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Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy

The continued urbanization poses several challenges for law enforcement agencies to ensure a safe and secure environment. Countries are spending a substantial amount of their budgets to control and prevent crime. However, limited efforts have been made in the crime prediction area due to the deficie...

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Main Authors: Butt, Umair Muneer, Letchmunan, Sukumar, Hassan, Fadratul Hafinaz, Koh, Tieng Wei
Format: Article
Published: Public Library of Science (PLoS) 2022
Online Access:http://psasir.upm.edu.my/id/eprint/101753/
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0274172
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spelling my.upm.eprints.1017532024-05-02T06:50:46Z http://psasir.upm.edu.my/id/eprint/101753/ Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy Butt, Umair Muneer Letchmunan, Sukumar Hassan, Fadratul Hafinaz Koh, Tieng Wei The continued urbanization poses several challenges for law enforcement agencies to ensure a safe and secure environment. Countries are spending a substantial amount of their budgets to control and prevent crime. However, limited efforts have been made in the crime prediction area due to the deficiency of spatiotemporal crime data. Several machine learning, deep learning, and time series analysis techniques are exploited, but accuracy issues prevail. Thus, this study proposed a Bidirectional Long Short Term Memory (BiLSTM) and Exponential Smoothing (ES) hybrid for crime forecasting. The proposed technique is evaluated using New York City crime data from 2010-2017. The proposed approach outperformed as compared to state-of-the-art Seasonal Autoregressive Integrated Moving Averages (SARIMA) with low Mean Absolute Percentage Error (MAPE) (0.3738, 0.3891, 0.3433, 0.3964), Root Mean Square Error (RMSE)(13.146, 13.669, 13.104, 13.77), and Mean Absolute Error (MAE) (9.837, 10.896, 10.598, 10.721). Therefore, the proposed technique can help law enforcement agencies to prevent and control crime by forecasting crime patterns. Public Library of Science (PLoS) 2022 Article PeerReviewed Butt, Umair Muneer and Letchmunan, Sukumar and Hassan, Fadratul Hafinaz and Koh, Tieng Wei (2022) Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy. Public Library of Science, 17 (9). art. no. 274172. pp. 1-22. ISSN 1932-6203 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0274172 10.1371/journal.pone.0274172
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description The continued urbanization poses several challenges for law enforcement agencies to ensure a safe and secure environment. Countries are spending a substantial amount of their budgets to control and prevent crime. However, limited efforts have been made in the crime prediction area due to the deficiency of spatiotemporal crime data. Several machine learning, deep learning, and time series analysis techniques are exploited, but accuracy issues prevail. Thus, this study proposed a Bidirectional Long Short Term Memory (BiLSTM) and Exponential Smoothing (ES) hybrid for crime forecasting. The proposed technique is evaluated using New York City crime data from 2010-2017. The proposed approach outperformed as compared to state-of-the-art Seasonal Autoregressive Integrated Moving Averages (SARIMA) with low Mean Absolute Percentage Error (MAPE) (0.3738, 0.3891, 0.3433, 0.3964), Root Mean Square Error (RMSE)(13.146, 13.669, 13.104, 13.77), and Mean Absolute Error (MAE) (9.837, 10.896, 10.598, 10.721). Therefore, the proposed technique can help law enforcement agencies to prevent and control crime by forecasting crime patterns.
format Article
author Butt, Umair Muneer
Letchmunan, Sukumar
Hassan, Fadratul Hafinaz
Koh, Tieng Wei
spellingShingle Butt, Umair Muneer
Letchmunan, Sukumar
Hassan, Fadratul Hafinaz
Koh, Tieng Wei
Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy
author_facet Butt, Umair Muneer
Letchmunan, Sukumar
Hassan, Fadratul Hafinaz
Koh, Tieng Wei
author_sort Butt, Umair Muneer
title Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy
title_short Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy
title_full Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy
title_fullStr Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy
title_full_unstemmed Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy
title_sort hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy
publisher Public Library of Science (PLoS)
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/101753/
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0274172
_version_ 1800093767771881472
score 13.211869

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