Designing Time Series Crime Prediction Model using Long Short-Term Memory Recurrent Neural Network
Tsion Eshetu Meskela1, Yidnekachew Kibru Afework2, Nigus Asres Ayele3, Muluken Wendwosen Teferi4, Tagele Berihun Mengist5
1Tsion Eshetu Meskela, Department of Computer Science and Engineering, Adama Science and Technology University, Adama, Ethiopia.
2Yidnekachew Kibru Afework, Department of Information Systems, Wolkite University, Wolkite, Ethiopia.
3Nigus Asres Ayele, Department of Information Technology, Wolkite University, Wolkite, Ethiopia.
4Muluken Wendwosen Teferi, Department of Information Technology, Wolkite University, Wolkite, Ethiopia.
5Tagele Berihun Mengist, Department of Software Engineering, Wolkite University, Wolkite, Ethiopia.
Manuscript received on October 06, 2020. | Revised Manuscript received on October 25, 2020. | Manuscript published on November 30, 2020. | PP: 402-405 | Volume-9 Issue-4, November 2020. | Retrieval Number: 100.1/ijrte.D5025119420 | DOI: 10.35940/ijrte.D5025.119420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Crime Prediction, LSTM, RNN, Predictive Policing, Time-Series Prediction.