Authors: Kristi Potter*,
Keywords: visualization, uncertainty
Session Type: Paper
Presentation File: No File Uploaded
Uncertainty is ubiquitous in scientific data. Aberrations such as variability, error, and missing data are key characteristics providing insights into the reliability of a given dataset. Without this information, data is often considered incomplete; however many visualizations do not include uncertainty due to increased complexity in the visual design. In my own work, I often encounter uncertainty stemming from large-scale, multi-run simulations where the variability between simulation runs reflects the range of possible outcomes. My approach to these problems often include multiple linked-windows, color mapping, and contouring, as well as more sophisticated, but domain-specific methods.
In this talk, I will go over the basics of uncertainty characterization and the challenges in including uncertainty in data visualization. I will briefly cover the types of uncertainty and the mathematical metrics most often used to measure uncertainty for visualization purposes including descriptive statistics and probability distributions. I will also provide a short history of uncertainty visualization techniques and a small subset of modern approaches that are easily applied in readily available software. Finally, I discuss my own work in ensemble visualization including the tools and techniques used to produce the resulting visualizations.