CHS: Small: Developing a Probabilistic Grammar of Graphics for Flexible Uncertainty Visualization

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This project aims to build a more systematic understanding of data visualizations of uncertain information and to make it easier for data visualization designers to construct such visualizations. When reporting on high-stakes and uncertain topics ranging from elections to natural disasters, journalists routinely employ uncertainty visualizations in an attempt to help the public answer important questions, such as "Who will win the next presidential election?" or "Should I evacuate in the face of potential flooding?". For example, journalists use "cones of uncertainty" to illustrate hurricane path predictions, despite evidence that uncertainty cone visualizations are hard to interpret and better uncertainty visualizations exist. The adoption of more effective uncertainty visualizations in practice lags behind research, in part because the construction of more sophisticated and effective uncertainty visualizations is harder than the construction of common but less effective uncertainty visualizations like confidence intervals, while developing prototypes to explore the design space of uncertainty visualizations is costly. This project will create a formal description of a wide variety of uncertainty visualizations, a toolkit that uses this description to make the creation of such data visualizations easier, and evaluations of whether the toolkit makes it easier for designers to create visualizations of uncertain data.Formalizations of data visualization construction into algebras or grammars have sought to decrease the technical sophistication users need to explore a range of correct and effective visualizations. While several popular examples exist, none of these grammars formalize the notions of uncertainty or probability, requiring users to recombine statistical transformations and geometries (or worse, to transform input data outside of the visualization framework) in order to create effective uncertainty visualizations, including modern frequency-framing uncertainty visualizations like hypothetical outcome plots. This work proposes a probabilistic grammar of graphics that directly incorporates probability distributions to make it easier to explore the design space of uncertainty visualizations. The work will involve systematically cataloging existing uncertainty visualizations, developing a consistent grammar to describe them, then implementing that grammar in existing frameworks and evaluating its potential to make it easier for both experienced visualization designers and visualization design students to construct high quality uncertainty visualizations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.