Every data visualization you’ve ever seen is a lie. At least in part. Any graph or chart represents layers and layers of abstraction. Your Jawbone Up’s report on how you slept is just points of data pulled from a spreadsheet that’s filled with numbers gathered from a sensor that thinks your stillness means you’re unconscious. The most objective data presentation–your snoring, maybe?–is at least once or twice removed from the data itself.
Which is why data-viz guru Nicholas Felton–creator of the Feltron report, the Reporter app, and Facebook’s Timeline–is suddenly so interested in photography. And what started as a collection of seemingly random photos he saved in a desktop folder has become a a curated photography book called PhotoViz, available for order now.
“Photo viz for me, in its briefest terms, is visualization done with photography or based on photography,” Felton says. And that means it’s visualization created without layers of abstraction, because every data point in an image is really just a photon hitting your camera sensor.
PhotoViz represents Felton’s overview of a field that goes as far back as photography itself, but is particularly ripe for exploration today, as we’re equipped with a whole new suite of post-production tools that are redefining photography as we’ve known it. As he points out, photographic pioneer Eadweard Muybridge was shooting galloping horses in the late 1800s as a way to quantify anatomy in motion for the first time. High-speed photography techniques allowed us to get better and better at capturing motion, leading to Harold Edgerton’s milk drops in the 1930s, which visualized the split-second moments of nature that otherwise went unseen.
Into the modern era, new techniques–like stacking frames through Photoshop–have pushed the bounds of photo visualization further. Photo designer Peter Funch uses Photoshop to show dozens of New Yorkers yawning on the street at the same time. Many moments condensed into one, with all the irrelevant subjects cut out, has a poignant, life-affirming effect.
We saw similar techniques play out in the last Olympics when high-speed cameras married with exposure stacking techniques helped create single-frame, step-by-step sequences of snowboarding and skiing runs, letting the viewer track every nuance of an athlete’s movement.
Why are these photo visualizations so powerful, compared with their graphically rendered counterparts? “They can be really descriptive,” Felton says. “When they are a sum of photographs, it gives you what we love about photography, the precision of the expression on someone’s face … all the background things we take in…maybe [there’s] an emotional connection in it.”
And that’s really the greatest appeal of photo visualization as a discipline. Whereas the best data designers have to work very hard to make you recognize the significance of a bar chart, photography is human at its core. It speaks in objects and faces, the language of life itself.
Which is why Felton believes the potential is only just being tapped now. As algorithmic technologies, like Facebook’s face-recognizing autotag feature, or Google’s Auto Awesome system, which can combine several failed family photos into one magical image where everyone is smiling, the possibilities for condensing and rearranging the data of photography is only growing.
“The Instagram model, where I need to find that precise moment to tell everything that I wanted to share is amazing, but there’s also an opportunity for the aggregate stories–how do you condense 200 photos from my Camera Roll into something that’s digestible?” he says. “I think we’re at the tip of the iceberg in how we find patterns and manage this glut.”