Now showing 1 - 4 of 4
  • Publication
    Challenging problems of geospatial visual analytics
    ( 2011)
    Andrienko, Gennady
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    Andrienko, Natalia
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    Keim, Daniel A.
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    MacEachren, Alan M.
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  • Publication
    A conceptual framework and taxonomy of techniques for analyzing movement
    ( 2011)
    Andrienko, Gennady
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    Andrienko, Natalia
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    Bak, Peter
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    Keim, Daniel A.
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    Kisilevich, S.
    ;
    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.
  • Publication
    Visual analytics tools for analysis of movement data
    ( 2007)
    Andrienko, Gennady
    ;
    Andrienko, Natalia
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    With widespread availability of low cost GPS devices, it is becoming possible to record data about the movement of people and objects at a large scale. While these data hide important knowledge for the optimization of location and mobility oriented infrastructures and services, by themselves they lack the necessary semantic embedding which would make fully automatic algorithmic analysis possible. At the same time, making the semantic link is easy for humans who however cannot deal well with massive amounts of data. In this paper, we argue that by using the right visual analytics tools for the analysis of massive collections of movement data, it is possible to effectively support human analysts in understanding movement behaviors and mobility patterns. We suggest a framework for analysis combining interactive visual displays, which are essential for supporting human perception, cognition, and reasoning, with database operations and computational methods, which are necessary for handling large amounts of data. We demonstrate the synergistic use of these techniques in case studies of two real datasets.
  • Publication
    Visual analytics methods for movement data
    ( 2007)
    Andrienko, Gennady
    ;
    Andrienko, Natalia
    ;
    Kopanakis, I.
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    Litgenberg, A.
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