A guide for creating accessible information visualizations for visually impaired & colorblind users

Part one:

Background

I decided to create this guide because I am a visually impaired and colorblind data science student. I was born with incomplete achromatopsia, a non-progressive genetic condition that limits the functioning of cones in the retina. This leads to color-vision deficiency, a high level of vision impairment (legal blindness), and severe light sensitivity. (Achromatopsia: Color Blindness and Other Vision Issues, 2022) Throughout my life, I have come across countless information visualizations that I was unable to fully understand due to inaccessible design. The frustration, disappointment, and shame of wanting to know what message the data is conveying, but being unable to is something I want to combat in my academic and professional practice. This was one of the reasons I chose to study data science, to work toward improving accessibility and inclusivity throughout the field. 

This guide is meant to be an overview of current understandings and best practices for visually-inclusive design in information visualizations. I will highlight what I found to be most helpful and provide a summary of key points. In the resources section of this site, I have linked to many different resources for anyone interested in learning more about these topics.

Purpose (aka why should fully-sighted people care about this?) 

The question of why we ought to care about accessible design may seem obvious, and yet the lack of inclusivity in the industry points to a widespread indifference to this topic. Perhaps it is difficult to conceptualize why someone with limited vision would be interested in viewing and interacting with visualizations, but the reality is that we (blind people) read the news, browse the web, and go about our academic and professional lives just like everyone else. And we would love to access the rich information that visualizations can provide!

Did you know that there are still no official industry standards for accessible design in information and data visualization? Although the W3C has published standards for general web design, information visualization designers and audiences have unique needs that should be addressed with agreed-upon industry standards documentation. Melanie Mazanec (2020) explains,

’‘Even though the [W3C] standards say so little about this topic, we in the data visualization community often fail to meet this low bar— especially when it comes to dashboards. While charts in the context of reports or articles come wrapped in a long description, business intelligence tools make it frictionless to throw together a few visualizations without any captions or tables. By not considering all users when we create dashboards, which are usually internally-facing, we risk creating workflows that require the use of inaccessible tools and precluding organizations from hiring disabled employees.”

In other words, inclusive design leads to more inclusive, connected, and collaborative environments, which is a win-win for everyone.  We hold open doors for eachother so that we too can experience belonging. Our design practices can reflect this very human instinct.

Barone & Elavsky (2023) reminds us that widespread systemic failures of accessibility are no one person's fault nor burden. It is only through a collective mindset shift towards inclusivity that these problems can be overcome.

Accessibility & Inclusivity

So what does inclusivity actually mean? And [how] it is different than accessibility? Inclusive design is a methodology that seeks to create objects and environments that are usable for all types of individuals. It posits that there is no ‘standard’ or ‘normal’ users that others differ from. Instead, every person has various and interconnected factors that affect how they access and use things. On a personal note, I cannot express in words how I felt the first time that I read `there is no such thing as a normal user.` It perfectly encapsulates the power of inclusive design, because every single time I came across a data visualization I could not understand, I felt precisely ‘abnormal.’ From the Inclusive Learning Design Handbook (n.d.) we can understand that “inclusivity provides a spectrum of tools and features that the end user can choose from to fit his or her requirements in the given context. In short, Inclusiveness is not prescriptive since the user chooses how best to help themselves.”

Accessible design focuses on the specific needs of disabled people and what standards and principles can be implemented to ensure equitable access. As is the case with all forms of equity, disabled users may need additional resources to achieve similar results as non-disabled users. In terms of information visualization, this may mean including additional elements into visualization design.

Spectrum of Visual Impairments

When designing inclusive visualizations for visually-impaired users, one of the most foundational realities that one must navigate is that vision and vision impairment (including color deficiency) is a broad spectrum. I have included below a graphic adapted from The Visually Impaired Designer‘s thesis because it is useful for understanding different levels of visual impairment and common terminology. For context, my best-corrected vision is 20/200 so I'm classified as “legally blind.” Functionally, this means that I must be 20 feet away from an object to see it at the same acuity as someone with ‘full’ vision (20/20) can see from 200 feet away.

Common issues faced by visually impaired users when interacting with information visualizations

There are a core set of problems often found in information visualizations that make them inaccessible to different users. Having low contrast (light image on light background or dark-on-dark) makes it difficult or impossib;e to distinguish different elements of a visualization. I came across this recently when viewing a New York Times data story which included a map that was so faint against the background that at first I didn't even realize it was there.

Another major issue, and my personal greatest source of frustration when viewing visualizations, is encoding data by color without direct labelling. If I must rely on a color-coded legend alone to read a visualization, it is essentially indecipherable to me.

Having low-quality images in visualization or not being able to zoom-in can also be a major hinderance. The remedy for this, having data that is zoomable to different levels of detail actually benefits all audiences- another score for the philosophy behind inclusive design! In fact, you may notice that all of these issues can be solved by adhering to the principles of good info viz design. 

In addition to these common problems, users who rely on screen-readers are completely excluded when visualizations lack proper captions and alt-text. Shockingly, a study published by Canelón & Hare (2021) examined over 7,000 visualizations shared to Twitter and found that only 3% included any alt-text and of that 84% of alt-text merely said "image." This reveals the scope of inaccessibilty in the information visualization field and why we need to continue working on this issue.

Luckily, all of these problems have relatively straight-forward solutions. Designers need only implement them more consistently,  Part Two of this guide looks at some of these solutions and how they can be incorporated into visualization design practices.