Journal article Open Access

Designing Time Series Crime Prediction Model using Long Short-Term Memory Recurrent Neural Network

Tsion Eshetu Meskela; Yidnekachew Kibru Afework,; Nigus Asres Ayele; Muluken Wendwosen Teferi,; Tagele Berihun Mengist

Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)

Crime influences people in many ways. Prior studies have shown the relationship between time and crime incidence behavior. This research attempts to determine and examine the relationship between time, crime incidences types and locations by using one of the neural network models for time series data that is, Long Short-Term Memory network. The collected data is pre-processed, analyzed and tested using Long Short-Term Memory recurrent neural network model. R-square score is also used to test the accuracy. The study results show that applying Long Short-Term Memory Recurrent Neural Network (LSTM RNN) enables to come up with more accurate prediction about crime incidence occurrence with respect to time. Predicting crimes accurately helps to improve crime prevention and decision and advance the justice system.

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