IEEE VIS 2024 Content: Visualizing Uncertainty in Sets

Visualizing Uncertainty in Sets

Christian Tominski -

Michael Behrisch -

Susanne Bleisch -

Sara Irina Fabrikant -

Eva Mayr -

Silvia Miksch -

Helen Purchase -

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DOI: 10.1109/MCG.2023.3300441

Room: Bayshore III

2024-10-16T16:48:00ZGMT-0600Change your timezone on the schedule page
2024-10-16T16:48:00Z
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Visualizing uncertainty in set-type data is crucial for accurate analysis and decision-making. This work introduces a framework that categorizes data characteristics and types of uncertainty, providing strategies for integrating uncertainty into visualizations. By addressing set membership, set attributes, and element attributes, the framework helps design effective visual representations that communicate both data and its inherent uncertainties. This approach not only aids in understanding complex datasets but also enhances decision-making in various applications, from academic course planning to complex scenarios like ensemble forecasting and gene mapping.
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Keywords

Uncertainty, Data Visualization, Measurement Uncertainty, Visual Analytics, Terminology, Task Analysis, Surveys, Conceptual Framework, Cardinality, Data Visualization, Visual Representation, Measure Of The Amount, Set Membership, Intersection Set, Visual Design, Different Types Of Uncertainty, Missing Values, Visual Methods, Fuzzy Set, Age Of Students, Color Values, Uncertainty Values, Explicit Representation, Aggregate Value, Exact Information, Uncertain Information, Table Cells, Temporal Uncertainty, Uncertain Data, Representation Of Uncertainty, Implicit Representation, Spatial Uncertainty, Point Symbol, Visual Clutter, Color Hue, Graphical Elements, Uncertain Value

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.