How Accessibility Standards Can Empower Better Chart Visual Design<\/h1>\nKent Eisenhuth<\/address>\n 2024-02-14T15:00:00+00:00
\n 2024-10-15T23:05:45+00:00
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Data visualizations are graphics that leverage our visual system<\/a> and innate capabilities to gather, accumulate, and process information in our environment, as shown in the animation in Figure 1.0.<\/p>\n
<\/a>Figure 1.0. An animation demonstrating our preattentive processing capability. Based on a lecture by Dr. Stephen Franconeri. (Large preview<\/a>)<\/figcaption><\/figure>\nAs a result, we\u2019re able to quickly spot trends, patterns, and outliers in all the images we see. Can you spot the visual patterns in Figure 1.1?<\/p>\n<\/p>\n <\/p>\n
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<\/a>\n Figure 1.1. Pattern groups defined by size, borders, fills, and shape types. (Large preview<\/a>)
\n <\/figcaption><\/figure>\nIn this example, there are patterns defined by the size of the shapes, the use of fills and borders, and the use of different types of shapes. These characteristics, or visual encodings, are the building blocks of visualizations. Good visualizations provide a glanceable view of a large data set we otherwise wouldn\u2019t be able to comprehend.<\/p>\n
Accessibility Challenges With Data Visualizations<\/h2>\n
Visualizations typically serve a wide array of use cases and can be quite complex. A lot of care goes into choosing the right encodings to represent each metric. Designers and engineers will use colors to draw attention to more important metrics or information and highlight outliers. Oftentimes, as these design decisions are made, considerations for people with vision disabilities are missed.<\/p>\n<\/p>\n
Vision disabilities affect hundreds of millions of people worldwide. For example, about 300 million people have color-deficient vision, and it\u2019s a condition that affects 1 in 12 men.1<\/sup><\/p>\n1<\/sup> Colour Blind Awareness (2023)<\/a><\/p>\n \ufffc<\/p>\n
Most people with these conditions don\u2019t use assistive technology when viewing the data. Because of this, the visual design of the chart needs to meet them where they are.<\/p>\n
Figure 1.2 is an example of a donut chart. At first glance, it might seem like the categorical color palette matches the theme of digital wellbeing. It\u2019s calm, it\u2019s cool, and it may even invoke a feeling of wellbeing.<\/p>\n<\/p>\n <\/p>\n
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<\/a>\n Figure 1.2. DON\u2019T: use low-contrast colors on your chart. (Large preview<\/a>)
\n <\/figcaption><\/figure>\nFigure 1.3 highlights how this same chart will appear to someone with a protanopia condition. You\u2019ll notice that it is slightly less readable because the Other and YouTube categories appearing at the top of the donut are indistinguishable from one another.<\/p>\n<\/p>\n <\/p>\n
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<\/a>\n Figure 1.3. How someone with protanopia might see an inaccessible color palette. (Large preview<\/a>)
\n <\/figcaption><\/figure>\nFor someone with achromatopsia, the chart will appear as it does in Figure 1.4<\/p>\n<\/p>\n
\n 2024-10-15T23:05:45+00:00
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As a result, we\u2019re able to quickly spot trends, patterns, and outliers in all the images we see. Can you spot the visual patterns in Figure 1.1?<\/p>\n <\/p>\n <\/a> In this example, there are patterns defined by the size of the shapes, the use of fills and borders, and the use of different types of shapes. These characteristics, or visual encodings, are the building blocks of visualizations. Good visualizations provide a glanceable view of a large data set we otherwise wouldn\u2019t be able to comprehend.<\/p>\n Visualizations typically serve a wide array of use cases and can be quite complex. A lot of care goes into choosing the right encodings to represent each metric. Designers and engineers will use colors to draw attention to more important metrics or information and highlight outliers. Oftentimes, as these design decisions are made, considerations for people with vision disabilities are missed.<\/p>\n<\/p>\n Vision disabilities affect hundreds of millions of people worldwide. For example, about 300 million people have color-deficient vision, and it\u2019s a condition that affects 1 in 12 men.1<\/sup><\/p>\n 1<\/sup> Colour Blind Awareness (2023)<\/a><\/p>\n \ufffc<\/p>\n Most people with these conditions don\u2019t use assistive technology when viewing the data. Because of this, the visual design of the chart needs to meet them where they are.<\/p>\n Figure 1.2 is an example of a donut chart. At first glance, it might seem like the categorical color palette matches the theme of digital wellbeing. It\u2019s calm, it\u2019s cool, and it may even invoke a feeling of wellbeing.<\/p>\n <\/p>\n <\/a> Figure 1.3 highlights how this same chart will appear to someone with a protanopia condition. You\u2019ll notice that it is slightly less readable because the Other and YouTube categories appearing at the top of the donut are indistinguishable from one another.<\/p>\n <\/p>\n <\/a> For someone with achromatopsia, the chart will appear as it does in Figure 1.4<\/p>\n<\/p>\n
\n <\/figcaption><\/figure>\nAccessibility Challenges With Data Visualizations<\/h2>\n
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