Crime Pattern

Crime pattern analysis aims to understand and predict criminal activity using various data sources, including police reports, social media, and environmental factors. Current research heavily utilizes machine learning, employing models like graph neural networks, random forests, and deep learning architectures to identify spatial and temporal crime patterns, often incorporating zero-inflated models to handle data sparsity. These advancements improve crime prediction accuracy and inform resource allocation for law enforcement, contributing to enhanced public safety and more effective crime prevention strategies. Furthermore, research increasingly emphasizes explainability and the incorporation of diverse data sources to provide a more comprehensive understanding of crime dynamics.