Using AI Techniques To Predict Property Crime Rates - A Comparison And Analysis
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Abstract
Property crime 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 property 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 study is to perform a comparative analysis on three different artificial intelligence techniques which are; Random Forest Classifier (RFC), Gradient Tree Boosting (GTB) and Support Vector Regression (SVR). These techniques are applied to four 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 GTB is the most effective method as it produced the lowest error measurements and the highest level of forecast accuracy in comparison to RFC and SVR
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