Intelligent Automation of Crime Prediction using Data Mining

Crime Pattern Theory is a way of elucidating the reasons why specific types of crime happen at certain areas only. According to the theory, the offenders, rather than venturing into unknown territories, frequently commit crimes by taking advantage of the opportunities they encounter in the places of their comfort zones or the places they are most familiar with. Spatial analysis of crimes and areas of higher concentration assist in preventing or at least reducing the amount of future crime from the knowledge discovery of past data. Our approach of crime prediction consists of five modules, namely data extraction, pre-processing, classification, pattern identification, prediction and google map visualization. This paper compares machine learning algorithms namely naive bayes, bayesian networks, weighted k-nearest neighbors, multi-layer perceptron classifier, guassian naive bayes, decision tree, random forest, adaboost, gradient boosting, linear discriminant analysis and quadratic discriminant analysis for crime category identification and crime prediction. The results have been evaluated on a real dataset of crimes in the city of San Francisco. The work successfully uses the temporal and spatial information in the data to locate the crime hotspots, predict the category of a crime in a region and additionally, the emergence of the crimes.