A Data Visualization of One’s Own

The 2016 election cast Giorgia Lupi and Stefanie Posavec's Dear Data: A Friendship in 52 Weeks of Data  in an entirely new light. Dear Data came out in September as polling data started to harden around Hillary Clinton's chances for the presidency. It was reassuring to see der Horror-Clown, as German newspapers referred to the Republican candidate, didn't seem to have much of a chance at the presidency. Even the Trump campaign expected to lose. Among the casualties of the stunning result was our faith in big data in general and its predictive efficacy in particular. By contrast, Dear Data testifies to the storytelling power of data when it is personal and concrete. Lupi and Posavec used very small data sets–the stuff of daily experience–and ingenious data visualization to create what they call a "personal documentary." Unlike, say, your Fitbit, their data visualizations are beautiful. They're drawn by hand on postcards, which they mailed to each other weekly. 

The first week Lupi and Posavec recorded how many times they checked the time. In "A Week of Clocks" Lupi, an Italian living in New York City, and the American-born Prosavec, who lives in London, tallied up how many times they looked at clocks. Lupi devised a symbolic system to capture why she checked the time. Prosevec drew radials to illustrate where she checked the time. Nothing earth-shattering was revealed in the exercise–really, what profound truths lurk in checking the time?–and the drawings are rather crude compared to what the authors will produce over the next 51 weeks. But their first subject hints at their larger project: to create a story of a self through data slices take over time. A narrative, after all, is change over time.

Like any good story, character is revealed Lupi's and Prosavec's data. With her tiny, precise symbols and keen attention to detail Lupi excels at examining intimate spaces. She inventories the closets in her Brooklyn apartment and compares the ratio of clothes she actually wears (a small share) to the clothes her boyfriend actually wears (every article in his closet). Early one morning, before any of her co-workers arrive for work, Lupi records the objects in every cubicle on her floor. Her co-workers must have been startled at the precision and stealth with which their workspaces were examined. We don't know for sure, but we find that Lupi sits only a few cubicles away from her boyfriend. How does that effect their relationship? Lupi doesn't say, letting the data speak for itself. 

Lupi is the more introspective of the authors, yet Posavec is the more personally revealing. For Lupi everything is data and she plunges right into it. Posavec approaches her data more warily, commenting more readily on her data gathering and visualizations. She is frank about her struggles to expand her design vocabulary while matching Lupi's craftsmanship. Posavec loves Brixton and cultivates a wide circle of friends throughout London. Her sociability leads to occasional gaps in data collection–the authors refer to them as "data voids"–such as when she's been out drinking with friends. It turns out Posavec likes to swear, greeting "A Week of Swearing" with "F*ck Yeah!!" She relishes the chance to count birds and small mammals (but no rats) in "A week of Urban Wildlife." One of the most effective data visualizations appears late in the book and not as part of a weekly assignment. Posavec bluntly charts the course of an argument and reconciliation with her husband over the apparent loss of her cellphone, which she uses to track her data for Dear Data.

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This is one of several instances in which Lupi and Posavec confront uncomfortable truths about themselves. During the "Week of Compliments" Lupi discovers the asymmetry between the compliments she receives and the ones she gives out. Lupi's boyfriend worries, not for the first time, for her sanity after she spends six hours obsessively drawing every door she's touched that week, tagged with characteristically whimsical attributes such as "So heavy!" "Boyfriend opened it for me," and "I locked it!" After Posavec finds the length of her to do list remains the same over a week, she consoles herself by admitting she  "may as well stop worrying about getting everything done, because the list will never, ever, die." Posavec has to confess to (and draw) a data void that lasts an entire week when she forgets to track data for that week's assignment. Neither Lupi nor Posavec feels especially good about themselves at the end of "A Week of Envy."

Writing as someone who has been creating a lot of data visualizations lately–working, alas, in JavaScript and HTML rather than colored pencils and markers–I can appreciate how honest Lupi and Posavec are about their data. My experience has been corporate managers dislike, and won't believe, metrics that tell them what they don't want to hear. Also, big data is often quite narrow, nothing like the range of experience the authors track. At the same time, I'm awed by their inventiveness in rendering their lives in data. Over the year we see them grow as data visualizers and artists. Lupi's meticulous, symbolic style evolves to the point at which she seems more interested in her visualizations as an abstract work of art. At one point she looks to Kandinsky and Malevich for inspiration. More representational in her style, Posavec tinkers with her spirals to capture the way data moves, how it changes over time and through space. Her to-do list diagram is a good example. For another example see her map of walking through a crowd trying to smile at strangers–and growing grumpier with the effort.

"To draw is to remember," Lupi and Posavec remind us when their 52 weeks are up. They ask us to join in their project of tracking and visualizing our data. We create data all the time, now more than ever. If you have the Google Maps app on your cellphone Google is mapping your movements on a daily basis. It knows you took the train to work, drove to the grocery store or pool hall, and rode your bike to the park. Lupi and Posavec show us how to reclaim ownership of our own data–through drawings, of course–to make our data work for us rather than some corporation trying to sell us something. "It's not that hard" to they assure us. Perhaps not, but to draw it as beautifully and imaginatively as they do certainly is. 

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