Smart Cities, Bad Metaphors, and a Better Urban Future

Maybe it’s a cliche—I think I’ve used it myself—to say that scientists’ and philosophers’ explanations for how the brain works tend to metaphorically track the most advanced technology of their time. Greek writers thought brains worked like hydraulic water clocks. European writers in the Middle Ages suggested that thoughts operated through gear-like mechanisms. In the 19th century the brain was like a telegraph; a few decades later, it was more like a telephone network. Shortly after that, no surprise, people thought the brain worked like a digital computer, and that maybe they could build computers that work like the brain, or talk to it. Not easy, since, metaphors aside, nobody really knows how the brain works. Science can be exciting like that.

The absence of a good metaphor hasn’t stopped anyone from studying brains, of course. But sometimes they confuse the map for the terrain, mistaking a good metaphor for a workable theory. It’s easy to do when it comes to complex systems that interact at scales either too big or too small for us to observe in their entirety. That’s true for the brain, a lump of think-meat generating an individual mind from, researchers think, around 86 billion individual cells woven into an electrochemical jelly-network. And it’s true for a city, the dense network in which millions of those individual minds come together to form a community. The people who write about cities—I’ve done it myself—also tend to grope for organizing metaphors in current science. A city is a machine, a city is an animal, a city is an ecosystem. Or maybe a city is like a computer. To the urbanist and media studies writer Shannon Mattern, that’s the dangerous one.

Mattern’s new book comes out August 10; it’s a collection (with revisions and updates) of some of her very smart work for Places Journal called A City Is Not a Computer: Other Urban Intelligences. In it, Mattern wrestles with the ways that particular metaphor has screwed up the design, planning, and living-in of cities in the 20th century. It happens at every scale, from surveilling individual people as if they were bits to monitoring the widescreen data necessary to keep a city functioning for the good of its inhabitants. Of all the ways information can travel through an urban network, Mattern says, it’d probably be better to have public libraries be the nodes than the panopticon-like centralized dashboards so many cities try to build. The problem is that the metrics people choose to track become targets to achieve. They become their own kind of metaphors, and they’re usually wrong.

The first two essays are the ones that had the most oomph when they were first published—and still do. “City Console” is a wild history of information dashboards and control rooms designed to be panopticons for urban data. These informational hubs collect input on how well municipal systems are working, crime is getting policed, children are getting educated, and so on. Mission control, but for freeways and sewage. My favorite example from Mattern’s book is the 1970s effort by Salvador Allende, then the leader of Chile, to build something called Project Cybersyn, with an “ops room” full of button-studded chairs that would have made Captain Kirk proud, plus wall-sized screens with flashing red lights. Of course, since no city had real-time data to fill those screens, they displayed hand-drawn slides instead. It’s goofy, but there’s a direct line from Cybersyn to the ways lots of US cities now collect and display law enforcement and other urban data in CompStat programs. They’re supposed to make government accountable, but they often justify worthless arrests or highlight misleading numbers—on-time transit travel instead of number of people carried, let’s say.

In the next essay, the titular one, Mattern warns against the ambitions of big Silicon Valley companies to build “smart cities.” When the essay first appeared, Amazon was still on tap to build a city-sized headquarters in New York, and Google was pushing to do much the same in Toronto. (The Google project, from a sibling company called Sidewalk Labs, would have featured wood skyscrapers, pavement that used lights to reconfigure its uses on the fly, self-driving cars, and underground trash tubes.) Now, of course, most of the big smart-city, tech-enabled projects have failed or scaled back. Hudson Yards in New York didn’t deploy with anywhere near the level of sensor and surveillance technology its developers promised (or maybe threatened). Cities still gather and share all kinds of data, but they’re not exactly “smart.”

In a conversation last month, I asked Mattern why tech companies seem to have failed to smarten up any cities, at least so far. She thinks it’s because they missed the most important parts of citymaking. “A lot of more computational and data-driven ways of thinking about cities give a false sense of omniscience,” Mattern says. The people in charge of cities think they’re getting raw truth when in fact the filters they choose determine what they see. “When everything is computational, or when we can operationalize even the more poetic and evanescent aspects of a city in a datapoint,” Mattern says, “that makes us unaware that it is a metaphor.”