Kai Ryssdal, American Public Media, Marketplace
When did the current rush to turn data into pictures begin?
Perhaps it was when Hans Rosling wowed the world from the TED stage in 2006 with his dramatic data visualizations of global development myths. Data visualization has been around for millennia, of course, and Ren Descartes is credited for inventing the notion of graphing data in the 17th century. Bar and pie charts followed in the late-18th/early 19th centuries, thanks in large part to the hard work of William Playfair, a Scottish social scientist, according to Stephen Few, author of “Information Dashboard Design: The Effective Visual Communication of Data.”
As Few details in his excellent white paper on data visualization’s past, present and future, the field exploded in the 1980s thanks to Edward Tufte’s seminal work, “The Visual Display of Quantitative Information” in 1983, and Apple’s debut of its personal computer. Suddenly, everyman could become a wizard of telling data stories through pictures.
Now, of course, there’s an app for that, and everyman/woman can make a graphic and via social media share it with an audience of potentially millions in seconds. Twitter and Instagram are alive with maps, graphs and doodles. The quality of our data imagery varies greatly, but strong pictures are emerging, and we’ve never had better opportunities to literally have a picture of our collective behaviors.
The results are fascinating: The Chinese search engine Baidu heat mapped the migration pattern of millions of Chinese as they trooped home to celebrate the New Year in January.
On a more serious note, Dan Munroe pointed out in Forbes.com recently, seeing outbreaks of vaccine preventable diseases on a map – this one, a world map from the Council on Foreign Relations — is brightly colored yet hard-hitting proof of the damage of the behavior of refusing vaccines.
Mobile phone data in particular may be priceless information for development, as a technology pervasive in developing markets. One mobile carrier analysis showed where folks needed buses in Abidjan, leading to a rework of the city’s transport system. Another mHealth data analysis showed how human travel leads to the spread of malaria.
What does this mean for those of us involved in measuring and changing behaviors? For one, graphic data leads to greater awareness, particularly as information flows across social and mobile media. We like shiny, pretty things. And what makes good data visualizations? Some schools, including Harvard and MIT, are helping us move toward a more scientific understanding of what makes visualized data memorable, and the Rhode Island School of Design is working on how best to put across complex scientific concepts.
And as we become more sophisticated in creating good graphics, with an evidence-based understanding of what truly imparts information, we’ll be that much better in conveying ideas in ways that transcend cultural and even language barriers – and changing behavior on a visible, grand scale.