CRIME ANALYSIS MAPPING, INTRUSION DETECTION-USING DATA MINING

Abstract

In this work, crime mapping and analysis are performed using KNN and ANN algorithms to simplify the process of identifying crime patterns. Crime mapping initiatives are often supported and funded by the Office of Community Oriented Policing Services (COPS). Evidence based research methods help in analyzing crime data and understanding trends. In this study, crime percentages are calculated based on historical data using data mining techniques. Crime analysis combines both quantitative and qualitative data along with analytical methods to support investigation and decision making. For improving public safety, crime prediction and planning have become important research areas. Data mining techniques help in identifying locations where crimes occur most frequently. In crime analysis mapping, the following steps are followed to reduce crime rates: 1) collecting crime data, 2) organizing and grouping the data, 3) applying clustering techniques, and 4) forecasting future crime patterns. Crime analysis combined with crime mapping helps in understanding crime trends and supports police departments in preventing and controlling criminal activities effectively.