The most useful and flexible definition of the term “smart city” is a city that employs digital technology to improve municipal management, governance, or long-range design and planning. This is a multistakeholder activity involving the deployment of these tools by multiple units and layers of government, businesses, and residents. By this standard almost every city on Earth can be considered “smart.” While scholars have argued for nearly a decade about the dangerous rhetoric of “smart” as both a descriptive and aspirational label for cities (e.g. Hollands 2008; Greenfield 2013), we cannot deny that digital systems have become as much a part of urban-ness today as mechanical ones. They are immanent in contemporary cities.

For most of the last decade, the smart city has been a corporate project. That is to say, it has not challenged existing power structures in either its formulation of the essential problems facing cities or the solutions. It has focused on incremental improvement to existing systems and solutions through better management enabled by digital technology. Its limited set of ambitions has ignored broader contemporary urban policy concerns to focus on efficient operation.

As a result, the smart city has overwhelmingly been envisioned and sold as an upgrade to existing cities. This stands in stark contrast to earlier movements in urban planning such as the Garden Cities movement, the City Beautiful, and the urban manifestations of modernism, which all sought to radically rework the underlying material basis of cities and the forms and structures that would make them possible. The smart city instead has had modest ambitions. It could best be seen as a campaign for incremental, iterative improvements to twentieth-century urban infrastructure designs that have failed to meet the burdens placed on them by the scale and speed of twenty-first-century urbanization.

This version of the smart city, cementing private-sector operators in key positions over an indefinite period, has unsurprisingly led to ominously, though not often overly, corporatist views of the future city. These visions have typically overlooked structural inconsistencies in global capitalism and the exclusions that it has created in cities— income inequality; inadequate housing supply and the corresponding problems in affordability; inequitable environmental risks; and so on.

The information-architectural consequences of this frame are profoundly dystopian. They celebrate and consolidate a massive centralization of all the key pieces of urban digital systems—from sensors that record events to the networks that transmit them and the computing capacity that analyzes and stores the data gathered. By and large, then, the idea of the smart city has evolved as a simulacrum of the larger global internet, in that its economic logic is rapidly centralizing information and power. The smart city, in computer metaphor terms, is a mainframe. Not only is this not seen as undesirable—it is a vision and design strategy actively pushed by the corporations involved in the creation of smart cities—it is largely not seen or understood by urban policymakers.


In my 2013 book, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, I made an initial attempt to map out this emerging political economy of cities. More recently, a number of scholars have addressed this directly, in considerable depth. As the smart city has added little in the existing domain of the physical city that is up for grabs, the focus in most cases is on the power dynamics of data, a new and fast-changing landscape.

My modest attempt at understanding who the builders of the smart city are, and what they want, was motivated by the wrong-headedness of the motivations and visions I saw from the likes of IBM, Cisco Systems, Siemens, and other companies looking to develop new markets. One only has to take a moment’s look at those visions and their specific manifestations—like the much-maligned Songdo International Business District in South Korea—to see how incomplete and limiting they are. But by the end of the book, it was clear that the biggest risk was not taking the smart city vision far enough. There was still plenty of room to push against that mainframe view of the smart city. There was still enough data and computing power out there at the edges of the network for really transformative ideas to take hold.

And so, in the final chapter of Smart Cities, I laid out 13 tenets of “a new civics for smart cities.” My inspiration was the work of Sir Patrick Geddes, one of the fathers of the Garden Cities movement. Geddes, unlike most of his Victorian-era urban reformers, was a biologist rather than an architect or civil engineer, an avid gardener rather than an inventor of things mechanical. He believed deeply in bottom-up change and had an uncanny grasp of urban systems thinking that presaged Jane Jacobs’s writing a half-century later, which popularized many of the same ideas and convictions.

My new civics sought to lay the seed for a Geddesian approach to smart cities by laying out a set of principles that might guide choices for a broad array of actors building smart cities in a more vernacular style, rather than master-planning them—or worse, packaging them as products and services to be installed off the shelf. These tenets covered a range of strategic recommendations—I urged cities to adhere to the following principles: be skeptical of easy digital solutions when analog answers were already at hand; build and keep control of their own digital infrastructure; avoid excessive integration and the excessive control points it creates; take on the challenge of data governance; craft bespoke solutions but with a mind to sharing with other cities; train professionals who can work in both digital and physical realms; inform long-term planning with rich data; be careful about technology-enabled crowdsourcing and devolution of power; ensure that everyone is well-connected and well-informed; harness data-driven urban science but in small measures; and be very careful about automating human decisions in ways that hide the impacts of our choices about consumption.


While I put my own spin on it, this civics for smart cities was in many ways simply reflecting what was already in the air. By 2012, a new planning practice was emerging in global cities. City governments were starting to take over the role of thought leader in the smart cities movement through the development of far-reaching smart city plans, or what I have come to call “digital master plans.” In 2015, along with my coauthor Stephen Lorimer, who now heads smart city planning for the Greater London Authority, I carried out a study looking at the content, planning process, and implementation approach of the most notable of these plans—in New York, Chicago, London, Singapore, San Francisco, Hong Kong, Barcelona, and Dublin. What we found was a wide variety of thinking in each of these phases of smart city planning.

This was to be expected. In speaking with planners involved in the mid-2000s in the early days of city sustainability plans, when the scope, authority, and best practices in carbon dioxide emissions reduction at the municipal level were unclear, we found great parallels. Though it is far less clear if digital master planning will ever receive the kind of statutory requirement that eventually catapulted sustainability planning to the fore in many cities. It could happen, however, if cybersecurity concerns expand.

These plans and the processes that make and implement them then are the central arena for shaping a better smart city movement. This is where the power struggles over the soul of the smart city are most likely to take place, and perhaps for which they are best suited. This begs the questions: What kind of research would make these plans better? What do cities need to know to chart their future?

It’s hard to narrow the cone, as there is so much that cities do not know. But if the early finding of the revolution in computationally intensive, data-driven urban research—itself enabled by smart city technology and methods—is any indication, we are just at the earliest stages of establishing the kind of ground truth needed to make better digital master plans.

The disturbing fact is that no one really knows what is going on in cities because of these technologies. That is to say, there is scant and poorly integrated evidence about how people’s decisions, and the ways those decisions add up to change, are being impacted by the spread of digital technology. Consider transport, which is by far the most quantitative and rigorously understood area of urban research and planning. It is also the area where smart city technologies are currently throwing off the largest amounts of data that cities have ready access to (despite the efforts of companies like Uber to keep these data closely held). But it could be said that our understanding of what is actually happening—how people are traveling and the implications for future transport planning and land use—is actually falling behind. If you don’t believe me, all you have to do is look at the apparently massive negative impact that the presence of digital technology is having on automotive safety. “Distracted driving” is an obvious, inevitable outcome of the spread of engrossing tools and services into cars that are still driven by people with limited attention. Yet we know, collectively, almost nothing about what is actually going on.

So we need to study the dynamics of smart cities much more than we do now. We need to know how markets are driving change, as much as we need to understand the effectiveness of public investment and use in smart city technologies. This is before we even get to thinking about desired directions, or about how policy and planning can turn the dial in a desired direction.

The problem is, getting to the bottom of this is going to involve a lot of data collection on topics, in locations, and about activities that are potentially very invasive. Shannon Mattern recently published a somewhat wrong-headed argument against the use of cities as “living labs.” While these critiques are indeed valid, they ignore the urgency of getting cities right—efficient cities are indeed one of the most powerful tools we have to combat climate change. If made more equitable, global poverty as well. We cannot simply say cities should not be “living laboratories,” to use the term of art, because the public case they must be is strong. The city must be harnessed—every bit as much as, say, the human genome—as a tool for the rapid expansion and acceleration of knowledge that can help us achieve humankind’s global economic and environmental goals.

What is needed rather is to make cities better laboratories. The city has always been a laboratory for figuring out what works, in business, in government, in culture. And anachronistic, Western notions of individual privacy are questionably robust when stacked against the greater good that is possible.

And so the challenge then becomes establishing a new approach to how we study cities in the era of smart cities, and I suspect the responses may hold in them the seeds of a new compact around urban governance (and a new civics that dictates what is expected of individuals and groups)—every bit as much as the massive instrumentation of urban bureaucracies about 150 years ago did. City charters today say very little about how municipalities and their partners can and should collect, analyze, and use data. In fifty years, they may cover little else, data will be such an important linchpin in what they do, how they create value—why they exist at all. To put it another way, the generation, use and handling of data about people and their activities needs to be baked into how city government works at the most basic levels. This will trigger debates about what rights people have to control information about them, and this will be the first step towards building the trust that will be needed for them to believe what the science that is done with their data says, what it means for policy, that we are even asking the right questions in the first place, and how governments use that to carry out their business.


Benjamin Edwards et al., “A Political Economy Framework for the Urban Data Revolution” (The Urban Institute, 2016), http://www.urban.org/research/publication/political-economy-framework-urban-data-revolution.
Adam Greenfield, Against the Smart City (Verso, 2013).
Robert G. Hollands, “Will the real smart city please stand up?” City 12, no. 3 (2008): 303–320.
Rob Kitchin, The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences (Sage, 2014).
Rob Kitchin et al. “Smart cities, urban technocrats, epistemic communities and advocacy coalitions” (The Programmable City Working Paper 26, 2017), http://progcity.maynoothuniversity.ie/.
Piyushimita Thakuriah et al., Seeing Cities through Big Data: Research, Methods and Applications in Urban Informatics (Springer, 2016).
Anthony Townsend, “Making Sense of the New Urban Science” (Data & Society Research Institute, 2016), http://www.citiesofdata.org/wp-content/uploads/2015/04/Making-Sense-of-the-New-Science-of-Cities-FINAL-2015.7.7.pdf.
Anthony Townsend and Stephen Lorimer, “Digital Master Planning: An Emerging Strategic Practice in Global Cities” (NYU Marron Institute of Urban Management, 2015), http://marroninstitute.nyu.edu/content/working-papers/digital-master-planning.