Application of Long Short-Term Memory to Crime Prediction for Community Safety

The inability to anticipate crime trends dynamically and in real time results in suboptimal resource allocation and delayed response times, undermining public safety efforts. Furthermore, the complex, non-linear nature of crime data necessitates advanced analytical techniques capable of capturing temporal dependencies and predicting future occurrences accurately. There is a pressing need for an innovative solution that leverages advanced machine learning techniques, particularly LSTM neural networks, to predict criminal activities with high accuracy and facilitate proactive law enforcement strategies. This research aimed to develop a predictive justice framework using Long Short-Term Memory (LSTM) neural networks to forecast criminal activities and enhance community safety. The research utilized a comprehensive dataset that included temporal, spatial, and socio-economic variables to train the LSTM model, which was fine-tuned for optimal accuracy. The results demonstrated that the LSTM model outperformed other machine learning models, such as Random Forest and Support Vector Machine (SVM), in predicting criminal activities with high precision, recall, and F1-score. The research highlighted the potential of LSTM networks in predictive policing, providing a tool that can help law enforcement agencies better allocate resources and prevent crimes.