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Visual Analytics for Understanding Phenomena in Space and Time

In: Visual Analytics for Data Scientists

Author

Listed:
  • Natalia Andrienko

    (Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
    City, University of London, Northampton Square, Department of Computer Science)

  • Gennady Andrienko

    (Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
    City, University of London, Northampton Square, Department of Computer Science)

  • 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
    University of Bonn)

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.

Suggested Citation

  • Natalia Andrienko & Gennady Andrienko & Georg Fuchs & Aidan Slingsby & Cagatay Turkay & Stefan Wrobel, 2020. "Visual Analytics for Understanding Phenomena in Space and Time," Springer Books, in: Visual Analytics for Data Scientists, chapter 0, pages 297-340, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-56146-8_10
    DOI: 10.1007/978-3-030-56146-8_10
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