Accessible Visualization: Design Space, Opportunities, and Challenges

Kim, Nam Wook; Joyner, Shakila Cherise; Riegelhuth, Amalia; Kim, Yea-Seul

doi:10.1111/cgf.14298

Cited by 80 publications

(52 citation statements)

References 116 publications

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“…The lack of rationale for caption guidelines raises the need to analyze captions systematically. While the three-level model by Kim et al [8] guides people on how to scaffold visualization information in order, Lundgard et al [11] propose a concrete model that categorizes the content in a caption into four semantic levels: 1) elemental and encoded, 2) statistical and relational, 3) perceptual and cognitive, and 4) contextual and domain-specific (Table 1).…”

Section: Natural Language Models For Captionsmentioning

confidence: 99%

How Do Captions Affect Visualization Reading?

Zhu¹,

Cheng²,

Wu³

2022

Preprint

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Captions help readers better understand visualizations. However, if the visualization is intended to communicate specific features, should the caption be statistical, and focus on specific values, or perceptual, and focus on general patterns? Prior work has shown that when captions mention visually salient features, readers tend to recall those features. Still, we lack explicit guidelines for how to compose the appropriate caption. Further, what if the author wishes to emphasize a less salient feature?In this paper, we study how the visual salience of the feature described in a caption, and the semantic level of the caption description, affect a reader’s takeaways from line charts. For each single- or multi-line chart, we generate 4 captions that 1) describe either the primary or secondary salient feature in a chart, and 2) describe the feature either at the statistical or perceptual levels. We then show participants random chart-caption pairs and record their takeaways. We find that the primary salient feature is more memorable for single-line charts when the caption is expressed at the statistical level; for primary and secondary features in multi-line charts, the perceptual level is more memorable. We also find that many readers will tend to recall y-axis numerical values when a caption is present.

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Section: Natural Language Models For Captionsmentioning

confidence: 99%

How Do Captions Affect Visualization Reading?

Zhu¹,

Cheng²,

Wu³

2022

Preprint

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“…Largely, access issues other than vision that affect data visualization (such as cognitive/neurological, vestibular, and motor concerns) are almost entirely unserved in this research space. Kim et al found that 56 papers have been published between 1999 and 2020 that focus on vision‐related accessibility (not including color vision deficiency), with only 3 being published at a visualization venue (and only recently since 2018) [KJRK21]. Marriott et al found that there is no research at all that engages motor accessibility [MLB∗21].…”

Section: Existing Work In Data Visualization and Accessibilitymentioning

confidence: 99%

How accessible is my visualization? Evaluating visualization accessibility with Chartability

Elavsky

Bennett

Moritz

2022

Computer Graphics Forum

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Novices and experts have struggled to evaluate the accessibility of data visualizations because there are no common shared guidelines across environments, platforms, and contexts in which data visualizations are authored. Between non‐specific standards bodies like WCAG, emerging research, and guidelines from specific communities of practice, it is hard to organize knowledge on how to evaluate accessible data visualizations. We present Chartability, a set of heuristics synthesized from these various sources which enables designers, developers, researchers, and auditors to evaluate data‐driven visualizations and interfaces for visual, motor, vestibular, neurological, and cognitive accessibility. In this paper, we outline our process of making a set of heuristics and accessibility principles for Chartability and highlight key features in the auditing process. Working with participants on real projects, we found that data practitioners with a novice level of accessibility skills were more confident and found auditing to be easier after using Chartability. Expert accessibility practitioners were eager to integrate Chartability into their own work. Reflecting on Chartability's development and the preliminary user evaluation, we discuss tradeoffs of open projects, working with high‐risk evaluations like auditing projects in the wild, and challenge future research projects at the intersection of visualization and accessibility to consider the broad intersections of disabilities.

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“…While not aiming at supporting people with impairment specifically, several techniques have been proposed to automatically generate captions for visualizations [31,21,54,44,45,18,59,86,16]. A more thorough survey on accessible visualizations can be found [47].…”

Section: Visualization Accessibility For Visually Impaired Peoplementioning

confidence: 99%

Communicating Visualizations without Visuals: Investigation of Visualization Alternative Text for People with Visual Impairments

Jung¹,

Mehta²,

Kulkarni³

et al. 2021

Preprint

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Alternative text is critical in communicating graphics to people who are blind or have low vision. Especially for graphics that contain rich information, such as visualizations, poorly written or an absence of alternative texts can worsen the information access inequality for people with visual impairments. In this work, we consolidate existing guidelines and survey current practices to inspect to what extent current practices and recommendations are aligned. Then, to gain more insight into what people want in visualization alternative texts, we interviewed 22 people with visual impairments regarding their experience with visualizations and their information needs in alternative texts. The study findings suggest that participants actively try to construct an image of visualizations in their head while listening to alternative texts and wish to carry out visualization tasks (e.g., retrieve specific values) as sighted viewers would.The study also provides ample support for the need to reference the underlying data instead of visual elements to reduce users' cognitive burden. Informed by the study, we provide a set of recommendations to compose an informative alternative text.

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Accessible Visualization: Design Space, Opportunities, and Challenges

Cited by 80 publications

References 116 publications

How Do Captions Affect Visualization Reading?

How Do Captions Affect Visualization Reading?

How accessible is my visualization? Evaluating visualization accessibility with Chartability

Communicating Visualizations without Visuals: Investigation of Visualization Alternative Text for People with Visual Impairments

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