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From the book Dream States: Smart Cities, Technology, and the Pursuit of Urban Utopias by John Lorinc
John Lorinc is a Toronto freelance journalist and editor focusing on cities, politics, business, climate change, and local history. John has won numerous National Magazine Awards for his journalism and was the 2019–20 Atkinson Fellow in Public Policy, which produced a series of ten articles on smart cities that were the basis of Dream States.
From the earliest periods of urban development, monarchs, philosophers, and eventually planners and architects have sought to design and build cities that aspire to some kind of idealistic vision. As University of London geographer David Pinder, a scholar of the utopian urban tradition, has explained, these have ranged from spiritual beliefs that informed the physical layout of ancient cities to the conjuring of political utopias as a means of addressing deep questions ranging for the nature of justice to the problems of poverty or social decay.
Urban-focused technology has equally deep roots, as engineers, governments, inventors and eventually profit-minded entrepreneurs devised solutions to the kinds of problems that have always arisen whenever humans decide to create settlements: how to move around, how to ensure access to clean water, how to dispose of waste, how to create durable structures suited for the density of urban spaces, and how to communicate efficiently.
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While the history and evolution of urban space has been wrought from commerce, war, social upheaval, and the complicated diffusion of ideas, the story of city-building, in many ways, is also about the collision between utopian dreams and engineered solutions. Both have sought to improve or perfect the urban condition. There have been many examples, particularly since the dawn of the industrial era, when these two impulses converged or aligned, yet others where they came into direct conflict. Time is also a factor, as the utopian solutions to one era’s failings become the political or technological conundrums of the next. Or visa versa.
In my first journalistic encounter with the concept of “smart cities,” I wrote a feature for The Globe and Mail, in 2015, on the emergence of these technologies.1https://www.theglobeandmail.com/news/world/how-cities-are-searching-for-solutions-among-massive-mounds-of-data/article23131733/ One example struck a chord. I interviewed Mike Flowers, a tough-talking New York lawyer who had served in Iraq with military intelligence and somehow wound up working for Michael Bloomberg when he was mayor. Bloomberg made his billions selling and analyzing financial data, and he was determined to bring that same ethic into the lumbering, archaic world of municipal government.
Flowers, who liked to refer to Bloomberg as “the old man,” worked as part of a small flying squad of data scientists in the mayor’s office. He would go to various New York City departments and beg them to hand over sprawling databases of granular building-level information about fire code violations, upgrades, liens, tax arrears, and so on. Using what he described as a “data bridge,” Flowers and his team poured all those datasets into one giant spreadsheet that had a separate entry for every municipal address in New York. Then they started looking for patterns, “querying” the spreadsheet to look for common traits of buildings where there had been fires. They eventually found the proverbial needle in the haystack, and reckoned they could begin sending notices to landlords whose buildings showed similar patterns of neglect, reasoning that these structures were more likely to fall victim to fire. It was, fundamentally, a data-driven prevention strategy, geared in this case to fire risk.
But Flowers, no wide-eyed technophile, knew enough to gut check this process. He would go on ride-alongs with seasoned New York bylaw inspectors. They’d swing by a building that the data patterns had flagged as a potential firetrap. But sometimes those inspectors, following a quick look-around, spotted a counterfactual—a detail that suggested the building didn’t pose the risk that the data predicted. Those conclusions, Flowers told me, were based on experience, observation, and common sense—all traits, he noted, that couldn’t be automated. The mayor’s efforts to bring data science to municipal decision-making didn’t stop at City Hall. In one of his signature moves, he invited technical research institutions from around the world to bid on the opportunity to build a world-class technology and engineering campus on city-owned land in order to fill a gap in New York’s university offerings.
Bloomberg had made huge donations to academic public health institutions, but he saw this initiative as a way of seeding start-ups, attracting venture capital and building an east coast tech hub to rival Boston. Among the winning projects was something called the Centre for Urban Science and Progress, a degree-granting research joint venture between NYU, Carnegie Mellon University and several other academic partners, including the University of Toronto and a host of blue-chip tech giants. It describes its field—”urban informatics”—as the “interdisciplinary application of science, technology, engineering, and mathematics in the service of urban communities across the globe.”
Intrigued, I wrote several more stories about the use of data analytics to confront urban issues from air pollution to policing. One project, developed in Saskatoon, aimed to use predictive analytics to identify Indigenous youth who were at risk of running away. From a great distance, the initiative, a joint venture by the University of Saskatchewan and the Saskatoon police department, seemed well-intentioned. But once you peeled the onion, it became clear this somewhat creepy project—the data required included personal information from social service agencies—had more to do with treating the symptoms than addressing root causes.
And then, in 2017, Sidewalk Labs, Google’s smart city start-up, rode into Toronto. In many ways, their gambit was the journalistic gift that kept on giving. It had all the ingredients—money, politics, real estate speculation, sci-fi technology, and combatants with global reputations and deep pockets. What’s more, it seemed that Sidewalk, which conspicuously eschewed the phrase “smart cities,” appeared intent on disrupting the multi-billion smart city industry, then dominated tech giants like Cisco, IBM and Siemens.
After all, disruption, in those halcyon pre-pandemic years, was short-hand for the tech sector’s compulsion to overwhelm, wreck and monopolize whatever stood in its way. Google and Sidewalk, in other words, seemed motivated by a desire to disrupt cities—an objective historically used to describe the work of marauders, invading armies and natural disasters.
Excerpted and reprinted with permission, John Lorinc and Coach House Books.
