• Admin Login
    View Item 
    •   Home
    • Theses and Dissertations
    • Theses and Dissertations
    • View Item
    •   Home
    • Theses and Dissertations
    • Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of WIRECommunitiesTitleAuthorsIssue DateSubmit DateSubjectsTypesJournalDepartmentPublisherThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsTypesJournalDepartmentPublisher

    Administrators

    Admin Login

    Local Links

    AboutThe University LibraryOpen Access Publications PolicyDeposit LicenceCOREWIRE Copyright and Reuse Information

    Statistics

    Display statistics

    The Use of Data Mining Techniques in Crime Trend Analysis and Offender Profiling

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Adderley PhD thesis.pdf
    Size:
    2.293Mb
    Format:
    PDF
    Download
    Authors
    Adderley, Richard
    Issue Date
    2007
    
    Metadata
    Show full item record
    Abstract
    The aim of this project is to ascertain whether the data in existing Police recording systems can be used by existing mature data mining techniques in an efficient manner to achieve results that are more accurate than those achieved by Police specialists when analysing crime. The Police Service has no formalised methodology of recording and analysing crime data and it is incumbent on each Force to train and develop appropriate personnel to provide operational analysis. Police data is inconsistent and, frequently, incomplete making the task of formal analysis far more difficult and current analytical practices are semi-manual and time consuming producing results of limited accuracy. These analytical processes would benefit from using data mining techniques within a structured approach as discussed within this thesis. The usage of supervised and unsupervised learning techniques within a structured methodology to mining Police data is evaluated. The research demonstrates that data mining techniques can be successfully used in operational policing. High volume crimes such as burglary that have been committed by one or more known offenders can be classified and the model used to attribute currently undetected crimes to one or more of those known offenders. Burglary crimes that previously had no overt relationship and the identity of the offender is unknown can be clustered with the ability to suggest one or more offenders who may be responsible for committing the crime. The same techniques used in analysing high volume crime can be used to link low volume major crimes such as serious sexual assaults. The recognised benefits include an improvement in the accuracy of results over current semi-manual processes and a reduction in the time taken to achieve those results.
    Publisher
    University of Wolverhampton
    URI
    http://hdl.handle.net/2436/15413
    Type
    Thesis or dissertation
    Language
    en
    Description
    A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy
    Collections
    Theses and Dissertations

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.