Crime Analysis and Prediction Using Enhanced Grading Tree Boosting

Abstract

A crime is an act that breaks the law of the state and is frowned upon by society. Criminality is an undesirable sensation that occurs in both developed and developing countries around the world. Anti-social behavior is considered a criminal offense. Violent crime data is an increasing concern worldwide and one which causes trauma to victims and puts pressure on law enforcement agencies. One essential weapon in the fight against violent crime is that of effective forecasting which helps law enforcement task forces to put in place effective preventative measures. Researchers and law enforcers now choose to harness the power of modern technology, namely artificial intelligence, to help them to predict property crime rates and to therefore create proactive preventative solutions. The objective of this paper is to perform a comparative analysis on different between Gradient Tree Boosting (GTB) and between Enhanced Gradient Tree Boosting (eGTB). These techniques are applied to separate crime types in the USA in order to compare and contrast in terms of quantitative measurement of error. The results of the study show that eGTB is the most effective method as it produced the lowest error measurements and the highest level of forecast accuracy in comparison to GTB.