Identifying high risk crime areas using topology

R. Frank, A.J. Park, P.L. Brantingham, Joseph Clare, K. Wuschke, M. Vajihollahi

    Research output: Chapter in Book/Conference paperConference paper

    3 Citations (Scopus)

    Abstract

    Computational criminology is an area of research
    that joins advanced theories in criminology with theories and
    methods in mathematics, computing science, geography and
    behavioural psychology. It is a multidisciplinary approach that
    takes the strengths of several disciplines and, with semantic
    challenges, builds new methods for the analysis of crime and
    crime patterns. This paper presents a developing algorithm
    for linking the geographic and cognitive psychology sides of
    criminology research with a prototype topology algorithm that
    joins local urban areas together using rules that define similarity
    between adjacent small units of analysis. The approach produces
    irregular shapes when mapped in a Euclidean space, but which
    follow expectations in a non-Euclidean topological sense. There
    are high local concentrations or hot spots of crime but frequently
    there is a sharp break on one side of the hot spot and with a
    gradual diffusion on the other. These shapes follow the cognitive
    psychological way of moving from one location to another without
    noticing gradual changes or conversely being aware of sharp
    changes from one location to the next. This article presents a
    pattern modeling approach that uses topology to spatially identify
    the concentrations of crime and their crisp breaks and gradual
    blending into adjacent areas using the basic components: interior,
    boundary and exterior. This topology algorithm is used to analyze
    crimes in a moderate sized city in British Columbia.
    Original languageEnglish
    Title of host publicationProceedings of the 2010 IEEE International Conference on Intelligence and Security Informatics.
    EditorsC.C. Yang, D. Zeng, K. Wang, A. Sanfillippo, H.H. Tsang, M-Y Day, U. Glasser, P.L. Brantingham, H. Chen
    Place of PublicationVancouver, BC Canada
    PublisherCPS/Elsevier
    Pages13-18
    Volume10.1109/ISI.2010.5484782
    EditionVancouver
    ISBN (Print)978 1 4244 6460 9
    Publication statusPublished - 2010
    EventIdentifying high risk crime areas using topology - Vancouver
    Duration: 1 Jan 2010 → …

    Conference

    ConferenceIdentifying high risk crime areas using topology
    Period1/01/10 → …

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