What is Chartability?
Chartability is a set of heuristics (testable questions) for ensuring that data visualizations, systems, and interfaces are accessible. Chartability is organized into principles with testable criteria and focused on creating an outcome that is an inclusive data experience for people with disabilities.
Chartability can help you find accessibility barriers in data visualizations
Being able to recognize which parts of a data visualization produce barriers for people with disabilities can be tough. But Chartability has compiled existing standards, research, and community best practices into a comprehensive set of things to look out for. Ideally, it can help folks who create visualizations really think deeply about accessibility when they design.
What is Accessibility?
Accessibility (also sometimes abbreviated as a11y) is the practice of ensuring that as many people as possible can use, understand, and have access to a technology, infrastructure, tool, product, or service.
What is Data Visualization?
Data Visualization (also sometimes abbreviated as dataviz or datavis) is presenting data in a structured, symbolic way. The structure and semantics go beyond the visual, however, so we prefer to call these data experiences.
With the massive rise in data-driven journalism, the ease and availability of charting and analytical solutions, and data’s near-ubiquitous appearance in public life, more thorough and robust accessibility considerations are overdue.
Chartability is organized into 7 principles, 4 common to the accessibility space: Perceivable, Operable, Understandable, and Robust (POUR) plus 3 more that extend from Robust: Compromising, Assistive, Flexible (CAF). These seven principles are used to focus explicitly on inclusive data experiences and the specific considerations produced by these challenging environments.
Why use Chartability?
Chartability helps the design of data experiences stick to standards-first (before venturing into novel, uncharted territory). Chartability is meant to be easy to get started, with a shortlist of 14 tests that can be conducted in 20-40 minutes (depending on the tester's experience). But Chartability's full test suite is also incredibly robust (50 heuristics total) and can integrate well into proper auditing work of complex systems.
Get the Workbook
The workbook for the latest version of Chartability lives at this link, which is always kept up to date. Currently, we also have a downloadable Microsoft Word version of Chartability's workbook, which should remain up to date with the online version. These workbooks are maintained from our single-source of truth at our POUR+CAF Github repo. That repo is also the place where we encourage community members to get involved and give feedback.
The workbook contains some very general information as well as a brief section on how to perform a "quick" style of audit using Chartability. Note that Chartability is a flexible material and is intended to assist professional auditors and teams or individuals looking to evaluate their own work as they go. In our own work on the professional side of the spectrum, we use Chartability to generate audit reports that generally take weeks of work and have between 100 and 200 pages of documented evidence, steps for reproduction, and recommendations for remediation. In our own work using Chartability for a "quick" style of audit, it is generally just a set of heuristics we keep in mind that add anywhere between 30 minutes and 2 hours of additional work on any given project.
Auditing is for Everyone
Because we have seen Chartability used effectively by individuals evaluating their own work, we highly recommend that every data visualization practitioner (at the bare minimum) learns to use Chartability as they design and engineer. It can be an immense time-saver and significantly reduces accessibility barriers for a very small overhead of additional work. But everyone, even scientists, data analysts, designers, and backend engineers can use Chartability too. We've also seen it work in amazing ways as a common conversation tool and set of standards within teams.
The best place to get started learning how to use Chartability is within the workbook itself! The workbook has a section, "How do you use Chartability?"" which provides an introduction to how someone might use Chartability to evaluate the accessibility barriers in their own work.
But Chartability is also something that can be integrated into larger auditing or research efforts as well, for folks who might be trained accessibility analysts or researchers looking to improve the thoroughness and quality of their methods. For example, Danyang Fan and colleagues have a great research paper, "The Accessibility of Data Visualizations on the Web for Screen Reader Users" where they adapted Chartability to suit their needs as part of a larger project, using it alongside other research methods.
Chartability's Series on Observable
We have a series for Chartability on Observable that is slowly rolling out as we get extra time. This is a collection of coding notebooks with live, editable examples of data experiences that engage Chartability at a deeper level. This series primarily focuses on techniques for designing and engineering for accessibility (not just auditing or evaluating). It may not help you learn how to use Chartability to evaluate visualizations but can be a great example of what data experiences that pass or fail are like and how to think more critically about the different aspects of their design.
Research on the Motivation and Design Process Behind Chartability
Elavsky, F. and Bennett, C. and Moritz, D. How accessible is my visualization? Evaluating visualization accessibility with Chartability. EuroVis 2022.
Talks, Workshops, and Podcasts with Chartability
[RECORDED] How to Integrate Accessibility into Your Data Viz Workflow (podcast), Frank Elavsky at Data Viz Today (May 2021)
[RECORDED] Accessibility in DataViz (podcast), Frank Elavsky at Tableau World Podcast (May 2021)
Entering Uncharted Territory: Accessible Data Visualization (talk/workshop), Frank Elavsky at Accessibility Camp Bay Area (May 2021)
Making Inclusive Charts (talk/workshop), Doug Schepers at AccessU (May 2021)
Accessibility is Critical to Data Visualization (talk/workshop), Frank Elavsky at NoVA UX (April 2021)
[RECORDED] How to Stop Designing Inaccessible Data Visualizations (talk/workshop), Frank Elavsky at IRE-NICAR (March 2021)
Introduction to Accessible Data Visualization (workshop), Frank Elavsky at Knowbility (December 2020)
Examples with Chartability
Vega-Lite Audit [alpha Chartability audit]
Chris DeMartini's Accessibility Journey [beta Chartability audit]
Example Chartability Audit [full beta Chartability audit]
"The Accessibility of Data Visualizations on the Web for Screen Reader Users," an academic project by Fan et al that adapts and integrates Chartability alongside their research methods.
General A11Y + Data Visualization Resources
The group, DatavizA11y maintains an excellent resources shortlist as well as a larger list of resources, updated occasionally.
While Chartability is very much the result of volunteer effort, there are services that we can provide related to data visualization and accessibility:
- Training: Do you have a team and need some training?
- Building: Are you looking to build inclusive data experiences and need specialized engineers and designers?
- Auditing: Do you want confidence in knowing your team's charts and diagrams are accessible and want a professional external audit?
If you're interested in any of these services, feel free to reach out to us at:
or call +1-919-932-9872.
With enough support we could develop improvements, training materials, tools, research, and more. There is already a lot of work being done in this space, but we could use some help.
Want to get involved?
Nothing about us, without us! Chartability has been vetted and tested by an array of people with disabilities, including folks who are blind, low-vision, have motor impairments, cognitive disability (Autism, vestibular disorders, and ADHD), and color vision deficiency. But even with all of that, it still could use more community contributors. The workbook’s online website is intended as the central source of collaboration and has a Github repo where community members can find out all the ways to get involved. The goal is for Chartability to grow into a community-driven, community-centered piece of work.
Credits and Thanks
Chartability was created by Frank Elavsky and is licensed under the CC-BY-SA (Creative Commons Attribution-ShareAlike 3.0 Unported) license.
Chartability is generously sponsored by Fizz Studio as well as by supporters of Frank Elavsky’s Patreon.
This project was possible thanks to a broad collaboration with many people:
Doug Schepers of Fizz Studio for being a great partner, sponsor, and guide.
Early testers and folks who contributed feedback (in no particular order): Chris DeMartini, Emily Kund, Amber Thomas (The Pudding), Øystein Moseng (Accessibility at Highcharts), Jennifer Zhang and Ryan Shugart (Accessibility at Microsoft), Silvia Canelón and Liz Hare (MiR), Dominik Moritz (Vega-Lite), and many others who have requested that they remain anonymous.
Core members of the DatavizA11y group not already listed.