In a post on his blog Sapping Attention entitled “The history of looking at data visualizations,” Ben Schmidt (Northeastern University) digs into the lessons, the reading conditions, and the design concerns that come out of an investigation of the history of data visualization.
One of the most important strands that emerged was about the cultural conditions necessary to read data visualization. Dancing around many mentions of the canonical figures in the history of datavis (Playfair, Tukey, Tufte) were questions about the underlying cognitive apparatus with which humans absorb data visualization. What makes the designers of visualizations think that some forms of data visualization are better than others? Does that change?
There’s an interesting paradox about what the history of data visualization shows. The standards for data visualization being good change seem to change over time. Preferred color schemes, preferred geometries, and standards about the use of things like ideograms change over time. But, although styles change, the justifications for styles are frequently cast in terms of science or objective rules. People don’t say “pie charts are out this decade”; they say, “pie charts are objectively bad at displaying quantity.”
The post covers historical approaches and contemporary visualization scholarship and tools to consider how these visualizations are read, rather than merely designed.
The standard narrative of data visualization, insofar as there is one, is of steadily increasing capacity as data visualizations forms become widespread. (The more scientific you are, I guess, the more you might also believe in constant capacity to apprehend data visualizations.) Landmark visualizations, you might think, introduce new forms that expand our capacity to understand quantities spatially. Michael Friendly’s timeline of milestone visualizations, which was occasionally referenced, lays out this idea fairly clearly; first we can read maps, then we learn to read timelines, then arbitrary coordinate charts, then boxplots; finally in the 90s and 00s we get treemaps and animated bubble charts, with every step expanding our ability to interpret. These techniques help expand understanding both for experts and, through popularizers (Playfair, Tufte, Rosling), the general public.
What that story misses are the capacities, practices, and cognitive abilities that were lost. (And the roads not taken, of course; but lost practices seem particularly interesting).
dh+lib readers will appreciate Schmidt’s consideration of some lesser-known examples of historic visualizations and his interest in exploring specific humanities traditions in the DH space: “one of the most interesting areas in this field going forward may be bridging the newfound recognition of the significance of data visualization as a powerful form of political rhetoric and scientific debate with a richer vocabulary for talking about the history of reading images.”