Prof. Dr.

Wrobel, Stefan

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  • Publication
    From movement tracks through events to places: Extracting and characterizing significant places from mobility data
    ( 2011)
    Andrienko, Gennady
    ;
    Andrienko, Natalia
    ;
    Hurter, Christophe
    ;
    Rinzivillo, Salvatore
    ;
    Wrobel, Stefan
    We propose a visual analytics procedure for analyzing movement data, i.e., recorded tracks of moving objects. It is oriented to a class of problems where it is required to determine significant places on the basis of certain types of events occurring repeatedly in movement data. The procedure consists of four major steps: (1) event extraction from trajectories; (2) event clustering and extraction of relevant places; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps are scalable with respect to the amount of the data under analysis. We demonstrate the use of the procedure by example of two realworld problems requiring analysis at different spatial scales.
  • Publication
    A conceptual framework and taxonomy of techniques for analyzing movement
    ( 2011)
    Andrienko, Gennady
    ;
    Andrienko, Natalia
    ;
    Bak, Peter
    ;
    Keim, Daniel A.
    ;
    Kisilevich, S.
    ;
    Wrobel, Stefan
    Movement data link together space, time, and objects positioned in space and time. They hold valuable and multifaceted information about moving objects, properties of space and time as well as events and processes occurring in space and time. We present a conceptual framework that describes in a systematic and comprehensive way the possible types of information that can be extracted from movement data and on this basis defines the respective types of analytical tasks. Tasks are distinguished according to the type of information they target and according to the level of analysis, which may be elementary (i.e. addressing specific elements of a set) or synoptic (i.e. addressing a set or subsets). We also present a taxonomy of generic analytic techniques, in which the types of tasks are linked to the corresponding classes of techniques that can support fulfilling them. We include techniques from several research fields: visualization and visual analytics, geographic informa tion science, database technology, and data mining. We expect the taxonomy to be valuable for analysts and researchers. Analysts will receive guidance in choosing suitable analytic techniques for their data and tasks. Researchers will learn what approaches exist in different fields and compare or relate them to the approaches they are going to undertake.