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Title: Crime prediction and detection with data mining
Author: Grover, Vikas
Awarding Body: University of Portsmouth
Current Institution: University of Portsmouth
Date of Award: 2009
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Data mining technologies have been used by marketers to provide personalisation. In other words, the exact placement of the right offer to the right person at the right time. The police can apply this technique for providing the right inquiry to the right perpetrators at the right time, before or after person has committed a crime. The aim of this Thesis is to use data mining in operational policing for crime prediction and detection. Crime data contains rich information. However, it is inconsistent, incomplete and noisy thus making it difficult to get any useful information from it. The goal of this Thesis is to use data mining techniques on Police data, which could be used for analysis while making Police strategies to reduce the crime activities. Volume crimes (such as robbery) are difficult to analyse because of their high number and similarity between their Modus Operandi (MO). The methodological approach developed in this Thesis will help Police analysts to attribute undetected crimes to known offenders who may be responsible, with 72.9% to 93.57% accuracy, for committing the crime.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available