Uncertainty visualization: Fundamentals and recent developments
David Hägele, Christoph Schulz, Cedric Aaron Beschle, Hannah Booth, Miriam Butt, Andrea Barth, Oliver Deußen, Daniel Weiskopf
Abstract
Abstract This paper provides a brief overview of uncertainty visualization along with some fundamental considerations on uncertainty propagation and modeling. Starting from the visualization pipeline, we discuss how the different stages along this pipeline can be affected by uncertainty and how they can deal with this and propagate uncertainty information to subsequent processing steps. We illustrate recent advances in the field with a number of examples from a wide range of applications: uncertainty visualization of hierarchical data, multivariate time series, stochastic partial differential equations, and data from linguistic annotation.