Crime Trends and Hotspot Analysis

Crime is an important part of public security and social stability. Crime pattern and hotspots analysis is crucial for operating law policing and policy making. This study investigates the evolution of crime patterns by exploring peak times and spatial distribution of crime. In this work, classical machine learning and clustering are employed to investigate historical crime data to describe actionable occurrences. GMM with PCA is used to identify the crime hotspots. The study shows variations in crime incidents, peak hours for crimes and crime hot spots. Based on these findings, targeted law enforcement intervention and allocation of resources are indicated. The findings offer important recommendations to law enforcement agencies, policymakers, and researchers for strengthening crime prevention approaches, sharpening patrolling tactics, and intensifying public safety initiatives. Further research can combine realtime data analysis and utilise state of the art deep learning techniques for improved crime prediction and surveillance.