Evaluation of Modern Crime Prediction Techniques

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M.Vijaya Kumar
Dr .C. Chandrasekar


Police analysts are required to unravel the complexities in data to assist operational personnel in arresting offenders and directing
crime prevention strategies. However, the volume of crime that is being committed and the awareness of modern criminals make this a daunting
task. The ability to analyse this amount of data with its inherent complexities without using computational support puts a strain on human
resources. This paper examines the current techniques that are used to predict crime and criminality. Over time, these techniques have been
refined and have achieved limited success. They are concentrated into three categories: statistical methods, these mainly relate to the journey to
crime, age of offending and offending behaviour; techniques using geographical information systems that identify crime hot spots, repeat
victimisation, crime attractors and crime generators; a miscellaneous group which includes machine learning techniques to identify patterns in
criminal behaviour and studies involving re-offending. The majority of current techniques involve the prediction of either a single offender's
criminality or a single crime type's next offence. These results are of only limited use in practical policing. It is our contention that Knowledge
Discovery in Databases should be used on all crime types together with offender data, as a whole, to predict crime and criminality within a small
geographical area of a police force.



Key Words: crime hot spots, repeat victimisation, Crime Pattern Theory, Statistical Methods.


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