The ability to predict crime is incredibly useful for police departments, city planners, and many other parties, but thus far current approaches have not made use of recent developments of machine learning techniques. In this paper, we present a novel approach to this task: Gaussian processes regression. Gaussian processes (GP) are a rich family of distributions that are able to learn functions. We train GPs on historic crime data to learn the underlying probability distribution of crime incidence to make predictions on future crime distributions.
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[v1] 2017-12-15 23:43:11
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