Influencing operational policing strategy by predictive service analytics
| Autor: | Heather Callaghan, Melanie-Jane Stoneman, Christina Latsou, Lei Mao, Hanjing Zhang, Sarah J. Dunnett, Lisa M. Jackson |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Service (business)
Engineering Process management business.industry media_common.quotation_subject 05 social sciences 020207 software engineering 02 engineering and technology Officer Product (business) Knowledge extraction Analytics 0502 economics and business 0202 electrical engineering electronic engineering information engineering Quality (business) Marketing business Neighbourhood (mathematics) 050203 business & management Criminal justice media_common |
| Zdroj: | HICSS |
| Popis: | Everyday 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. |
| Databáze: | OpenAIRE |
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