Visual Analytics for Understanding Phenomena in Space and Time

Natalia Andrienko, Gennady Andrienko, Georg Fuchs, Aidan Slingsby, Cagatay Turkay and Stefan Wrobel
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Natalia Andrienko: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
Gennady Andrienko: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
Georg Fuchs: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
Aidan Slingsby: City, University of London, Northampton Square, Department of Computer Science
Cagatay Turkay: University of Warwick, Centre for Interdisciplinary Methodologies
Stefan Wrobel: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven

Chapter Chapter 10 in Visual Analytics for Data Scientists, 2020, pp 297-340 from Springer

Abstract: Abstract There are different kinds of spatio-temporal phenomena, including events that occur at different locations, movements of discrete entities, changes of shapes and sizes of entities, changes of conditions at different places and overall situations across large areas. Spatio-temporal data may specify positions, times, and characteristics of spatial events, represent trajectories of moving entities, or consist of spatially referenced time series of attribute values. It is possible to transform spatiotemporal data from one of the forms to another and thus adapt them to analysis tasks. After presenting a motivating example of analysis, we discuss the specifics of spatio-temporal data and possible data quality issues that often appear in real data sets and influence analysis processes and results.We introduce and discuss the visual analytics techniques suitable for spatio-temporal data and present another example of an analytical workflow.

Date: 2020
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DOI: 10.1007/978-3-030-56146-8_10

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