International Research Journal of Education and Technology

The increase in crime data recording coupled with data analytics resulted in the growth of research approaches aimed at extracting knowledge from crime records to better understand criminal behaviour and ultimately prevent future crimes. While many of these approaches make use of clustering and association rule mining techniques, there are fewer approaches focusing on predictive models of crime. In this paper we explore models for predicting the frequency of several types of crimes prediction by LSOA code (Lower layer super output areas an administrative system of area for public) and the frequency of anti-social behaviour crimes. The crime face image and video data are from the police and contain over records before preprocessing. The results looking at predictive performance as well as processing time indicate that decision trees can be used to reliably predict image and video crime frequency in general, as well as anti-social behaviour frequency