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Uncertainty visualization: Fundamentals and recent developments

David Hägele, Christoph Schulz, Cedric Beschle, Hannah Booth, Miriam Butt, Andrea Barth, Oliver Deussen and Daniel Weiskopf

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.

Funding source: Deutsche Forschungsgemeinschaft

Award Identifier / Grant number: 251654672—TRR 161

Funding source: Fonds Wetenschappelijk Onderzoek

Award Identifier / Grant number: 12ZL522N

Funding statement: This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project ID 251654672—TRR 161 (Projects A01, B07, and D02). Hannah Booth is funded by a postdoctoral fellowship from the Research Foundation – Flanders (FWO), Grant no. 12ZL522N.

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Received: 2022-05-17
Revised: 2022-08-12
Accepted: 2022-08-13
Published Online: 2022-08-31
Published in Print: 2022-08-26

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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