The Science of What We Do (and Don't) Know About Data Visualization


Visualization is easy, right? After all, it’s just some colorful shapes and a few text labels. But things are more complex than they seem, largely due to the the ways we see and digest charts, graphs, and other data-driven images. While scientifically-backed studies do exist, there are actually many things we don’t know about how and why visualization works. To help you make better decisions when visualizing your data, here’s a brief tour of the research.

The Early Years of Understanding Data
While the early days of visualization go back over 200 years, actual research to understand how it works really only started in the 1960s. Jacques Bertin’s Sémiologie Graphique (Semiology of Graphics), published in 1969, was the first systematic treatment of the different ways graphical representations encode data. Bertin coined many terms of the trade, such as the mark, which is the basic unit of every visualization, like a bar, line, or circle sector. He also defined a number of retinal variables, which are the visual properties we use to express the data; these include color, size, location, etc.

In the early 1980s, Bertin’s work was picked up by researchers in statistical graphics and the nascent field of visualization (which didn’t quite have its name yet). William Cleveland and Robert McGill performed experiments to find out which of Bertin’s retinal variables were best suited for particular types of data, while Jock Mackinlay built a system that put Bertin’s and their work to use to create visualizations from data….

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