Crime Analysis, Hotspot Detection, and Prediction Using Data Mining Techniques

Abstract

Crime has become one of the major social challenges affecting public safety, economic development, and the overall quality of life. The increasing volume of crime-related data generated by law enforcement agencies provides an opportunity to apply data mining techniques for effective crime analysis and prediction. Crime analysis involves identifying patterns, trends, and relationships among criminal activities, while hotspot detection focuses on locating geographical areas with high crime concentrations. Predictive analytics can further assist in forecasting future crime occurrences based on historical data. This research presents a comprehensive framework for crime analysis, hotspot detection, and crime prediction using data mining techniques. Various algorithms such as classification, clustering, association rule mining, and predictive modeling are discussed for extracting meaningful insights from crime datasets. The proposed system helps law enforcement agencies identify crime-prone regions, allocate resources efficiently, and implement preventive measures to enhance community safety.

Keywords