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A conceptual framework and taxonomy of techniques for analyzing movement

: Andrienko, Gennady; Andrienko, N.; Bak, P.; Keim, D.; Kisilevich, S.; Wrobel, S.

Preprint urn:nbn:de:0011-n-1699448 (1.0 MByte PDF)
MD5 Fingerprint: db28a7f284198bbdbf7841e4f8b522e9
Created on: 16.2.2012

Journal of visual languages and computing 22 (2011), No.3, pp.213-232
ISSN: 1045-926X
ISSN: 1095-8533
Journal Article, Electronic Publication
Fraunhofer IAIS ()
moving object; trajectory; movement data; visual analytics

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.