Geographic prediction of crimes against property using Neural Networks and the SARIMA model

Espinosa, Fredy Humberto Troncoso; Correa, Danilo Alberto Gomez

doi:10.47460/uct.v26i113.576

Cited by 1 publication

(1 citation statement)

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“…This section evaluates six state-of-the-art statistical and deep learning approaches for crime prediction. Primarily, three state-of-the-art evaluation measures, Mean Absolute Error (MAE), Median Absolute Deviation (MAD), and Mean Squared Error (MSE), are used [ 27 , 59 ]. Furthermore, three spatiotemporal crime datasets from Chicago [ 52 ], New York [ 53 ], and Lahore [ 54 ] are used for monthly and weekly crime predictions.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Leveraging transfer learning with deep learning for crime prediction

Butt,

Letchmunan,

Hassan

et al. 2024

PLoS ONE

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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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Section: Experimental Evaluationmentioning

confidence: 99%