Innovations in Visualization

Visualizing Uncertainty

Torre Zuk
Sheelagh Carpendale
Christopher Collins


This research project will investigate how to represent visually the uncertainty that is inherent in many types of data and processes, and analyze how these interactive representations are utilized. Often data, or the manner in which it is acquired, has some type of uncertainty associated with it. This may be due to how it is collected in that instruments have limitations, or how it is generated in that simulations are often based on probabilities and so provide us with stochastic data. Even data free of uncertainty will often acquire uncertainty due to processing and viewing transformations that modify it. The process of interpretation or generalization from specific data also has inherent uncertainty as these processes usually contain non-deterministic mappings. The focus of this research will be in the domain of Evidence-based Medicine, in which probabilities and Bayesian inference are used to make decisions under uncertainty.

To visually integrate uncertainty into a visual representation that has more than one dimension is not straight forward. Therefore even though this information may be of critical importance to a user’s task, the uncertainty is regularly not presented. The importance of visualizing significant uncertainty is widely accepted, but no practical framework exists. Great amounts of effort are required to acquire, process, and analyze data, while the presentation of this information is usually done using existing or generic systems that may not lend themselves to the specific task. Often the data or meta-data has a level of uncertainty associated with it, or it may represent one potential outcome, and so it is important that a user has appropriate confidence in their interpretation. The goal of this research will be to determine and what and how visual representations can be provided and manipulated to enable users to understand and manage uncertainty effectively.


Visualizing Uncertainty in Lattices to Support Decision-Making


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Torre D. Zuk. Visualizing Uncertainty. PhD thesis, Department of Computer Science, University of Calgary, Calgary, Alberta, Canada, April, 2008. PDF Paper
Torre Zuk, Jon Downton, David Gray and Sheelagh Carpendale and J.D. Liang. Exploration of uncertainty in bidirectional vector fields. In Proc. SPIE & IS&T Conf. Electronic Imaging, Vol. 6809: Visualization and Data Analysis 2008, 6809. (Katy Börner and Matti T. Gröhn and Jinah Park and Jonathan C. Roberts, Ed.) SPIE, 68090B, 2008. PDF Paper
Torre Zuk and Sheelagh Carpendale. Visualization of Uncertainty and Reasoning. In Proceedings of the 7th International Symposium on Smart Graphics (June 25-27, 2007, Kyoto, Japan), 4569. (Berlin, Heidelberg), (Andreas Butz and Brian Fisher and Antonio Krüger and Patrick Olivier and Shigeru Owada, Ed.) Springer-Verlag, pages 164-177, 2007. PDF Paper
Christopher Collins, Sheelagh Carpendale and Gerald Penn. Visualizing Uncertainty in Lattices to Support Decision-Making. In Proceedings of Eurographics/IEEE VGTC Symposium on Visualization (EuroVis 2007). Eurographics Association, 2007. The definitive version is available at \url{}. PDF Paper Video File
Torre Zuk, M. Sheelagh T. Carpendale and W.D. Glanzman. Visualizing Temporal Uncertainty in 3D Virtual Reconstructions. In Proceedings of the 6th International Symposium on Virtual Reality, Archaeology and Cultural Heritage (VAST 2005). The Eurographics Association, pages 99-106, 2005. PDF Paper
Torre Zuk and M. Sheelagh T. Carpendale. Theoretical Analysis of Uncertainty Visualizations. In Proc. SPIE & IS&T Conf. Electronic Imaging, Vol. 6060: Visualization and Data Analysis 2006. (Robert F. Erbacher and Jonathan C. Roberts and Matti T. Gröhn and Katy Börner, Ed.) SPIE, page 606007, 2006. PDF Paper