Spatio-temporal data analysis plays a central role in many security-related applications including those relevant to transportation infrastructure and border security. In this paper, we investigate prospective spatio-temporal analysis methods that aim to identify "unusual" clusters of events, or hotspots, in both spatial and temporal dimensions. We propose a support vector machine-based approach and compare it with a well-known prospective method based on space-time scan statistic using three problem scenarios. The first two scenarios are based on simulated data with known hotspots. The third scenario uses a real-world crime analysis data set involving vehicles.


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    Titel :

    Prospective spatio-temporal data analysis for security informatics


    Beteiligte:
    Wei Chang, (Autor:in) / Daniel Zeng, (Autor:in) / Hsinchun Chen, (Autor:in)


    Erscheinungsdatum :

    2005-01-01


    Format / Umfang :

    308752 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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