Predicting Crime Scene Attendance

Abstract

This paper ascertains the feasibility of using a data mining supervised learning algorithm to predict which crime scenes potentially offer the best opportunity of recovering forensic samples such as finger prints or DNA. UK police forces have a finite number of Crime Scene Investigators and an ever-increasing demand to attend crime scenes. Most forces have a documented attendance criterion but crimes are not evenly distributed throughout the working day. This, at times, results in more scenes to examine than there are investigators, thereby posing the question, ‘Which scenes should be attended first in order to retrieve forensics?’. The Insightful Miner data mining workbench tool has been used to retrieve, combine, clean, manipulate and model the data. The results from Northamptonshire Police have demonstrated that the modelling process is able to predict crime scene attendance to an accuracy of 68 per cent which is significantly better than human experts. The methodology was used in a second police force (Gwent) which records data into different computer systems and the results were comparable to those achieved in Northamptonshire Police. This demonstrates that the methodology is portable between forces.