After a few solid years of thinking, writing and consulting about smart cities, I’ve distilled my recommendations down to four questions any municipal administrator or concerned citizen should ask when presented with propositions for the technological improvement of everyday urban life:
0. What does it do?
This question seems so obvious that you mightn’t think it needs to be posed explicitly. Incredibly, though, in my travels I’ve met a huge number of people, both in and out of government, who are so enamored of technological intervention both for its own sake, and for the gloss of modernity they think comes along with it, that they forget to ask just what it is they’re signing up for. Sometimes, indeed, they don’t even care. They should care, and so should you. What is the thing supposed to do in the first place?
1. Does it work?
Does the proposed intervention do what it’s supposed to do? This is by no means a settled matter of fact, even when dealing with technologies that might work stably and well in other contexts. Demand some kind of evidence that the proposed intervention actually functions in the way its vendors and advocates claim it will when deployed in an urban environment like yours, not just for a few weeks, but on an ongoing basis. If no such evidence is forthcoming, feel free to drive a much harder bargain, or to walk away entirely.
2. Do we agree that what it’s supposed to do is something worth doing?
A proposed technology might indeed do what its manufacturers say it will, but that thing might be monstrous — or at the very least, not something that a majority of citizens consider to be an end worth pursuing as a matter of public purpose. Say that someone is proposing to license and install new facial-recognition software for the city’s CCTV network, and that software reliably identifies 95% of the individuals that pass before its cameras. Is this a goal that the public has passed collective judgment on, and considers to be an acceptable expression of its will? (Are there procedures in place to reverse the deployment and its effects, should that collective judgment change in the future?)
3. Does it do that thing at reasonable cost, compared to other ways of addressing the issue at hand?
Maybe the proposed technological deployment serves an end that’s more or less universally regarded as desirable in your city, like reducing violence or vehicular traffic. And maybe the system on offer does actually (consistently, demonstrably, reliably) function toward that end. So far, so good. Are you convinced, though, that you’ve exhausted available ways of addressing the issue at hand that might be cheaper, less complex or less dependent on long-term systems integration, maintenance and upkeep commitments? Perhaps a summer jobs program is more effective at reducing youth violence than a cutting-edge predictive policing suite, and achieves its goals at a fraction of the cost (and without either abrogating the community’s rights or abrading its sensitivities). It might not be as superficially glamorous, and it won’t necessarily get your city talked about in puff pieces on cutting-edge urban innovation, but shouldn’t you exhaust that and other possible alternatives before shelling out in perpetuity for the complicated, big-ticket item?
Again, this almost shouldn’t need to be said in so many words, but: if you can’t come up with affirmative answers to questions 1 through 3, you should strongly reconsider whether the investment at hand is one worth making.
Note too that the framework I offer here limits itself to a consideration of the smart city at face value and on its own terms, i.e. those of financial cost-effectiveness and process efficiency. The truth, of course, is that are other ways of accounting for cost and benefit, and that the costs reckoned in dollars are neither the only ones incurred in any given deployment of informatic technology, nor by any stretch of the imagination the ones that matter most. But for the moment, let’s agree to place all such considerations to one side. What you might find startling, in doing so, is that the smart city very often cannot even justify itself on its own, artificially constrained terms.
A response to a piece Ayona Datta wrote for the forthcoming Dialogues in Human Geography, which I am pleased to present to you on the occasion of the Urban Age Delhi conference on Governing Urban Futures.
«Dites donc, c’est pas Alphaville qui faut appeler votre patelin, c’est Zéroville!»
– Eddie Constantine as private eye Lemmy Caution, in J.-L. Godard’s Alphaville (1965)
In her paper examining the Indian “smart city” development of Dholera, Ayona Datta furnishes us with a very welcome case study in just how elite actors mobilize the language and appearance of technological contemporaneity to achieve ends of displacement. Her analysis is straightforward and capably presented, and the most useful thing I can do in the space allotted to me for commentary is cosign, and hopefully amplify, certain of her conclusions.
Datta, as I understand her, has two fundamental aims here. The first is simply to establish that in the claims made on its behalf, in its disconnection from the history of the land, in its mendacity about its actual purpose, and in every other relevant register, the development site of Dholera is utterly exemplary of the smart city paradigm so many of us have inveighed against over the past half-decade or so. The second — to me, a more interesting and more enduringly valuable ambition — is to demonstrate that this existing line of critique is insufficient to understand the particular kind of work Dholera does in Indian politics.
What any such understanding would require is a kind of multipronged forensic analysis, and that is precisely what Datta provides. In the case of Dholera, an account of the actual site, its physical characteristics and existing demography are no less crucial to the development of insight than the provisions of the enabling legislation facilitating the new city and the highly interested language swirling around it. And without wanting to be overly actor-networky about things, this attention to detail — to the ways in which specific institutions, events, personalities, laws, and phrases mesh with one another to produce a state of affairs — is the most welcome aspect of the article at hand.
So what is Dholera?
We learn that it is a 903 km^2 development site in the Indian state of Gujarat, masterplanned by the UK engineering firm Halcrow, with some ten percent of its budget provided by the Indian national government and Japanese enterprises (specifically Hitachi, Mitsubishi, Toshiba, JGC and TEPCO), and the balance by unspecified but presumably domestic “private sector” entities.
We learn that — just like the canonical smart cities that came before it, among which the South Korean New Songdo and Abu Dhabi’s Masdar City remain the most widely known — Dholera is promised to be a place where embedded information-technological services and systems mediate every aspect of everyday urban life. Everything from personal mobility to access control and the functioning of the supply and removal chain is to be handled by IT vendors, “optimized” in the name of maximum efficiency. (That this model of urbanity bears no resemblance whatsoever to the way in which any Indian city current or historical functions doesn’t matter; indeed, as we’ll see, it’s an important part of the value proposition.)
And, like the canonical smart cities as well, we learn that Dholera is represented to the world by its backers as having been magicked into being from nothing, on what planners are pleased to call a “greenfield site.” But of course the terrain upon which this tomorrowland is to be built wasn’t precisely a bare Cartesian plane beforehand: parcels of land had to be acquired to make the project a reality, and the people living and working on those parcels dealt with somehow.
The particular history of this process of acquisition and displacement (and the local resistance to it) deserves sustained attention. In Datta’s telling, this smart city with Indian characteristics turns out to be a place where the impoverished people who currently occupy the site — subsistence farmers and fishers, mostly, with soil rendered barren by previous government misadventures in macroengineering, and a literacy rate that stands twenty full points lower than India’s average of 77% — have been convicted by more powerful forces of failing to fully capitalize on the value latent in their land, and sentenced to expulsion.
Here we see how a global body of rhetoric, promulgated primarily by all-but-placeless multinational enterprises, converges with powerful regional and class interests, squeezing the nation-state (and the better part of its citizenry) to a thin paste between them.
As Dholera functions within Gujarat, so does “investor-friendly” Gujarat function within India. Depicted by its own business-development initiative as “Vibrant Gujarat,” the state is home to both India’s Prime Minister Narendra Modi, and the philosophy of governance he pursued as the state’s Chief Minister between 2001 and 2012, a policy stance that has been described as “native-born neoliberalism.” For over a decade, the state has consciously positioned itself as a testbed for techniques and practices that are seen as efficacious in overwriting external perceptions of India as teeming, mired in grinding, endemic poverty, and riven by communal strife with an image more consonant with its ambitions and hopes. These techniques and practices amount to a “Gujarati model,” intended to realize “maximum governance with minimum government,” and a nation desperate to cut the fetters imposed by its legendary bureacratariat might well regard that model as a turnkey solution for its own economic problems. This was the broadly implicit — and at times, entirely explicit — message of the 2014 Modi campaign, and indeed the Gujarati model has become national policy since his election to the Prime Ministership.
In a pungent and fascinating passage, Datta traces the development of this discourse to Modi’s Asian peregrinations. Denied entry to the US, UK and EU for his role in fanning Gujarat’s vicious communal pogroms of 2002, Modi took to visiting China and Japan instead — and there, Datta argues, he encountered both a “Shanghai model” of top-down citymaking, readymade for transplantation into Gujarati soil, and a set of institutional partners prepared to foot the bill. The bitter irony is that identification with the program of crash urbanization that resulted is what lubricated Modi’s ongoing and evidently successful pivot from redhanded champion of the Hindu far right to polished global statesman — all of which suggests that well-intentioned British and American moves to marginalize the firebrand of 2002 may have resulted in the mother of all unintended consequences.
For reasons of investor confidence, if no other, it was important that whatever happened in Gujarat was seen to transpire under the rule of law. Ensuring the formal legitimation of whatever measures it deemed necessary in the development of Dholera, the State of Gujarat granted itself new powers of eminent domain, leaving the victims of dispossession quite literally no ground to stand on.
Here Datta embarks upon the sort of detailed explication that helps us understand how a single piece of legislation (specifically, Gujarat’s 2009 Special Investment Region Act) allowed the state to do an end run around provisions dating to 1894 that guaranteed small holders both due process and fair recompense for land forcibly acquired. Under SIR’s terms, the state simply had to designate land as “needed for public purpose,” while nothing compelled them to compensate those with existing tenure; and of course parcels assembled in this way could then be transferred to whatever “parastatal” or entirely private development partners the state found congenial.
But after all the farmers and fishermen have been chased from the land, the digital infrastructure laid down, and the golf club opened for business, what of day-to-day life in this environment? Here is where more of the detail that Datta lavishes on other aspects of her tale would be welcome; specifically, I would have appreciated a closer look at the social and spatial logic of this ostensibly smart place on Earth. But then Dholera, like Masdar and Songdo before it, may as yet (and forever will?) be too unrealized to permit this sort of interrogation. What we do know is that Dholera’s promise/premise of furnishing citizens with information allowing them to “manage their lives better” dovetails perfectly with the neoliberal fetish for the individualization of responsibility, and parallel deemphasis of collective or solidaristic action of any sort.
This, however, is only to scratch the surface of the questions we could ask of this project. If one were of a particularly sincere cast of mind, and thought that honest answers might actually be forthcoming, one might want to inquire as to why all of the following were so heedlessly conflated in Dholera’s conception:
— the perceived necessity of developing entirely new urbanized sites as a response to India’s social and economic transformation;
— the desirability of doing so using putatively “advanced,” i.e. imported, methodologies, practices and paradigms;
— the degree to which such models do or can speak to the needs and desires of the populations they are ostensibly intended to benefit;
— the suitability of informationtechnical systems and services as means to any ends articulated by these populations; and
— the identity of the institutions, organizations or actors best placed to achieve the conception and deployment of such systems.
But it may well be pointless to ask such things of Dholera’s promoters, or to subject their rhetoric to any particularly fine level of analysis. Like “eco,” “green,” and “sustainable,” it appears that “smart” is simply the latest in a series of terms used to wreathe development projects with a superficial gloss of contemporaneity. Indeed, despite the claims of advocates that it could only operate at peak efficiency when designed and purpose-
built from the ground up to accommodate information technology, Dholera wasn’t identified (by its developers, or anyone else) as a “smart city” until 2012, some three years after the project’s inception. This record suggests that, like any other sufficiently interested party, the actors responsible for developing Dholera are comfortable latching onto just about any old justificatory apparatus if it serves their shortterm needs — helping the project pass through the decision gate of an endorsement, authorizing vote or allocation of investment — and discard it without compunction once it has outlived its utility.
When someone lays out the evidence as clearly as Datta does here, one conclusion is unavoidable: the point was never, really, to engineer an all-seeing technological utopia. It was to dispossess the near-voiceless of their land and turn it to the ends of optimal revenue generation. At times, various actors involved in facilitating Dholera’s transformation from salinated flatland to smart city have been surprisingly forthright about their values and motivations. Datta quotes Amitabh Kant, CEO of a state-sponsored enterprise invested in the project, as mentioning that “the key challenge” to transforming India “will be to monetize land values.” Meanwhile, the Indian Intelligence Bureau justifies the surveillance it maintains on the lowercaste activists resisting the seizure of their land on the grounds of their involvement in “anti-
development activities.” The degree of cooptation of the apparatus of state on view here is truly impressive; not even in the US or the UK are the logics of accumulation by dispossession generally quite that close to the surface.
But why this particular confluence of ideas, why India, why now? We are often told, in the West, that India’s cities are now home to a growing middle class — crores of people liberated from the village and its strictures, flush with capital pumped into the economy by globalization’s outsourced engineering and back-office operations, and beginning to flex their consumerist muscles. Given the very real benisons of all this material comfort and choice, it may well be that those on the receiving end of it are not overly inclined to question where it all comes from.
One result of this is that perhaps nowhere else in the world has reified “IT” to the extent that India has. That I can tell, anyway, the acronym is universally understood at all levels of Indian society — and nearly as universally, is understood to be desirable for its connotations of efficiency, effortlessness, logic, cleanroom sterility and sheer modernity. It is a thing and a force both, something one applies to a circumstance to make it better. And to be a software engineer in this new economy is a noble thing, for in the end what else does so much of the new prosperity consist of but software?
In such a charged environment, the unremarkable trappings of postindustrial knowledge production acquire a curious valence in and of themselves. One can get some sense of what this looks like from Dholera’s promotional materials, which are dense with bombast and puffery of a distinctly Indian flavor: the state government’s act of having “appointed a consultant to develop the master plan of the project” and the involvement of “a global IT powerhouse” (transparently Cisco) are in themselves proffered — and perhaps even accepted by the intended audience — as guarantors of excellence.
I have no interest in judging an entire people’s aspirations, but the darker and more troubling aspects of all this are obvious. Among other things, the uncritical embrace of practices, arrangements and ways of doing things that originate in the developed world, and must therefore be “modern,” allows propositions that wouldn’t otherwise stand up to the slightest challenge to pass by without comment. Perhaps this explains why the State of Gujarat’s due diligence doesn’t seem to have extended to wondering why the masterplanner they entrusted with the development of a megacity destined to be twice the size of Mumbai — the UK-based engineering practice Halcrow — had no other successful urban planning engagements to its name, nor why its business fundamentals were evidently so weak that it had to be bailed out (and was eventually fully acquired) by the American concern C2HM HILL. Even mild skepticism regarding such matters would be powerfully salutary, particularly at a moment when the Modi government plans to plant one hundred such cities on the land.
This is why I’m glad to see Datta throw a little shade at relative optimists like Ashis Nandy, quoted here to the effect that whatever our reservations now, all will be well in the fullness of time, as India’s propensity to “corrupt” the bestlaid plans renders the alien imposition of the smart city “impure…but ultimately less malevolent.” This is charming, and undoubtedly contains an element of truth, but as Datta implies, it verges on irresponsible naivete given the power of certain imperial discourses to transform subjectivity.
And in the end, subjectivity — the realm of sentiment, hope, fear, pride and desire — is far more central to the whys and wherefores of Dholera than might appear to be the case. For a state that has solid historical justification for wanting to believe in the possibility of a new beginning, the notion of rebuilding from a truly clean slate must be all but irresistible. There are many, no doubt, for whom this insistence that the ghosts of 2002 be plowed under constitutes a virtue — indeed, a primary aspect of Dholera’s appeal. For millions of young Indians just now coming into their maturity, it’s easy to imagine how tempting it must be to treat historical events that happened in childhood as bygones that should remain bygones, and considerations of communal, religious or caste identity as baggage from the past that has no place in an India founded on gleaming clean-room technology. They may well be willing and more than willing to overlook the fiction of its origin myth, if Dholera’s promise of a new and glorious dawn can only be extended to Gujarat, to Narendra Modi, to India itself.
What remains clear is that others will pay the price for that wiping of the slate. Where master-planned High Modernist environments like Brasilia and Chandigarh at least paid lip service to the notion that issues of social justice could be addressed via the technics of the built environment, Dholera doesn’t even rise to that low standard. From the evidence at hand here, it seems certain that this smart city is destined to do little but inscribe further injustice and sorrow on the land. And for demonstrating just what the rhetoric of the smart city accomplishes on this particular terrain, at this particular moment in history, we have Ayona Datta to thank.
Consider the driverless car, as currently envisioned by Google.
That I can tell, anyway, most discussion of its prospects, whether breathlessly anticipatory or frankly horrendified, is content to weigh it more or less as given. But as I’m always harping on about, I just don’t believe we usefully understand any technology in the abstract, as it sits on a smoothly-paved pad in placid Mountain View. To garner even a first-pass appreciation for the contours of its eventual place in our lives, we have to consider what it would work like, and how people would experience it, in a specified actual context. And so here — as just such a first pass, at least — I try to imagine what would happen if autonomous vehicles like those demo’ed by Google were deployed as a service in the place I remain most familiar with, New York City.
The most likely near-term scenario is that such vehicles would be constructed as a fleet of automated taxicabs, not the more radical and frankly more interesting possibility that the service embracing them would be designed to afford truly public transit. The truth of the matter is that the arrival of the technological capability bound up in these vehicles begins to upend these standing categories…but the world can only accommodate so much novelty at once. The vehicle itself is only one component of an distributed actor-network dedicated to the accomplishment of mobility; when the autonomous vehicle begins to supplant the conventional taxi, that whole network has to restabilize around both the vehicle’s own capabilities and the ways in which those capabilities couple with other, existing actors.
In this case, that means actors like the Taxi and Limousine Commission. Enabling legislation, a body of suitable regulation, a controlling legal authority, the agreement on procedures for assessing liability to calibrate the furnishment of insurance: all of these things will need to be decided upon before any such thing as the automation of surface traffic in New York City can happen. And these provisions have a conservative effect. During the elapse of some arbitrary transitional period, anyway, they’ll tend to drag this theoretically disruptive actor back toward the categories we’re familiar with, the modes in which we’re used to the world working. That period may last months or it may last decades; there’s just no way of knowing ahead of time. But during this interregnum, we’ll approach the new thing through interfaces, metaphors and other linkages we’re already used to.
Automated taxis, as envisioned by designer Petr Kubik.
So. What can we reasonably assert of a driverless car on the Google model, when such a thing is deployed on the streets and known to its riders as a taxi?
On the plus side of the ledger:
– Black men would finally be able to hail a cab in New York City;
– So would people who use wheelchairs, folks carrying bulky packages, and others habitually and summarily bypassed by drivers;
– Sexual harassment of women riding alone would instantly cease to be an issue;
– You’d never have a driver slow as if to pick you up, roll down the window to inquire as to your destination, and only then decide it wasn’t somewhere they felt like taking you. (Yes, this is against the law, but any New Yorker will tell you it happens every damn day of the week);
– Similarly, if you happen to need a cab at 4:30, you’ll be able to catch one — getting stuck in the trenches of shift change would be a thing of the past;
– The eerily smooth ride of continuous algorithmic control will replace the lurching stop-and-go style endemic to the last few generations of NYC drivers, with everything that implies for both fuel efficiency and your ability to keep your lunch down.
These are all very good things, and they’d all be true no matter how banjaxed the service-design implementation turns out to be. (As, let’s face it, it would be: remember that we’re talking about Google here.) But as I’m fond of pointing out, none of these very good things can be had without cost. What does the flipside of the equation look like?
– Most obviously, a full-fleet replacement would immediately zero out some 50,000 jobs — mostly jobs held by immigrants, in an economy with few other decent prospects for their employment. Let’s be clear that these, while not great jobs (shitty hours, no benefits, physical discomfort, occasionally abusive customers), generate a net revenue that averages somewhere around $23/hour, and this at a time when the New York State minimum wage stands at $8/hour. These are jobs that tie families and entire communities together;
– The wholesale replacement of these drivers would eliminate one of the very few remaining contexts in which wealthy New Yorkers encounter recent immigrants and their culture at all;
– Though this is admittedly less of an issue in Manhattan, it does eliminate at least some opportunity for drivers to develop and demonstrate mastery and urban savoir faire;
– It would give Google, an advertising broker, unparalleled insight into the comings and goings of a relatively wealthy cohort of riders, and in general a dataset of enormous and irreplicable value;
– Finally, by displacing alternatives, and over the long term undermining the ecosystem of technical capabilities, human competences and other provisions that undergirds contemporary taxi service, the autonomous taxi would in time tend to bring into being and stabilize the conditions for its own perpetuation, to the exclusion of other ways of doing things that might ultimately be more productive. Of course, you could say precisely the same thing about contemporary taxis — that’s kind of the point I’m trying to make. But we should see these dynamics with clear eyes before jumping in, no?
I’m sure, quite sure, that there are weighting factors I’ve overlooked, perhaps even obvious and significant ones. This isn’t the whole story, or anything like it. There is one broadly observable trend I can’t help but noticing, however, in all the above: the benefits we stand to derive from deploying autonomous vehicles on our streets in this way are all felt in the near or even immediate term, while the costs all tend to be circumstances that only tell in the fullness of time. And we haven’t as a species historically tended to do very well with this pattern, the prime example being our experience of the automobile itself. It’s something to keep in mind.
There’s also something to be gleaned from Google’s decision to throw in their lot with Uber — an organization explicitly oriented toward the demands of the wealthy and boundlessly, even gleefully, corrosive of the public trust. And that is that you shouldn’t set your hopes on any mobility service Google builds on their autonomous-vehicle technology ever being positioned as the public accommodation or public utility it certainly could be. The decision to more tightly integrate Uber into their suite of wayfinding and journey-planning services makes it clear that for Google, the prerogative to maximize return on investment for a very few will always outweigh the interests of the communities in which they operate. And that, too, is something to keep in mind, anytime you hear someone touting all of the ways in which the clean, effortless autotaxi stands to resculpt the city.
UPDATE: Event confirmed for 14th March, 2014. See the final post.
For the past half-decade or so, in a phenomenon most everyone reading this site is no doubt already intimately acquainted with, data-derived artifacts (dynamic visualizations, digital maps, interactive representations of place-specific information, even static “infographics”) have taken increasing prominence in the visual imaginary of mass culture.
We see such images all the time now: broadly speaking, the visual rhetoric associated with them is the animating stuff of everything from car commercials to the weather forecast. The same rhetoric breathes life into election and sports coverage on television, the title sequences of movies, viral Facebook posts and the interactive features on newspaper sites.
Sometimes — in fact, often — these images are deployed as abstract tokens, empty fetishes of futurity, tech-ness, data-ness, evidence-basedness…ultimately, au-courantness. Just as often, and very problematically, they’re used to “prove” things.
But we’ve also begun to see the first inklings of ways in which such artifacts can be used more interestingly, to open up rather than shut down collective discussion around issues of great popular import — to ask its users to consider how and why the state of affairs represented by a given visualization got to be that way, whether that state of affairs is at all OK with them, and what if anything ought to be done to redress it. And this is whether the topic at hand happens to be land use, urban renewal and gentrification, informal housing, the differential consequences of public and privatized mass transit or expenditures in the criminal justice system.
Very few methods of advocacy can convey the consequences of our collective decisions as viscerally as a soundly-designed visualization. (Similarly, if there’s a better way of helping people imagine the spatial implications of alternative policy directions, strategies, investments and allocations, I haven’t stumbled onto it yet, although that certainly blurs the distinction between representing that which does exist and simulating that which does not.) What would happen if such visualizations were consciously and explicitly used as the ground text and point of departure for a moderated deliberative process? Could democracy be done this way? Could this be done at regular intervals? And how might doing so lead to better outcomes (or simply more buy-in) than existing procedures?
There’s plenty of rough precedent for such a notion, albeit scattered across a few different registers of activity:
– A few savvy journalists are starting to use data-based visualizations and maps as the starting point for their more traditional investigative efforts, and the narratives built on them. Visualizations, in this mode, essentially allow unexpected correlations and fact patterns to rise to the surface of awareness, and suggest what questions it might therefore be fruitful for a reporter to ask.
– SeeClickFix, of course, already allows citizens to levy demands on local government bodies, though it doesn’t provide for the organization of autonomous response to the conditions it documents, and it forthrightly positions the objects it represents as problems rather than matters of concern. More proactive and affirmative in its framing is Change By Us, which does emphasize voluntarism, though still with a sense of supplication to (elected or appointed) representatives in government. (The site answers the question “Who’s listening?” by promising that a “network of city leaders is ready to hear your ideas and provide guidance for your projects.”) In any event, both SeeClickFix and Change By Us focus on highly granular, literally pothole- or at most community-garden-scale issues.
– Storefront Democracy, a student project of Kristin Gräfe and (ex-Urbanscaler) Jeff Kirsch, reimagined the front window of a city councillor’s district office as a site where community sentiment on various questions, expressed as votes, could be visualized. Voting is not quite the same thing as democracy, much less deliberation, but the project began to explore ways in which situated representations might be used to catalyze conversations about matters facing the community.
– There are even full-blown technological platforms that promise to enable robust networked democracy, though for all the technology involved this one at least seems to blow right by the potential of visualized states of affairs to serve as focal points for managed dissensus.
Draw out all of those threads, and what do you wind up with? I’m not at all sure, but the question is certainly provocative enough that I want to explore its implications in further depth and detail. Again, I’m interested in digital cartography and interactive representations of data used as the starting point, rather than the product and culmination, of a decision process. My intention is to disturb these things as settled facts, disinter them from the loam of zeitgeisty but near-meaningless infoporn that furnishes more than one glossy coffee-table book, and activate them instead as situated social objects. I think by now it’s clear that data-driven projects like Digital Matatus can furnish people with practical tools to manage the way things are in the city. But can they usefully catalyze conversation about the way things could (or should) be? And can we somehow bundle information about provenance into every representation of data, allowing users to ask how it was gathered, by whom, using what means and for what notional purpose, so they can arrive at their own determinations of its reliability and relevance? All of that remains to be seen.
If you find yourself nodding at any of this — or, indeed, you think it’s all deeply misguided, but nevertheless worth contesting in person — consider this a heads-up that I’ll be convening a one-day seminar on this and related topics at LSE in mid-March, and am looking for qualified speakers beyond my personal orbit and existing friendship circles. If you’re interested in either attending or speaking, please do email me at your earliest convenience at my first initial dot my last name at lse.ac.uk. Limited travel support is available – I have an event budget that allows me to fly in two to three speakers and put you up in Central London for a night, so if you or someone you know is inclined to present I definitely encourage you to get in touch. And let’s see if together we can’t figure out if there’s a thing here or not.
Now that we’re finally slouching toward Amazon to be born — i.e. I’m confident that the Kindle edition, at least, will ship within the next ten days — I’m happy to be able to share this final bibliography for “Against the smart city.” I hope, as ever, you find it useful.
Alcatel-Lucent Corporation. “Getting Smart About Smart Cities: Understanding the Market Opportunity in the Cities of Tomorrow,” February 2012.
Alexander, Steve. “IBM wants Minneapolis to become a ‘smarter city,'” Minneapolis Star Tribune, 6 June 2011.
Allease, Eve. “Abu Dhabi, United Arab Emirates: Future Green City Now,” Urban Times, 22 May 2011.
Allianz Open Knowledge Initiative. “Masdar City: a desert utopia,” 30 March 2009.
Alusi, Annissa, Robert G. Eccles, Amy C. Edmondson and Tiona Zuzul. “Sustainable Cities: Oxymoron or the Shape of the Future?,” Harvard Business School Working Paper 11-062, 20 March 2011.
Amnesty International. “Amnesty International Report 2008: Americas Regional Update. Selected events covering the period from January to April 2008,” 28 May 2008.
— “‘We have come to take your souls’: the caveirão and policing in Rio de Janeiro,” 13 March 2006.
Android Open Source Project. “Licenses.”
Beer, Stafford. Platform for Change: A Message from Stafford Beer. Wiley, New York, 1975.
Bell, Genevieve and Paul Dourish. “Yesterday’s Tomorrows: Notes on Ubiquitous Computing’s Dominant Vision,” Personal and Ubiquitous Computing Volume 11 Issue 2, January 2007.
Bettencourt, Luís M.A., et al. “Growth, innovation, scaling, and the pace of life in cities,” Proceedings of the National Academy of Sciences, Volume 104 Number 17, 24 April 2007.
Biddle, Sam. “Racial Profiling: Newest Trend in Silicon Valley?,” Valleywag, 7 August 2013.
Black, Edwin. IBM and the Holocaust: The Strategic Alliance between Nazi Germany and America’s Most Powerful Corporation, Random House, New York, 2001.
Boudreau, John. “Cisco helps build prototype for instant cities,” San Jose Mercury News, 01 June 2010.
Brand, Stewart. How Buildings Learn, Viking Press, New York, 1994.
Brewster, Kent. “Profiling Atherton,” July 2013.
Buro Happold. “Projects: PlanIT Valley.”
Carlisle, Tamsin. “Masdar City clips another $2.5bn from price tag,” The National, 1 December 2010.
Chalmers, Matthew and Ian MacColl. “Seamful and Seamless Design in Ubiquitous Computing,” Technical Report Equator-03-005, 2004.
Chen, David W. “Survey Raises Questions on Data-Driven Policy,” The New York Times, 8 February 2010.
Chomsky, Noam. “The Case Against B.F. Skinner,” New York Review of Books, 30 December 1971.
Cisco Systems. “Cisco and Lake Nona Unite to Create First U.S. Iconic Smart+Connected Community in Orlando, Florida,” 24 October 2012.
— “Cisco Contributes to Open Source.”
— “Cities of the Future: Songdo, South Korea,” 2012.
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The following is section 4 of “Against the smart city,” the first part of The City Is Here For You To Use. Our Do projects will be publishing “Against the smart city” in stand-alone POD pamphlet and Kindle editions later on this month.
4 | The smart city pretends to an objectivity, a unity and a perfect knowledge that are nowhere achievable, even in principle.
Of the major technology vendors working in the field, Siemens makes the strongest and most explicit statement of the philosophical underpinnings on which their (and indeed the entire) smart-city enterprise is founded: “Several decades from now cities will have countless autonomous, intelligently functioning IT systems that will have perfect knowledge of users’ habits and energy consumption, and provide optimum service…The goal of such a city is to optimally regulate and control resources by means of autonomous IT systems.”
We’ve already considered what kind of ideological work is being done when efforts like these are positioned as taking place in some proximate future. The claim of perfect competence Siemens makes for its autonomous IT systems, though, is by far the more important part of the passage. It reflects a clear philosophical position, and while this position is more forthrightly articulated here than it is anywhere else in the smart-city literature, it is without question latent in the work of IBM, Cisco and their peers. Given its foundational importance to the smart-city value proposition, I believe it’s worth unpacking in some detail.
What we encounter in this statement is an unreconstructed logical positivism, which, among other things, implicitly holds that the world is in principle perfectly knowable, its contents enumerable, and their relations capable of being meaningfully encoded in the state of a technical system, without bias or distortion. As applied to the affairs of cities, it is effectively an argument there is one and only one universal and transcendently correct solution to each identified individual or collective human need; that this solution can be arrived at algorithmically, via the operations of a technical system furnished with the proper inputs; and that this solution is something which can be encoded in public policy, again without distortion. (Left unstated, but strongly implicit, is the presumption that whatever policies are arrived at in this way will be applied transparently, dispassionately and in a manner free from politics.)
Every single aspect of this argument is problematic.
— Perfectly knowable, without bias or distortion: Collectively, we’ve known since Heisenberg that to observe the behavior of a system is to intervene in it. Even in principle, there is no way to stand outside a system and take a snapshot of it as it existed at time T.
But it’s not as if any of us enjoy the luxury of living in principle. We act in historical space and time, as do the technological systems we devise and enlist as our surrogates and extensions. So when Siemens talks about a city’s autonomous systems acting on “perfect knowledge” of residents’ habits and behaviors, what they are suggesting in the first place is that everything those residents ever do — whether in public, or in spaces and settings formerly thought of as private — can be sensed accurately, raised to the network without loss, and submitted to the consideration of some system capable of interpreting it appropriately. And furthermore, that all of these efforts can somehow, by means unspecified, avoid being skewed by the entropy, error and contingency that mark everything else that transpires inside history.
Some skepticism regarding this scenario would certainly be understandable. It’s hard to see how Siemens, or anybody else, could avoid the slippage that’s bound to occur at every step of this process, even under the most favorable circumstances imaginable.
However thoroughly Siemens may deploy their sensors, to start with, they’ll only ever capture the qualities about the world that are amenable to capture, measure only those quantities that can be measured. Let’s stipulate, for the moment, that these sensing mechanisms somehow operate flawlessly, and in perpetuity. What if information crucial to the formulation of sound civic policy is somehow absent from their soundings, resides in the space between them, or is derived from the interaction between whatever quality of the world we set out to measure and our corporeal experience of it?
Other distortions may creep into the quantification of urban processes. Actors whose performance is subject to measurement may consciously adapt their behavior to produce metrics favorable to them in one way or another. For example, a police officer under pressure to “make quota” may issue citations for infractions she would ordinarily overlook; conversely, her precinct commander, squeezed by City Hall to present the city as an ever-safer haven for investment, may downwardly classify felony assault as a simple misdemeanor. This is the phenomenon known to viewers of The Wire as “juking the stats,” and it’s particularly likely to happen when financial or other incentives are contingent on achieving some nominal performance threshold. Nor is it the only factor likely to skew the act of data collection; long, sad experience suggests that the usual array of all-too-human pressures will continue to condition any such effort. (Consider the recent case in which Seoul Metro operators were charged with using CCTV cameras to surreptitiously ogle women passengers, rather than scan platforms and cars for criminal activity as intended.)
What about those human behaviors, and they are many, that we may for whatever reason wish to hide, dissemble, disguise, or otherwise prevent being disclosed to the surveillant systems all around us? “Perfect knowledge,” by definition, implies either that no such attempts at obfuscation will be made, or that any and all such attempts will remain fruitless. Neither one of these circumstances sounds very much like any city I’m familiar with, or, for that matter, would want to be.
And what about the question of interpretation? The Siemens scenario amounts to a bizarre compound assertion that each of our acts has a single salient meaning, which is always and invariably straightforwardly self-evident — in fact, so much so that this meaning can be recognized, made sense of and acted upon remotely, by a machinic system, without any possibility of mistaken appraisal.
The most prominent advocates of this approach appear to believe that the contingency of data capture is not an issue, nor is any particular act of interpretation involved in making use of whatever data is retrieved from the world in this way. When discussing their own smart-city venture, senior IBM executives argue, in so many words, that “the data is the data”: transcendent, limpid and uncompromised by human frailty. This mystification of “the data” goes unremarked upon and unchallenged not merely in IBM’s material, but in the overwhelming majority of discussions of the smart city. But different values for air pollution in a given location can be produced by varying the height at which a sensor is mounted by a few meters. Perceptions of risk in a neighborhood can be transformed by altering the taxonomy used to classify reported crimes ever so slightly. And anyone who’s ever worked in opinion polling knows how sensitive the results are to the precise wording of a survey. The fact is that the data is never “just” the data, and to assert otherwise is to lend inherently political and interested decisions regarding the act of data collection an unwonted gloss of neutrality and dispassionate scientific objectivity.
The bold claim of perfect knowledge appears incompatible with the messy reality of all known information-processing systems, the human individuals and institutions that make use of them and, more broadly, with the world as we experience it. In fact, it’s astonishing that anyone would ever be so unwary as to claim perfection on behalf of any computational system, no matter how powerful.
— One and only one solution: With their inherent, definitional diversity, layeredness and complexity, we can usefully think of cities as tragic. As individuals and communities, the people who live in them hold to multiple competing and equally valid conceptions of the good, and it’s impossible to fully satisfy all of them at the same time. A wavefront of gentrification can open up exciting new opportunities for young homesteaders, small retailers and craft producers, but tends to displace the very people who’d given a neighborhood its character and identity. An increased police presence on the streets of a district reassures some residents, but makes others uneasy, and puts yet others at definable risk. Even something as seemingly straightforward and honorable as an anticorruption initiative can undo a fabric of relations that offered the otherwise voiceless at least some access to local power. We should know by now that there are and can be no Pareto-optimal solutions for any system as complex as a city.
— Arrived at algorithmically: Assume, for the sake of argument, that there could be such a solution, a master formula capable of resolving all resource-allocation conflicts and balancing the needs of all a city’s competing constituencies. It certainly would be convenient if this golden mean could be determined automatically and consistently, via the application of a set procedure — in a word, algorithmically.
In urban planning, the idea that certain kinds of challenges are susceptible to algorithmic resolution has a long pedigree. It’s already present in the Corbusian doctrine that the ideal and correct ratio of spatial provisioning in a city can be calculated from nothing more than an enumeration of the population, it underpins the complex composite indices of Jay Forrester’s 1969 Urban Dynamics, and it lay at the heart of the RAND Corporation’s (eventually disastrous) intervention in the management of 1970s New York City. No doubt part of the idea’s appeal to smart-city advocates, too, is the familial resemblance such an algorithm would bear to the formulae by which commercial real-estate developers calculate air rights, the land area that must be reserved for parking in a community of a given size, and so on.
In the right context, at the appropriate scale, such tools are surely useful. But the wholesale surrender of municipal management to an algorithmic toolset — for that is surely what is implied by the word “autonomous” — would seem to repose an undue amount of trust in the party responsible for authoring the algorithm. At least, if the formulae at the heart of the Siemens scenario turn out to be anything at all like the ones used in the current generation of computational models, critical, life-altering decisions will hinge on the interaction of poorly-defined and surprisingly subjective values: a “quality of life” metric, a vague category of “supercreative” occupations, or other idiosyncrasies along these lines. The output generated by such a procedure may turn on half-clever abstractions, in which a complex circumstance resistant to direct measurement is represented by the manipulation of some more easily-determined proxy value: average walking speed stands in for the more inchoate “pace” of urban life, while the number of patent applications constitutes an index of “innovation.”
Even beyond whatever doubts we may harbor as to the ability of algorithms constructed in this way to capture urban dynamics with any sensitivity, the element of the arbitrary we see here should give us pause. Given the significant scope for discretion in defining the variables on which any such thing is founded, we need to understand that the authorship of an algorithm intended to guide the distribution of civic resources is itself an inherently political act. And at least as things stand today, neither in the Siemens material nor anywhere else in the smart-city literature is there any suggestion that either algorithms or their designers would be subject to the ordinary processes of democratic accountability.
— Encoded in public policy, and applied transparently, dispassionately and in a manner free from politics: A review of the relevant history suggests that policy recommendations derived from computational models are only rarely applied to questions as politically sensitive as resource allocation without some intermediate tuning taking place. Inconvenient results may be suppressed, arbitrarily overridden by more heavily-weighted decision factors, or simply ignored.
The best-documented example of this tendency remains the work of the New York City-RAND Institute, explicitly chartered to implant in the governance of New York City “the kind of streamlined, modern management that Robert McNamara applied in the Pentagon with such success” during his tenure as Secretary of Defense (1961-1968). The statistics-driven approach that McNamara’s Whiz Kids had so famously brought to the prosecution of the war in Vietnam, variously thought of as “systems analysis” or “operations research,” was first applied to New York in a series of studies conducted between 1973 and 1975, in which RAND used FDNY incident response-time data to determine the optimal distribution of fire stations.
Methodological flaws undermined the effort from the outset. RAND, for simplicity’s sake, chose to use the time a company arrived at the scene of a fire as the basis of their model, rather than the time at which that company actually began fighting the fire; somewhat unbelievably, for anyone with the slightest familiarity with New York City, RAND’s analysts then compounded their error by refusing to acknowledge traffic as a factor in response time. Again, we see some easily-measured value used as a proxy for a reality that is harder to quantify, and again we see the distortion of ostensibly neutral results by the choices made by an algorithm’s designers. But the more enduring lesson for proponents of data-driven policy has to do with how the study’s results were applied. Despite the mantle of coolly “objective” scientism that systems analysis preferred to wrap itself in, RAND’s final recommendations bowed to factionalism within the Fire Department, as well as the departmental leadership’s need to placate critical external constituencies; the exercise, in other words, turned out to be nothing if not political.
The consequences of RAND’s intervention were catastrophic. Following their recommendations, fire battalions in some of the most vulnerable sections of the city were decommissioned, while the department opened other stations in low-density, low-threat areas; the spatial distribution of firefighting assets remaining actually prevented resources from being applied where they were most critically needed. Great swaths of the city’s poorest neighborhoods burned to the ground as a direct result — most memorably the South Bronx, but immense tracts of Manhattan and Brooklyn as well. Hundreds of thousands of residents were displaced, many permanently, and the unforgettable images that emerged fueled perceptions of the city’s nigh-apocalyptic unmanageability that impeded its prospects well into the 1980s. Might a less-biased model, or a less politically-skewed application of the extant findings, have produced a more favorable outcome? This obviously remains unknowable…but the human and economic calamity that actually did transpire is a matter of public record.
Examples like this counsel us to be wary of claims that any autonomous system will ever be entrusted with the regulation and control of civic resources — just as we ought to be wary of claims that the application of some single master algorithm could result in an Pareto-efficient distribution of resources, or that the complex urban ecology might be sufficiently characterized in data to permit the effective operation of such an algorithm in the first place. For all of the conceptual flaws we’ve identified in the Siemens proposition, though, it’s the word “goal” that just leaps off the page. In all my thinking about cities, it has frankly never occurred to me to assert that cities have goals. (What is Cleveland’s goal? Karachi’s?) What is being suggested here strikes me as a rather profound misunderstanding of what a city is. Hierarchical organizations can be said to have goals, certainly, but not anything as heterogeneous in composition as a city, and most especially not a city in anything resembling a democratic society.
By failing to account for the situation of technological devices inside historical space and time, the diversity and complexity of the urban ecology, the reality of politics or, most puzzlingly of all, the “normal accidents” all complex systems are subject to, Siemens’ vision of cities perfectly regulated by autonomous smart systems thoroughly disqualifies itself. But it’s in this depiction of a city as an entity with unitary goals that it comes closest to self-parody.
If it seems like breaking a butterfly on a wheel to subject marketing copy to this kind of dissection, I am merely taking Siemens and the other advocates of the smart city at their word, and this is what they (claim to) really believe. When pushed on the question, of course, some individuals working for enterprises at the heart of the smart-city discourse admit that what their employers actually propose to do is distinctly more modest: they simply mean to deploy sensors on municipal infrastructure, and adjust lighting levels, headway or flow rates to accommodate real-time need. If this is the case, perhaps they ought to have a word with their copywriters, who do the endeavor no favors by indulging in the imperial overreach of their rhetoric. As matters now stand, the claim of perfect competence that is implicit in most smart-city promotional language — and thoroughly explicit in the Siemens material — is incommensurate with everything we know about the way technical systems work, as well as the world they work in. The municipal governments that constitute the primary intended audience for materials like these can only be advised, therefore, to approach all such claims with the greatest caution.
 For example, in New York City, an anonymous survey of “hundreds of retired high-ranking [NYPD] officials” found that “tremendous pressure to reduce crime, year after year, prompted some supervisors and precinct commanders to distort crime statistics” they submitted to the centralized COMPSTAT system. Chen, David W., “Survey Raises Questions on Data-Driven Policy,” The New York Times, 08 February 2010.
 Simon, David, Kia Corthron, Ed Burns and Chris Collins, The Wire, Season 4, Episode 9: “Know Your Place,” first aired 12 November 2006.
 Fletcher, Jim, IBM Distinguished Engineer, and Guruduth Banavar, Vice President and Chief Technology Officer for Global Public Sector, personal communication, 08 June 2011.
 Migurski, Michal. “Visualizing Urban Data,” in Segaran, Toby and Jeff Hammerbacher, Beautiful Data: The Stories Behind Elegant Data Solutions, O’Reilly Media, Sebastopol CA, 2012: pp. 167-182. See also Migurski, Michal. “Oakland Crime Maps X,” tecznotes, 03 March 2008.
 See, as well, Sen’s dissection of the inherent conflict between even mildly liberal values and Pareto optimality. Sen, Amartya Kumar. “The impossibility of a Paretian liberal.” Journal of Political Economy Volume 78 Number 1, Jan-Feb 1970.
 Forrester, Jay. Urban Dynamics, The MIT Press, Cambridge, MA, 1969.
 See Flood, Joe. The Fires: How a Computer Formula Burned Down New York City — And Determined The Future Of American Cities, Riverhead Books, New York, 2010.
 See, e.g. Bettencourt, Luís M.A. et al. “Growth, innovation, scaling, and the pace of life in cities,” Proceedings of the National Academy of Sciences, Volume 104 Number 17, 24 April 2007, pp. 7301-7306.
 Flood, ibid., Chapter Six.
 Rider, Kenneth L. “A Parametric Model for the Allocation of Fire Companies,” New York City-RAND Institute report R-1615-NYC/HUD, April 1975; Kolesar, Peter. “A Model for Predicting Average Fire Company Travel Times,” New York City-RAND Institute report R-1624-NYC, June 1975.
 Perrow, Charles. Normal Accidents: Living with High-Risk Technologies, Basic Books, New York, 1984.