Predicting the Type of Crime: Intelligence Gathering and Crime Analysis

Abstract

Crimes are expected to rise with an increase in population and the rising gap between society’s income levels. Crimes contribute to a significant portion of the socioeconomic loss to any society, not only through its indirect damage to the social fabric and peace but also the more direct negative impacts on the economy, social parameters, and reputation of a nation. Policing and other preventive resources are limited and have to be utilized. The conventional methods are being superseded by more modern approaches of machine learning algorithms capable of making predictions where the relationships between the features and the outcomes are complex. Making it possible for such algorithms to provide indicators of specific areas that may become criminal hot-spots. These predictions can be used by policymakers and police personals alike to make effective and informed strategies that can curtail criminal activities and contribute to the nation’s development. This paper aims to predict factors that most affected crimes in Saudi Arabia by developing a machine learning model to predict an acceptable output value. Our results show that FAMD as features selection methods showed more accuracy on machine learning classifiers than the PCA method. The naïve Bayes classifier performs better than other classifiers on both features selections methods with an accuracy of 97.53% for FAMD, and PCA equals to 97.10%.