Crime forecasting using data mining techniques

Abstract

Crime affects our society dearly in many different ways. The major challenge faced by Law Enforcement agencies is quickly and efficiently analyzing voluminous amount of data for proper investigation process. To control such nuisance, a system is required that analyze data proficiently, determine hidden patterns in data and perform crime forecasting. Our research makes use of data mining techniques, such as; association mining, clustering, classification and regression to find hidden trends in data and predict crime. Association mining build rules on the basis of prominent trend or most recurring pattern found in data set. Clustering help predict crime by forming clusters of data and then classifying them to number of classes based on similarity or dissimilarity between crime incidents. Classification and regression is used for predicting crime events. In our research we followed divide and conquer approach by dividing problem into several analysis units and solving them separately. Data with appropriate attributes like days, holidays and lunar days was used for pattern identification, and crime counts along with economic information such as population, youth factor, employment rate, income rate and land characteristic was used for prediction techniques. Few important patterns (holidays v/s weekdays, Lunar v/s non-lunar days and major road networks) were analyzed by classification rule building and spatial and temporal clustering (clustering and outlier analysis, heat maps, hotspot analysis) was performed by statistical approaches amalgamated by data mining algorithms to identify crime vulnerable areas and also crime cold spots. Lastly crime prediction was performed by regression (crime counts) and classification (crime spectrums) to predict crime. MAPE lowest of 30% was achieved by linear regression and accuracy of above 80% was achieved in case of every classifier in predicting spectrums of crime on monthly data. Important finding was, how socioeconomic information when added to dataset affect crime prediction and made results accurate and meaningful.