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chartability.html
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---
layout: chartability
permalink: /chartability/
title: Chartability
---
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<meta name="application-name" content="Chartability" />
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<meta property="og:image" content="http://www.frank.computer/images/projects/chartability.png" />
<title>Chartability</title>
<meta name="author" content="Frank Elavsky, Cynthia Bennett, and Dominik Moritz" />
<meta
property="og:title"
content="How accessible is my visualization? Evaluating visualization accessibility with Chartability."
/>
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<meta
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content="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."
/>
<meta
property="og:description"
content="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."
/>
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<header>
<a href="#main" class="top-link">Skip to main content</a
><a href="https://www.frank.computer/papers/2022-eurovis-chartability.pdf" class="top-link">Download the pdf</a
><a href="https://www.frank.computer" class="top-link">Go to frank.computer</a>
<nav>
<h1>Table of Contents</h1>
<ol>
<li><a href="#">Chartability</a></li>
<li>1. <a href="#introduction">Introduction</a></li>
<li>
2. <a href="#existing-work-in-data-visualization-and-accessibility">Existing Work</a>
<details>
<summary aria-label="More sections"></summary>
<ol>
<li>
2.1.
<a href="#research-advancements-in-data-visualization-and-accessibility"
>Research Advancements in Data Visualization and Accessibility</a
>
</li>
<li>
2.2.
<a href="#accessibility-practices-in-data-visualization-tools-and-libraries"
>Accessibility Practices in Data Visualization Tools and Libraries</a
>
</li>
<li>2.3. <a href="#accessibility-in-practice-broadly">Accessibility in Practice, Broadly</a></li>
<li>
2.4.
<a href="#using-heuristics-to-break-into-under-addressed-areas"
>Using Heuristics to Break Into Under-addressed Areas</a
>
</li>
</ol>
</details>
</li>
<li>
3. <a href="#making-chartability">Making Chartability</a>
<details>
<summary aria-label="More sections"></summary>
<ol>
<li>3.1. <a href="#previewing-chartability">Previewing Chartability’s 10 Critical Heuristics</a></li>
<li>3.2. <a href="#compromising">Compromising</a></li>
<li>3.3. <a href="#assistive">Assistive</a></li>
<li>3.4. <a href="#flexible">Flexible</a></li>
</ol>
</details>
</li>
<li>
4. <a href="#using-chartability">Using Chartability</a>
<details>
<summary aria-label="More sections"></summary>
<ol>
<li>
4.1. <a href="#visual-testing-perceivable">Visual Testing (Perceivable)</a>
<ol>
<li>📊 <a href="#Figure1">Figure 1</a></li>
<li>📊 <a href="#Figure2">Figure 2</a></li>
</ol>
</li>
<li>
4.2. <a href="#keyboard-probing-operable-assistive">Keyboard Probing (Operable, Assistive)</a>
<ol>
<li>📊 <a href="#Figure3">Figure 3</a></li>
</ol>
</li>
<li>
4.3.
<a href="#screen-reader-inspecting-perceivable-operable-robust-assistive"
>Screen Reader Inspecting (Perceivable, Operable, Robust, Assistive)
</a>
<ol>
<li>📊 <a href="#Figure4">Figure 4</a></li>
<li>📊 <a href="#Figure5">Figure 5</a></li>
<li>📊 <a href="#Figure6">Figure 6</a></li>
</ol>
</li>
<li>
4.4.
<a href="#checking-cognitive-barriers-understandable-compromising"
>Checking Cognitive Barriers (Understandable, Compromising)</a
>
<ol>
<li>📊 <a href="#Figure7">Figure 7</a></li>
</ol>
</li>
<li>
4.5.
<a href="#evaluating-context-robust-assistive-flexible"
>Evaluating Context (Robust, Assistive, Flexible)
</a>
<ol>
<li>📊 <a href="#Figure8">Figure 8</a></li>
</ol>
</li>
</ol>
</details>
</li>
<li>
5. <a href="#validating-chartability">Validating Chartability</a>
<details>
<summary aria-label="More sections"></summary>
<ol>
<li>
5.1.
<a href="#pre-validations-and-flipped-roles-participants-question-me"
>Pre-Validations and Flipped Roles: Participants Question Me</a
>
</li>
<li>5.2. <a href="#sec:critical heuristics">Discovering “Critical” Heuristics</a></li>
<li>5.3. <a href="#selecting-participants-and-projects">Selecting Participants and Projects</a></li>
</ol>
</details>
</li>
<li>
6. <a href="#study-results">Study Results</a>
<details>
<summary aria-label="More sections"></summary>
<ol>
<li>
6.1.
<a href="#real-access-has-more-considerations-than-colorblindness"
>Real Access has more Considerations than Colorblindness</a
>
</li>
<li>6.2. <a href="#audits-are-slow-but-help-me-focus">Audits are Slow, but Help me Focus</a></li>
<li>
6.3.
<a href="#chartability-helps-me-remember-and-stay-consistent"
>Chartability Helps me Remember and Stay Consistent</a
>
</li>
<li>
6.4.
<a href="#access-is-an-experience-not-just-compliance"
>Access is an Experience, not just Compliance</a
>
</li>
<li>
6.5.
<a href="#everyone-wants-more-evaluation-resources-and-tools"
>Everyone wants More Evaluation Resources and Tools</a
>
</li>
<li>
6.6.
<a href="#experts-novices-will-struggle.-novices-this-was-so-helpful"
>Experts: “Novices will Struggle.” Novices: “This was so helpful”</a
>
</li>
<li>
6.7. <a href="#what-about-auditors-with-disabilities">What about Auditors with Disabilities?</a>
</li>
</ol>
</details>
</li>
<li>7. <a href="#extended-results">Extended Results</a></li>
<li>8. <a href="#discussion">Discussion</a></li>
<li>9. <a href="#conclusion">Conclusion</a></li>
<li><a href="#acknowledgements">Acknowledgements</a></li>
<li><a href="#references">References</a></li>
</ol>
</nav>
</header>
<div class="main-wrapper">
<main id="main">
<div class="section" id="">
<h1 class="title">
How accessible is my visualization? Evaluating visualization accessibility with Chartability
</h1>
<p class="author">
Frank Elavsky,<span class="math inline"><sup>1</sup></span> Cynthia Bennett,<span class="math inline"
><sup>1</sup></span
>
and Dominik Moritz<span class="math inline"><sup>1</sup></span> <br />
<span class="math inline"><sup>1</sup></span
>Carnegie Mellon University, Human-Computer Interaction Institute<br />
</p>
<div class="abstract">
<div class="abstract-title">Abstract</div>
<p>
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.
</p>
</div>
</div>
<div class="section" id="introduction">
<h1>Introduction</h1>
<p>
26% of people in the United States self-report living with at least one disability <span
class="citation"
data-cites="cdc_disability_2018"
>[<a
href="#ref-Okoro2018Prevalence"
id="cdc_disability_201801"
aria-label="72, C. Okoro"
title="72, C. Okoro"
role="doc-noteref"
>72</a
>]</span
>. Of those, 13.7% live with a mobility disability and 10.8% with a cognitive disability. Globally, the
World Health Organization reports that 29% of the world lives with uncorrected or uncorrectable blindness,
low vision, or moderate to severe visual impairment <span
class="citation"
data-cites="noauthor_world_nodate"
>[<a
href="#ref-OrganizationWorld"
id="noauthor_world_nodate01"
aria-label="75, W. H. Organization"
title="75, W. H. Organization"
role="doc-noteref"
>75</a
>]</span
>. Access is a significant inclusion effort that has broad international impact, especially for data
visualization.
</p>
<p>
Accessibility is the practice of making information, content, and functionality fully available to and
usable by people with disabilities. As part of this process, practitioners need to be able to identify
accessibility barriers. While general accessibility standards help, evaluating the inaccessibility of
complex data systems can be a daunting and often expensive task. State-of-the art automated compliance
checkers only find 57% of accessibility errors <span class="citation" data-cites="noauthor_study_2021"
>[<a
href="#ref-Deque2021Automated"
id="noauthor_study_202101"
aria-label="28, Deque"
title="28, Deque"
role="doc-noteref"
>28</a
>]</span
>, meaning accessible experiences must still be manually designed and checked for quality. And following
standards may only account for up to half of the needs of people with disabilities
<span class="citation" data-cites="power_2012"
>[<a
href="#ref-Power2012Guidelines"
id="power_201201"
aria-label="77, C. Power"
title="77, C. Power"
role="doc-noteref"
>77</a
>]</span
>
anyway. Additionally, the intended wide applicability of these general standards means they fall short for
information-rich systems, such as data visualizations (which use size, color, angles, shapes, and other
dimensions to encode information). These specific contexts, communities, and libraries that deal with data
visualizations and information-rich interfaces often have their own tools and guidelines for use, but they
seldom include accessibility. Finally, research at the intersection of data visualization and accessibility
has yet to meaningfully permeate data visualization tools and communities and primarily focuses on blindness
and low vision, neglecting diverse accessibility needs of people with other disabilities.
</p>
<p>
Synthesizing evolving accessibility standards, research findings, and artifacts from communities of practice
into usable knowledge for a specific, evolving domain is a wicked problem. To address this, we present
Chartability. Chartability is an accessibility evaluation system specific to data visualizations and
interfaces which aims to help practitioners answer the question, “how accessible is my data visualization?”
Chartability organizes knowledge from disparate bodies of work into testable heuristics based on the
functional accessibility principles POUR (Perceivable, Operable, Understandable, and Robust) <span
class="citation"
data-cites="initiative_wai_accessibility_nodate"
>[<a
href="#ref-WAI2019Accessibility"
id="initiative_wai_accessibility_nodate01"
aria-label="98, WAI"
title="98, WAI"
role="doc-noteref"
>98</a
>]</span
>
and 3 novel principles CAF (Compromising, Assistive, and Flexible), which we added to attend to the unique
qualities and demands of data visualizations. We refer to these 7 heuristic principles as POUR+CAF.
Chartability is a community-contributed project that leverages the governance strategies of open source
projects as a way to address the complex dual-evolution of both accessibility and data interaction
practices.
</p>
<p>
We additionally present an initial, light evaluation of Chartability from the experience of practitioners
using it. We set out to see if using Chartability reduces the barrier of entry into this work for
accessibility novices and if accessibility experts had any feedback to share about its use. We gave
practitioners introductory material for Chartability and instructed them to use it according to their needs.
We found that before using Chartability only accessibility experts believed auditing data visualizations to
be somewhat easy or easy, while the other group believed auditing data visualizations to be somewhat hard or
hard. All novice accessibility practitioners became more confident after using Chartability and believed
auditing data visualizations for accessibility to be less difficult. Conversely while the expert
accessibility practitioners were already confident in their ability to evaluate accessibility (and all
unanimously had no change in their before and after evaluations), they were excited to adopt Chartability
into their set of auditing resources.
</p>
<p>
Our work sets out to acknowledge that data practitioners face significant barriers when first making data
visualizations, systems, and experiences accessible. While Chartability contributes to filling gaps and
organizing knowledge, it also challenges visualization and data interaction researchers to explore new
horizons of possibilities in this space. As such, we conclude with recommendations for future research at
the crossroads of data visualization and accessibility.
</p>
</div>
<div class="section" id="existing-work-in-data-visualization-and-accessibility">
<h1>Existing Work in Data Visualization and Accessibility</h1>
<p>
While recent works at the intersection of data visualization and accessibility are promising, they do not
provide a consistent and unified methodology for designers to evaluate the accessibility of their work
across the broad spectrum of disability considerations.
</p>
<div class="section" id="research-advancements-in-data-visualization-and-accessibility">
<h2>Research Advancements in Data Visualization and Accessibility</h2>
<p>
In parallel to Mack <span>et al</span>’s “What do we mean by Accessibility Research?” <span
class="citation"
data-cites="mack_what_2021"
>[<a
href="#ref-Mack2021What"
id="mack_what_202101"
aria-label="58, K. Mack"
title="58, K. Mack"
role="doc-noteref"
>58</a
>]</span
>
when we asked “What do we mean by data visualization accessibility research?” we found that nearly all
topics of study were vision-related. 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 <span>et al</span> 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) <span
class="citation"
data-cites="kim_accessible_2021"
>[<a