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.