Visualizing Uncertainty in Sets

Christian Tominski, Michael Behrisch, Susanne Bleisch, Sara Irina Fabrikant, Eva Mayr, Silvia Miksch, Helen Purchase

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Set visualization facilitates the exploration and analysis of set-type data. However, how sets should be visualized when the data are uncertain is still an open research challenge. To address the problem of depicting uncertainty in set visualization, we ask 1) which aspects of set type data can be affected by uncertainty and 2) which characteristics of uncertainty influence the visualization design. We answer these research questions by first describing a conceptual framework that brings together 1) the information that is primarily relevant in sets (i.e., set membership, set attributes, and element attributes) and 2) different plausible categories of (un)certainty (i.e., certainty, undefined uncertainty as a binary fact, and defined uncertainty as quantifiable measure). Following the structure of our framework, we systematically discuss basic visualization examples of integrating uncertainty in set visualizations. We draw on existing knowledge about general uncertainty visualization and previous evidence of its effectiveness.

Original languageEnglish
Pages (from-to)49-61
Number of pages13
JournalIEEE Computer Graphics and Applications
Volume43
Issue number5
Early online date1 Aug 2023
DOIs
Publication statusPublished - 1 Sept 2023

Keywords

  • Data visualization
  • Measurement uncertainty
  • Surveys
  • Task analysis
  • Terminology
  • Uncertainty
  • Visual analytics

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