Using big data analytics and genetic algorithms to predict street crime and optimise crime reduction measures
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Street crime and fear of street crime have significant adverse impacts on individual lives, the use and regeneration of urban areas, the ability to attract businesses and investment, the price of property, and the ability of citizens to live full and creative lives. This project will use the city of Glasgow as model to analyse multiple live and historic datasets (such as CCTV) to understand the pattern of crime in the city in new ways - potentially finding previously undiscovered relationships. It will create simulations and models that allow new approaches to be developed and tested for managing street environments to reduce crime - potentially including improved design of street lighting and soundscapes. As well as reduced street crime these strategies will seek to balance other objectives - such as lower service costs (e.g. from improved design of street lighting, and policing patterns), lower carbon emissions, and improved public confidence and acceptance. These strategies will then be tested through using the city as a living lab, testing solutions on real city streets - with the impact of different strategies being tested in a range of different situations.
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Potential Impact:
Who will benefit from this research?
- Local communities
- Policymakers
- Designers and operators of public services
- Commercial sector
How will they benefit from this research?
- Improved understanding of the patterns and causes of crime of city streets
- Improved understanding of which crime management/reduction policies are likely to effective in different contexts
- Reduced crime and fear of crime
- Improved citizen confidence, increased positive and creative use of city streets
- Improved investment, area regeneration and property prices due to reduced crime levels
- Improved design of streets and street based systems to help reduce crime
- Reduced carbon emissions and costs due to optimised operation of city systems (eg. street lighting)
- Improved citizen and community involvement in development and implementation of crime reduction measures
- Improved public acceptance and understanding of crime reduction measures
- Potential to develop commercial models that assist other city authorities to implement similar analytical and crime reduction measures
University of Strathclyde | LEAD_ORG |
Richard Bellingham | PI_PER |
John Quigley | COI_PER |
Francesco Sindico | COI_PER |
Maxim Fedorov | COI_PER |
Ivan Andonovic | COI_PER |
Robert Rogerson | COI_PER |
Subjects by relevance
- Towns and cities
- Crime
- Crimes
- Emissions
- Streets
- Urban design
- Street lighting
- Optimisation
- Creativity
- Criminology
- Enterprises
- Residents
Extracted key phrases
- Street crime
- Crime reduction measure
- Real city street
- Reduced crime level
- Crime management
- Street lighting
- Creative use
- Street environment
- Big datum analytic
- City system
- City authority
- Genetic algorithm
- Reduction policy
- Significant adverse impact
- Low carbon emission