Law enforcement agencies are an excellent source of large amounts of data. Intelligence requires information or data and a methodology for analyzing the data. Police and other forces of civil order would benefit directly from intelligence to help combat all forms of crime and intelligence is a requirement for managing and fighting “intelligent” forms of crime ( Tastle, 2013). While increased police presence has been documented to reduce increases in crime ( Chalfin and McCrary, 2018), police need to be able to plan for and respond effectively to criminal events, especially those that affect personal or public safety. The ever increasing population along with the rise in urbanization has led to dramatic increases in criminal activities ( Fajnzylber et al., 2002 Zhang, 2016), particularly in urban settings ( Tumulak and Espinosa, 2017 Stebbins, 2019 Zhu et al., 2019). The location prediction neural networks are able to predict the zip code location or adjacent location 31.2% of the time.Ĭrime is a global concern that impacts individuals and society on a daily basis and negatively affects society ( Costa, 2010). The neural network models are able to predict the type of crime being committed 16.4% of the time for 27 different types of crime or 27.1% of the time when similar crimes are grouped into seven categories of crime. The neural network crime prediction models utilize geo-spatiality to provide immediate information on crimes to enhance law enforcement decision making. Neural network models to predict specific types of crime using location and time information and to predict a crime’s location when given the crime and time of day are developed to demonstrate the application of neural networks to police decision making. This article reviews and examines existing research on the utilization of neural networks for forecasting crime and other police decision making problem solving. They have also been shown to be a useful tool for working with big data oriented environments such as law enforcement. Neural networks are a machine learning method that excel in solving classification and forecasting problems.
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