A Design Space of Vision Science Methods for Visualization Research

Elliott, Madison; Nothelfer, Christine; Xiong, Cindy; Szafir, Danielle Albers

doi:10.1109/tvcg.2020.3029413

Cited by 29 publications

(26 citation statements)

References 76 publications

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“…Prior work demonstrates that the perception of and interaction with visualizations can be successfully studied in isolation [6]. Examples in the realm of physicalizations include the perception of physical size [18], the influence of orientation on perception [29] and the interaction with physical data points [14].…”

Section: Rationale For Methodologymentioning

confidence: 99%

“…Fourth, our study uses a non-interactive 'data-agnostic' approach as used in prior work that studied aspects of physicalization in isolation [14,18,29]. It is also motivated by work from Elliott et al [6] which describes experimental methods to study visualizations systematically by breaking them up into specific isolated topics and paradigms. An inherent limitation of this approach and methodology is the removal of context, as we studied reconfiguration independently from data actions with a real dataset.…”

Section: Limitationsmentioning

confidence: 99%

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Reconfiguration Strategies with Composite Data Physicalizations

Sauvé

Verweij²,

Alexander

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Section: Rationale For Methodologymentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Reconfiguration Strategies with Composite Data Physicalizations

Sauvé

Verweij²,

Alexander

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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“…Some design spaces in visualization literature for other domains are based on an abstraction of tasks [ENXS20, LJS20]. However, this is not easy since the choice of design options does not only depend on the task, but also on the user and the data.…”

Section: Design Space Of Oddvmentioning

confidence: 99%

Design Space of Origin‐Destination Data Visualization

Tennekes

Chen

2021

Computer Graphics Forum

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Visualization is an essential tool for observing and analyzing origin‐destination (OD) data, which encodes flows between geographic locations, e.g., in applications concerning commuting, migration, and transport of goods. However, depicting OD data often encounter issues of cluttering and occlusion. To address these issues, many visual designs feature data abstraction and visual abstraction, such as node aggregation and edge bundling, resulting in information loss. The recent theoretical and empirical developments in visualization have substantiated the merits of such abstraction, while confirming that viewers' knowledge can alleviate the negative impact due to information loss. It is thus desirable to map out different ways of losing and adding information in origin‐destination data visualization (ODDV). We therefore formulate a new design space of ODDV based on the categorization of informative operations on OD data in data abstraction and visual abstraction. We apply this design space to existing ODDV methods, outline strategies for exploring the design space, and suggest ideas for further exploration.

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“…All participants completed all trials of each study. We followed standard signal-detection methodology for recording participant responses 42 : participants pressed ''E'' to indicate that they saw an emphasized item, and ''N'' to indicate that there was no emphasized item. In trials where participants responded that an emphasis effect was present, a pop-up box appeared, asking participants to rate how different the emphasized point was from the other points (e.g.…”

Section: Stimuli: Twelve Emphasis Effectsmentioning

confidence: 99%

Which emphasis technique to use? Perception of emphasis techniques with varying distractors, backgrounds, and visualization types

Mairena

Gutwin

Cockburn

2021

Information Visualization

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Emphasis effects are visual changes that make data elements distinct from their surroundings. Designers may use computational saliency models to predict how a viewer’s attention will be guided by a specific effect; however, although saliency models provide a foundational understanding of emphasis perception, they only cover specific visual effects in abstract conditions. To address these limitations, we carried out crowdsourced studies that evaluate emphasis perception in a wider range of conditions than previously studied. We varied effect magnitude, distractor number and type, background, and visualization type, and measured the perceived emphasis of 12 visual effects. Our results show that there are perceptual commonalities of emphasis across a wide range of environments, but also that there are limitations on perceptibility for some effects, dependent on a visualization’s background or type. We developed a model of emphasis predictability based on simple scatterplots that can be extended to other viewing conditions. Our studies provide designers with new understanding of how viewers experience emphasis in realistic visualization settings.

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A Design Space of Vision Science Methods for Visualization Research

Cited by 29 publications

References 76 publications

Reconfiguration Strategies with Composite Data Physicalizations

Reconfiguration Strategies with Composite Data Physicalizations

Design Space of Origin‐Destination Data Visualization

Which emphasis technique to use? Perception of emphasis techniques with varying distractors, backgrounds, and visualization types

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