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. 2024 Apr 17;19(4):e0296486.
doi: 10.1371/journal.pone.0296486. eCollection 2024.

Leveraging transfer learning with deep learning for crime prediction

Umair Muneer Butt  1   2 , Sukumar Letchmunan  1 , Fadratul Hafinaz Hassan  1 , Tieng Wei Koh  3
Affiliations

Leveraging transfer learning with deep learning for crime prediction

Umair Muneer Butt et al. PLoS One. .

Abstract

Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep learning methods, including Simple Moving Averages (SMA), Weighted Moving Averages (WMA), Exponential Moving Averages (EMA), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (BiLSTMs), and Convolutional Neural Networks and Long Short Term Memory (CNN-LSTM) for crime prediction. Primarily, this study proposed a BiLSTM based transfer learning architecture due to its high accuracy in predicting weekly and monthly crime trends. The transfer learning paradigm leverages the fine-tuned BiLSTM model to transfer crime knowledge from one neighbourhood to another. The proposed method is evaluated on Chicago, New York, and Lahore crime datasets. Experimental results demonstrate the superiority of transfer learning with BiLSTM, achieving low error values and reduced execution time. These prediction results can significantly enhance the efficiency of law enforcement agencies in controlling and preventing crime.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Change in the crime rate of Chicago city from 2001 to 2012.
Fig 2
Fig 2. Proposed methodology by leveraging transfer learning for crime prediction.
Fig 3
Fig 3. Crime distribution over the years in Chicago, New York, and Lahore.
Fig 4
Fig 4. A detailed architecture of BiLSTM for crime prediction.
Fig 5
Fig 5. District-wise crime prediction for a month and a week using statistical and deep learning models.
Fig 6
Fig 6. Borough-wise crime prediction for a month and week using statistical and deep learning models.
Fig 7
Fig 7. Town-wise crime prediction for a month and a week using statistical and deep learning models.
Fig 8
Fig 8. Crime prediction methodology using BiLSTM under transfer learning paradigm.
Fig 9
Fig 9. Architecture details of transfer learning using BiLSTM for crime prediction.
Fig 10
Fig 10. Line graph showing the optimization process using state-of-the-art optimizers for crime prediction.
Fig 11
Fig 11. Knowledge transfer from one district1 to district2, Brooklyn to Manhattan, and from Iqbal Town to Nishtar Town.
Fig 12
Fig 12. Comparison of datasets over execution time for a month using transfer learning.
See this image and copyright information in PMC

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