Crime Analysis and Prediction using Data Mining

Abstract

Crime analysis and prediction is systematic way for detecting crime, analyzing patterns of crime and predicting crime trends. Data mining, it is suitable method to apply on large quantity of crime dataset and information obtained from data mining technique is very effective that support law enforcement officers. Our system can detect and predict areas which are crime prone and have high probability for crime occurrence and can visualize crime prone areas in radius. The results of this method can be used to increase people’s awareness related to the locations that are dangerous and to assist police force to predict crimes that will occur in the future in a target area within a target time. With the rapid increase and usages of computerized and informational systems, data analysts of crime can assist the police forces to fasten up the process of crime solving in our community. It is interesting to know that about 15% of the criminals commits 40% of the total occurrence. Even though it is not possible to estimate who may be the victims of a crime but we can estimate the place that has high chance for crime occurrence. K-means algorithm, is popular for cluster analysis in data mining, is done by splitting data into different subset based on their different means. Expectation - maximization algorithm is an extension where it is possible to split the data based on their available parameters. Data mining framework which is easy to implement, works with the geospatial plot of crime and criminal activities and assists to increase the effectiveness of the police forces and other law enforcement agencies. Our developed system can also be applied for the Bangladesh crime departments, by doing this the Bangladesh police can reduce the crime and solve the crimes with minimum time.