transit

San Francisco-area designer Steve Pepple wondered whether data could help him explore neighborhoods — on the way to helping city planners build better, perhaps “smarter” cities.

He describes in a fun post on Medium, a genuine “data story,” how he set out to help people find “pockets of activity, vibrance, and new things.”

First, he joined a do-it-yourself project called Sense Your City. Sensors around the world reaped “hyper-local data about environmental conditions, such as noise, dust, light, and pollutants.” That data led to “pockets of activity, vibrance, and new things.” In San Francisco, he writes, “you could see when the fog rolls in.”

That sounds like fun, but could he find a key insights about mobility, housing, or neighborhood change? He had frustrating moments. “I had a bunch of data to work with,” he writes. His many sources included DataSF, San Francisco Planning, SPUR, and the City of Oakland. But he didn’t know what he was doing. Perhaps worse, “I was stymied by trying to find a significant, cumulative insight by cobbling together and analyzing all the data.”

He had been inspired by one man’s discovery who famously found such an insight in San Francisco rental data. Eric Fischer had pored over newspaper ads back to 1948 and surprised urbanists with his conclusion: the usual levers for regulating demand wouldn’t solve the city’s housing crisis.

Smaller experiments

With no big discoveries in sight, Pepple turned to shorter experiments. Apparently as part of a fellowship with Stamen Design and Gray Area Foundation for the Arts, he looked at Instagram photos to track street movement, such as some taken during the 2015 Pride Parade. Data from those photos showed events like parade formation and, in the afternoon, a drift into block parties.

His Medium post includes a short series of visualizations that are more fun and vibrant than you might think any “smart city” has a right to be. With “color quantization,” he created a “spatial database of color” that showed parks and murals in the Mission District. In another map, colors expressed “density of people and establishments,” with brighter and more colorful buildings showing the most active.

Tangible, street-level meaning came in large, street-level screens…that showed passersby how they could “interact with data about their neighborhood and arrive at their own discoveries.”

Clues

It was his work with the urban designers and architects Perkins + Will that his approach had what I find the most interesting and possibly the most meaningful clue. Pepple and team could see and hear neighborhoods with the most activity. Could that be found with data? Indeed, data tracked activity and what people said about the places where it occurred; social media helped characterize those places; mapping tools connected hotspots.

A look at the maps Pepple and team produced shows that transit, living space, restaurants, and other amenities fail to anchor social hotspots. Fine, but why? If the data can reveal this, perhaps it can go the rest of the way.

Keep your eye on Pepple. His playful and often fun analysis puts a bright, even inspiring face on urban data.

Thanks to Stephanie Langenfeld McReynolds, vice president of marketing at Alation, for alerting me to Pepple’s work.