Creating Visualizations that are understandable to broad audiences

Kelly at VizCandy tells us we must know our audience when building a data visualization, giving steps to think about when planning what charts and visualizations to build, and how much data to display. I have a few follow-on suggestions for actually representing the data. Many of the examples I give are U.S.-centric, as that is where I work, but I would love to hear international examples and suggestions, too.

First, be very deliberate about your color choice: if you only have one dimension of data that continuously increases, don’t use more than one hue to represent it, and make the darkest or lightest (or white) version represent the highest or lowest value. If your data diverges from a central point, use two hues that both go from dark to light (or vice versa) from the central point. And when choosing the hue to use (what some people refer to as “color” – red, orange, yellow, blue, etc.), try to pick something that is familiar to your audience if there is a cultural or natural association with it: green for plants or money, blue for water, blue for cold and red for hot (in the U.S. at least) if you have a diverging dataset on temperature. Red can also mean danger (in the U.S.) or stop or other negative things; green is the opposite. However, a red-green diverging scale would be terrible for someone with red-green color blindness who can’t tell the difference between the two!

What other colors do you associate with particular things?

One other note about hue: yellow-green is the most obvious to the human eye, based on our collection of cones and wavelengths to which they are most sensitive. So don’t use it as a middle value!

If your data is overlaid on a map, put in geographic markers to orient the viewer. This can mean city, state/territory, country, continent, ocean basin, river or lake labels as your scale dictates. Don’t clutter your design, but if your point is not to teach geography with this particular viz, don’t make your viewer spend their time figuring out what area you are showing. In fact, the labels may help them learn geography by giving them something else to associate with that location, i.e., your data (but don’t quote me on that).

Avoid the use of jargon, back to Kelly’s point about keeping it simple. If it’s not important for the viewer to know what that word means, that is, they can understand the point of your visualization if the title and supporting information is in plain language, then don’t give them a vocabulary lesson. This means using the measurement units your audience is most likely to use, with because of their background knowledge or their frame of reference. That could be inches instead of centimeters if it’s not a (graduate-level or above) scientific audience in the U.S., or acres instead of square feet or square miles if you’re talking to agricultural audiences. Also don’t use abbreviations of unfamiliar organizations or measurements, or even those that could be easily misread; better to spell out NASA or NSA than have your audience confuse the budget you’re talking about!

Include comparisons whenever you can. This can be another way to orient the audience as well as a way to introduce unfamiliar data or jargon if it’s necessary. If you are showing a lay audience the gross national product of an unfamiliar country or city, use their local city, state, or national data along side for reference. If that means a smaller or additional visualization next to the one that conveys your main point, think about how much easier having that will make interpretation.

All of these points come from a great deal of science education and science communication research, including my dissertation, which I can discuss in more detail if anyone would like. Many of these resources are unfortunately not open-access, so I can’t post them here, but feel free to contact me and I can help direct you, or any good university library will have access. I will post more articles and the like here as I find them.




Posted: April 30, 2014

Tags: Data Visualization, Katie Stofer, Science Education Research, Science Outreach, Theory To Practice

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