K
Kristin Potter
Researcher at University of Utah
Publications - 31
Citations - 1483
Kristin Potter is an academic researcher from University of Utah. The author has contributed to research in topics: Visualization & Data visualization. The author has an hindex of 15, co-authored 31 publications receiving 1280 citations. Previous affiliations of Kristin Potter include Scientific Computing and Imaging Institute & University of Oregon.
Papers
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Proceedings ArticleDOI
Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data
Kristin Potter,Andrew T. Wilson,Peer-Timo Bremer,Dean N. Williams,Charles Doutriaux,Valerio Pascucci,Chris R. Johnson +6 more
TL;DR: This article argues that combining multiple linked displays yields a clearer presentation of the data and facilitates a greater level of visual data analysis, and demonstrates the framework using driving problems from climate modeling and meteorology and discusses generalizations to other fields.
Journal ArticleDOI
A user study of visualization effectiveness using EEG and cognitive load
Erik W. Anderson,Kristin Potter,Laura E. Matzen,J. F. Shepherd,Gilbert A. Preston,Cláudio T. Silva +5 more
TL;DR: This work presents an alternative approach to visualization evaluation in which brain activity is passively recorded using electroencephalography (EEG) to compare different visualization techniques in terms of the burden they place on a viewer's cognitive resources.
Book ChapterDOI
Overview and State-of-the-Art of Uncertainty Visualization
Georges-Pierre Bonneau,Hans-Christian Hege,Chris R. Johnson,Manuel M. Oliveira,Kristin Potter,Penny Rheingans,Thomas Schultz,Thomas Schultz +7 more
TL;DR: This chapter outlines sources and models of uncertainty, gives an overview of the state-of-the-art, provides general guidelines, outline small exemplary applications, and discusses open problems in uncertainty visualization.
Book ChapterDOI
From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches
TL;DR: This paper identifies frequently occurring types of uncertainty and connects those uncertainty representations to ones commonly used in visualization, and looks at various approaches to visualizing this uncertainty by partitioning the work based on the dimensionality of the data and the dimensionalities of the uncertainty.
Journal ArticleDOI
Visualizing summary statistics and uncertainty
TL;DR: This work takes inspiration from graphical data analysis to create visual representations that show not only the data value, but also important characteristics of the data including uncertainty, as well as an extension of the summary plot to two dimensional distributions.