis supports decisions regarding further investigations into the capability of flexible neighbourhood policing practices; alongside wider operations i.e. optimal officer training times.
History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Aeronautical and Automotive Engineering
Published in
Hawaii International Conference on System ScienceCitation
JACKSON, L.M. ... et al, 2017. Influencing operational policing strategy by predictive service analytics. Presented at the Hawaii International Conference on System Sciences (HICSS-51), Hawaii, 3rd-6th January 2018.Publisher
University of Hawaii at Manoa © The AuthorsVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2017-09-22Publication date
2017Notes
This is a conference paper.Publisher version
Language
- en