Crime Prediction

Crime prediction research aims to forecast the spatial and temporal occurrence of criminal activity using various data sources, including historical crime records, demographic information, and mobility patterns. Current research emphasizes the development of sophisticated machine learning models, such as graph neural networks, recurrent neural networks (like LSTMs and GRUs), and ensemble methods (like Random Forests), often incorporating spatial and temporal dependencies to improve prediction accuracy and address data sparsity and class imbalance. These advancements hold significant potential for enhancing public safety by optimizing resource allocation, informing proactive policing strategies, and improving the effectiveness of crime prevention programs.