Revealing uncertainty for information visualization

Abstract

Uncertainty in data occurs in domains ranging from natural science to medicine to computer science. By developing ways to include uncertainty in our information visualizations we can provide more accurate visual depictions of critical datasets. One hindrance to visualizing uncertainty is that we must first understand what uncertainty is and how it is expressed by users. We reviewed existing work from several domains on uncertainty and conducted qualitative interviews with 18 people from diverse domains who self-identified as working with uncertainty. We created a classification of uncertainty representing commonalities in uncertainty across domains and that will be useful for developing appropriate visualizations of uncertainty. Copyright 2008 ACM.