Content authors: Nancy Rice, Research Scientist, Minnesota Department of Health and Angela Sechler, Business Intelligence Product Manager, Minnesota Department of Health

What is data visualization?

Data usually consists of numbers and categories. Examples of numerical data are number of people in an area or dollar amounts in a budget. Examples of categorical data are geographic regions (like counties) and types of bills to be paid (like office supplies or food).

Data visualization presents information in visual formats rather than relying only on text or tables. These visuals can be interactive, allowing people to filter or select categories such as age, gender, or Minnesota county. We typically show results through bar charts, line graphs, maps, and other visual displays that make trends and patterns easier to recognize. Because visuals rely heavily on color to convey meaning, creating them requires careful attention to color use and accessibility.

Why do we need to consider accessibility for data visualizations?

Data visualizations are meant to be viewed, which can create barriers for people with limited or no vision. Without accessible features, these users may not be able to understand or interact with the information. Accessibility also matters for interactive data visualizations. 

Not everyone accesses information visually, and people have a wide range of visual abilities. Some individuals are blind, while others may have low vision, color blindness, double vision, or other visual conditions. Even people with full vision may prefer or require non-visual formats, such as using a screen reader to listen to content. Because people interact with information in many different ways, it’s important to apply basic accessibility principles so the data is usable for as many people as possible.

How do I make a visualization accessible? Color considerations.

 Accessible data visualization involves many considerations. Here, we’ll focus on color and provide a few tips below. For more thorough information, refer to the “Resources” section at the end. 

• State of Minnesota employees can also connect with the Data Visualization community.

Color Choices

Color can help distinguish elements in a visualization but is not accessible to everyone. Consider how the visualization would look in black and white, or without visual cues at all. Would the information still be clear and understandable?

Example: Line graph

Option 1: The line graph below displays rainfall in inches from Site A and Site B from May to August 2022. The colors of the lines are similar making it hard to distinguish Site A from Site B. 

Option 2: The line graph shows the same information as Option 1, but it adds distinct shape markers and clear labels for each line. These changes make the differences easier to see and more accessible to a wider range of users. 

Tips to remember when using color

• Don’t use color alone to communicate information within the visualization. Use labels, shapes, or patterns in addition to color, when needed.
• Make sure colors placed next to each other have enough contrast to easily be distinguished. Color contrast can be tested with the online WebAIM: Contrast Checker or other tools like the Colour Contrast Analyser.  
  • For text less than 18 pt font or 14 pt bold font, the color contrast ratio should be 4.5:1. 
  • For larger text or for non-text features (like bars or areas in a map), the ratio should be 3:1. 
  • Note: Not all Minnesota Brand colors provide sufficient contrast when used together. For example, the brand’s primary green doesn’t have enough contrast against white (ratio is 2.3:1). You’ll need to darken it if you’d like to use it next to white. Check the Minnesota Brand Style Guide - 2025 Edition (PDF) for more information and options.
• Include a table with the data, when possible. Including the full data set helps ensure more users can access and understand the information in your visualization. 

Color Palettes

 Color palettes can be useful, but no palette will work for everyone. To test accessibility, view the visualization in grayscale. If you can’t distinguish between components, others may struggle as well. You can also test color combinations with the Color Palette by Deque. 

Have questions or want to know more? 

Explore the resources below to get started:

Office of Accessibility

• Accessible Data Visualization web page
• Data Visualization with WCAG 2.1: Content on Hover or Focus, and Pointer Gestures
• Accessible Word Document eLearning, Module 3: Working with Colors
• How to Improve Non-text Contrast: Color Schemes and Interface Components (Understanding Success Criteria 1.4.11 Non-text Contrast)
• More Attractive, Engaging, Accessible: How to Use Color with Intention

State of Minnesota Employees only

• Data Viz A11y Community of Practice - This group is open to all State of Minnesota staff who would like to learn more about data visualization accessibility. 

External resources

Web Content Accessibility Guidelines (WCAG)

• Success Criterion 1.4.3 Contrast (Minimum), Level AA: The visual presentation of text and images of text has a contrast ratio of at least 4.5:1.
• Success Criterion 1.4.11 - Non-text Contrast, Level AA: The visual presentation of the following has a contrast ratio of at least 3:1 against adjacent color(s).
• WebAIM Contrast and Color Accessibility: Understanding WCAG 2 contrast and color requirements.

Tools

• WebAIM Contrast Checker
• Deque Color Palette Contrast Checker
• Vispero (formerly TPGi) Colour Contrast Analyser
• Colblindor Coblis (Color Blindness Simulator)