From Quantification To Visualization: A Taxonomy Of Uncertainty Visualization Approaches

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.

Kristin Potter, Paul Rosen, and Chris R Johnson. From Quantification To Visualization: A Taxonomy Of Uncertainty Visualization Approaches. WoCoUQ 2011: Uncertainty Quantification in Scientific Computing, 2012.

@inproceedings{potter2012quantification,
  title = {From Quantification to Visualization: A Taxonomy of Uncertainty Visualization
    Approaches},
  author = {Potter, Kristin and Rosen, Paul and Johnson, Chris R},
  booktitle = {WoCoUQ 2011: Uncertainty Quantification in Scientific Computing},
  series = {IFIP Advances in Information and Communication Technology},
  volume = {377},
  pages = {226--249},
  year = {2012},
  keywords = {uncertainty visualization},
  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.}
}