The Visualization of Uncertainty
2010
2:30 p.m.
Kristin Potter
Abstract
The graphical depiction of uncertainty information is emerging as a problem of great importance in the field of visualization. Scientific data sets are not considered complete without indications of error, accuracy, or levels of confidence, and this information is often presented as charts and tables alongside visual representations of the data. Â Uncertainty measures are often excluded from explicit representation within data visualizations because the increased visual complexity incurred can cause clutter, obscure the data display, and may lead to erroneous conclusions or false predictions.
However, uncertainty is an essential component of the data, and its display must be integrated in order for a visualization to be considered a true representation of the data. The growing need for the addition of qualitative information into the visual representation of data, and the challenges associated with that need, command fundamental research on the visualization of uncertainty.
This talk will discuss approaches for advancing the field of uncertainty visualization. First, extensions to traditional graphical data analysis techniques in order to convey uncertainty information will be presented, followed by common approaches in scientific and information visualization.
The challenges of creating new visual devices to handle the complexities of uncertainty data will be addressed and a framework for combing the most effective techniques for ensemble-type weather and climate simulation data will be presented. Finally, current research on brain classification data will be presented and ongoing challenges discussed.