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From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches

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.
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Kristin Potter, Paul Rosen, Chris Johnson. From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches. 10th Working Conference on Uncertainty Quantification in Scientific Computing (WoCoUQ), Aug 2011, Boulder, CO, United States. pp.226-249, ⟨10.1007/978-3-642-32677-6_15⟩. ⟨hal-01518665⟩

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