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Visual Analytics for Understanding Spatial Distributions and Spatial Variation

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

We begin with a simple motivating example that shows how putting spatial data on a map and seeing spatial relationships can help an analyst to make important discoveries. We consider possible contents and forms of spatial data, the ways of specifying spatial locations, and how to use spatial references for joining different datasets. We discuss the specifics of the (geographic) space and spatial phenomena, where spatial relationships within and between the phenomena play a crucial role. The First Law of Geography states: “Everything is related to everything else, but near things are more related than distant things”, emphasising the importance of distance relationships. However, the spatial context, which includes the properties of the underlying space and the things and phenomena existing around, often modifies these relationships; hence, everything related to space needs to be considered in its spatial context. We describe techniques for transforming and analysing spatial data and give an example of an analytical workflow where some of these techniques are used but, as usual, the main instruments of the analysis are human background knowledge, capability to see and interpret patterns, and reasoning.

Suggested Citation

  • Natalia Andrienko & Gennady Andrienko & Georg Fuchs & Aidan Slingsby & Cagatay Turkay & Stefan Wrobel, 2020. "Visual Analytics for Understanding Spatial Distributions and Spatial Variation," Springer Books, in: Visual Analytics for Data Scientists, chapter 0, pages 261-295, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-56146-8_9
    DOI: 10.1007/978-3-030-56146-8_9
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