Dataset specification.

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.

犯罪始终是关乎公众安全与安定环境保障的核心议题。学界已开展诸多犯罪预测相关研究，均强调采用深度学习方法以实现精准预测的重要价值。然而，训练前沿深度学习驱动的犯罪预测系统所需的充足犯罪数据与相关资源，仍是一项挑战。为解决这一问题，本研究采用迁移学习范式。此外，本研究对前沿统计与深度学习方法进行微调以用于犯罪预测，涵盖简单移动平均（Simple Moving Averages, SMA）、加权移动平均（Weighted Moving Averages, WMA）、指数移动平均（Exponential Moving Averages, EMA）、长短期记忆网络（Long Short Term Memory, LSTM）、双向长短期记忆网络（Bi-directional Long Short Term Memory, BiLSTMs）以及卷积神经网络-长短期记忆混合网络（Convolutional Neural Networks and Long Short Term Memory, CNN-LSTM）。核心而言，本研究提出一种基于双向长短期记忆网络的迁移学习架构，因其在周度与月度犯罪趋势预测中具备高精度表现。该迁移学习范式借助微调后的双向长短期记忆网络模型，实现犯罪知识从一个社区向另一社区的迁移。本研究在芝加哥、纽约与拉合尔的犯罪数据集上对所提方法进行评估。实验结果表明，结合双向长短期记忆网络的迁移学习方法性能更优，可实现更低的误差值与更短的运行时长。该预测结果可显著提升执法机构管控与预防犯罪的工作效率。