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From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches

  • Kristin Potter
  • Paul Rosen
  • Chris R. Johnson
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 377)

Abstract

Quantifying uncertainty is an increasingly important topic across many domains. The uncertainties present in data come with many diverse representations having originated from a wide variety of disciplines. Communicating these uncertainties is a task often left to visualization without clear connection between the quantification and visualization. In this paper, we first identify frequently occurring types of uncertainty. Second, we connect those uncertainty representations to ones commonly used in visualization. We then look at various approaches to visualizing this uncertainty by partitioning the work based on the dimensionality of the data and the dimensionality of the uncertainty. We also discuss noteworthy exceptions to our taxonomy along with future research directions for the uncertainty visualization community.

Keywords

uncertainty visualization 

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Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Kristin Potter
    • 1
  • Paul Rosen
    • 1
  • Chris R. Johnson
    • 1
  1. 1.Scientific Computing and Imaging InstituteUniversity of UtahSalt Lake CityUSA

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