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Visual analytics for understanding spatial situations from episodic movement data

 
: Andrienko, Natalia; Andrienko, Gennady; Stange, Hendrik; Liebig, Thomas; Hecker, Dirk

:
Preprint urn:nbn:de:0011-n-2250905 (782 KByte PDF)
MD5 Fingerprint: c0a188d2e967426bacfbdaf63c927af9
The original publication is available at springerlink.com
Created on: 17.1.2013


Künstliche Intelligenz : KI 26 (2012), No.3, pp.241-251
ISSN: 0933-1875
ISSN: 1610-1987
English
Journal Article, Electronic Publication
Fraunhofer IAIS ()
georeferenced data; episodic movement data; position measurement; spatio-temporal aggregation

Abstract
Continuing advances in modern data acquisition techniques result in rapidly growing amounts of georeferenced data about moving objects and in emergence of new data types.We define episodic movement data as a new complex data type to be considered in the research fields relevant to data analysis. In episodic movement data, position measurements may be separated by large time gaps, in which the positions of the moving objects are unknown and cannot be reliably reconstructed. Many of the existing methods for movement analysis are designed for data with fine temporal resolution and cannot be applied to discontinuous trajectories. We present an approach utilising Visual Analytics methods to explore and understand the temporal variation of spatial situations derived from episodic movement data b y means of spatio-temporal aggregation. The situations are defined in terms of the presence of moving objects in different places and in terms of flows (collective movements) between the places. The approach, which combines interactive visual displays with clustering of the spatial situations, is presented by example of a real dataset collected by Bluetooth sensors.

: http://publica.fraunhofer.de/documents/N-225090.html