By now nearly everyone reading these words has heard that over half of the earth’s population lives in cities. Cities vary widely in how well they function and for whose benefit. Nearly everyone can agree that some kind of planning is required for cities to function well for their residents, but philosophies concerning what kind of planning differ widely, as when the writer and activist Jane Jacobs excoriated the urban planning efforts of Robert Moses, known as “the master builder”, in her book The Death and Life of Great American Cities.
More recently, the concept of “smart cities” has become popular, based in part on the involvement of tech giants such as IBM. Can newfangled technology such as an “Internet of things” make cities smart? Regardless of the answer to this question, the term “smart cities” is useful because it encourages us to think of a city as a single organism capable of making intelligent collective decisions. That’s an important goal to strive for, no matter whether the solutions end up being high-tech or low-tech.
An example of the “city as organism” metaphor is 311, a three-digit telephone number that the residents of some cities can call to report minor dysfunctions such as a pothole, a fallen tree, or graffiti. It first arose as a “cultural mutation” in the city of Baltimore as a way to handle calls that were inappropriate for 911, which should be reserved for life and death matters. Soon it became apparent that 311 could be useful in its own right by having residents serve as a kind of perceptual organ, relaying information to the relevant departments of the city administration so that they can deploy their services without needing to gather the information themselves. 311 is often described as the “eyes” and “ears” of the city, much in keeping with the ideal of a city as a single organism, complete with organs of perception and a nervous system.
311 is a success story of the Smart Cities movement, implemented in over 400 cities in America. Someone who knows a lot about it is Daniel T. O’Brien, Associate Professor of Public Policy and Urban Affairs at Northeastern University and co-director of the Boston Area Research Initiative (BARI), a consortium of universities that works with the city of Boston to use data and technology to simultaneously advance both science and policy. His recent book published by Harvard University Press, The Urban Commons: How Data and Technology Can Rebuild Our Communities, was awarded the Dennis Judd Best Book Award by the American Political Science Association. Our conversation covers the history of city planning, the smart cities movement, and 311 as a success story within the movement. All of these cases allow a comparison of laissez-faire, centralized planning, and the Third Way of entrepreneurship and all other forms of positive social change.
David Sloan Wilson: Welcome, Dan! In the spirit of full disclosure, our readers should know that you received your PhD with me at Binghamton University, where you helped me to develop the Binghamton Neighborhood Project. What you have done in Boston far outstrips what I have continued to do in Binghamton. I couldn’t be more proud and will return to my own challenges in Binghamton later in our conversation.
Daniel T. O’Brien: It’s a pleasure to be chatting about this with you, David. Thanks for inviting the conversation. In regards to the two B’ton projects, I couldn’t have accomplished any of what we’ve done here in Boston without the experience in Binghamton, which taught me that knocking on doors, being persistent, and convincing myself and others that scientists—including those who have not traditionally collaborated with city officials—could offer much to the continued evolution of urban policy and practice. To boot, in Boston I am privileged to be surrounded by many, many institutions, from public to private to non-profit to academic, that are excited to engage collaboratively in this work.
DSW: Yet, coordinating all of that does not self-organize! I want to begin with the broad history of urban planning before proceeding to the smart cities movement and 311 as a success story within that movement. Why is urban planning needed by cities at all? In other words, what happens to a city if we take “laissez-faire” seriously and just let it be?
DTO: To put it in evolutionary terms, urban planning is a group-level adaptation. If private citizens were left entirely to their own devices, they would build and modify in ways that could easily become counter-productive to society as a whole. In its least problematic form, this could manifest as inefficiency—for example, cities settled before the development of grid systems tend to have circuitous road maps that are difficult to navigate—and at worst counter-productive, with developers making decisions that benefit themselves but have negative externalities for neighboring communities. Planning is essential to make sure that the greater good of the city is protected and realized.
A similar logic applies to the development of city services, as the infrastructure that is necessary for a city to even exist typically requires expertise and equipment that very few individuals would have—nor would we want them to. Think, for a moment, about whether you would want to trust your neighbor to own a bucket truck or street paver, and would further want to rely on him to pave the road in front of his house, and at the same time be obliged to do the same for the stretch of road in front of your house.
DSW: Right! I like your point about road construction. In my vision statement, I mention the collective inefficiencies of people driving on roads. You’re making the same point about the roads themselves. Next, the history of urban planning includes many examples of top-down efforts, such as by the “master builder” Robert Moses in New York City. What is the track record of centralized planning efforts?
DTO: Mixed at best. We learned the lesson that we need planning of some sort first. Inevitably, as these things go, it was misinterpreted to mean that a centralized body needed to make all decisions in what in practice became a rather dictatorial manner. Efforts by Moses and his ilk are generally seen as having disrupted and even completely eliminating communities without their input and replacing them with developments that then often had mediocre results. That balances out to a net negative.
DSW: OK, if doing nothing doesn’t work and centralized planning doesn’t work, how about relying on the private sector? Does entrepreneurship as typically imagined—encouraging business start-ups in an unfettered market—have a better track record than centralized planning?
DTO: Not particularly. In all reality, most times we talk about the role of the private sector in urban planning and development, it is as the developer. Those are the interests that I noted above as being in potential competition with each other or even with the greater public good. If we turn again briefly to city services, however, entrepreneurs in recent years have been proposing many more tools and technologies for improving city services, which makes them more of a partner to public agencies than the object of regulation. I’m sure we’ll return to this shortly as we discuss smart cities.
DSW: Right! I would hope that the private sector can play a large role in Third Way governance, which merely requires orienting it in the right way. That brings us to the Third Way of entrepreneurship and all forms of social change, which requires: a) choosing a systemic target of selection (in this case a well-functioning city); b) orienting variation around the target and c) mindfully replicating best practices in a way that is sensitive to context. Are there any examples of this in the history of urban planning? Does Jane Jacobs represent such an approach?
DTO: This is a useful framing, but as you well know, we need to be very specific in what the unit of variation and selection is that we care about. Selection is not for a well-functioning city, just as it is not for an organism on the whole. It is for the traits therein. In biology, we select on traits vis-à-vis their impact on survival and reproduction, even if some are rather distal. My noting this might be bit esoteric for some readers, but I think it is useful to the discussion here for the following reason. Urban planners have never disagreed on the ultimate goal—thriving neighborhoods—but they have disagreed on the traits necessary to arrive there. Thus, in what amounts to artificial cultural selection, they have different logics for what will make for a thriving neighborhood.
The battle between Moses and Jacobs reflects a crucial tension in this regard, one that ultimately vindicated Jacobs’ criticisms. Moses saw the aging of a neighborhood’s infrastructure and the poverty of those who lived there as clear evidence that the place was a failure. Jacobs argued that this was myopic in that it omitted critical social dynamics that might act as a resource to a neighborhood in lieu of financial or institutional wealth. In the opening to her book, The Death and Life of American Cities, she describes Boston’s North End as a poor place with high-quality health and educational outcomes, rejecting the argument that it should be razed (which was considered by the Boston Redevelopment Authority) and stating that this was a tight-knit community characterized by what we would now call “high social capital.” Thus, she wanted planning to maximize these social elements, whereas Moses was more concerned with the amenities themselves.
DSW: OK, there is quite a bit to unpack here. I agree that only by selecting specific traits, such as running speed in an organism or the distribution of traffic lights in a city, can we improve the functioning of a whole organism or the whole city. Nevertheless, especially in the case of artificial cultural selection, it is necessary to monitor the effects of the trait on the functioning of the whole. You acknowledge this yourself when you say that all city planners are working toward thriving neighborhoods and merely disagree on how to do it. In my Vision Statement, I give the example of an automobile assembly plant testing different solutions to small inefficiencies. This is very much a trait-based approach, which nevertheless requires a whole-system evaluation to know which traits to select. My point is that the situation is no different for urban planning, except for a lot less control and precision in determining the whole-system consequences of implementing a given policy.
The case of Moses vs. Jacobs raises a new and important issue. They didn’t just differ in specific policy solutions, or traits as you put it. They differed in their entire philosophies. Jacobs thought that Moses was tone-deaf about the quality of urban life, which means that almost any specific policy suggestion he was likely to make would be wrong from her perspective. Testing and selecting different perspectives is more difficult than testing and selection of different specific policy “traits”. This is what Thomas Kuhn was reaching for with his concept of paradigms.
DTO: Agreed. When it comes to urban planning—or any other exercise of goal-driven design—the entire approach will be defined by your objective, which is generally rooted in your chosen metric of “success.” Moses and Jacobs clashed in that they disagreed on that objective metric, and, as importantly, on the factors and processes that they believed to contribute to success. In Kuhnian terms, as you suggest, they had completely different paradigms. Moses’ was oriented towards the infrastructure itself, with a certain ignorance for “softer” measures that not only mattered to Jacobs but that we have come to embrace now, such as social capital and quality of life.
DSW: Great! Our overview of laissez-faire, centralized planning, and the Third Way for the broad history of urban planning has been helpful. Now I’d like to repeat it for the more recent Smart Cities movement. Could you please provide a concise summary of how this movement got started?
DTO: This “movement” has an interesting history when framed this way, and one whose near-future I find fascinating, but also sitting at a consequential turning point. Smart cities technologies are altering city services with objectives that we might describe as selecting specific traits (the engineers would call this “optimization”). Smart cities in some ways started in a few testbeds, the most prominent being Singapore, where the goal has been to use information to maximize efficiency. This has occurred alongside movements in the United States to leverage administrative data to track performance metrics for public agencies—programs like CityStat in Baltimore, MD. This is in many ways resurrecting the centralized approach that you noted earlier. The government is assuming that efficiency is what matters and then evolving city services to accomplish that purpose, rather than exploring what communities really want. The thing is, they are being enabled and even misled in this by private corporations who have imagined solutions to the problems of urban communities without consulting actual residents.
Thus, there is an evolution required here at two levels. Conceptually, what is it that we actually want to maximize / select for / optimize for? And the process for getting there will require more communication with communities and fewer closed-loop conversations between private corporations (as sellers) and policymakers (as clients).
DSW: These are great points. Let me play them back to be sure we are on the same page. A city government or private corporation thinks it knows how to make a city smart and goes about doing it in their own way. This requires a variation, selection, and replication process and therefore looks like the Third Way. Unfortunately, what they thought would make the city smart proves not to be the case. They have no way of knowing this, however, because they never consulted the residents of the city. They succeeded in implementing their plan but not their ultimate goal.
Examples like this can be cited almost without end, such as national indicators of economic growth and performance metrics for public school students and teachers. The lesson to be learned is: Never use a proxy as a target of selection unless you can be sure that it represents what you are actually trying to select!
DTO: This is absolutely right. I might reframe slightly to say that city governments and private corporations have collaborated in the use of “smart city” technologies to improve the city. At the outset, they assumed that the objective outcome we wanted to pursue—and thus be the rubric for the process of variation, selection, and replication that you describe—was efficiency. In some cases, it is not even clear what the overarching objective was apart from developing and using fancy new gadgets. It has become increasingly clear, however, that mere efficiency is not necessarily what communities always want or need. If the objective is going to drive the process of evolution of the city, and we want that evolution to provide the greatest benefit to the greatest number of people, then we need to have a very serious collective conversation at the outset, and throughout, about what the objectives are.
DSW: How do you get around this problem for BARI? What is your own approach to making Boston smart? Let’s discuss BARI in general before focusing on 311.
DTO: Three things help us to solve this at BARI: an initial predisposition towards questions of social import; widespread collaboration across institutions and disciplines; and our own process of variation, selection, and replication. Taking those in order, the first is that we are one of the few smart cities-oriented centers that were not hatched out of a school of engineering or other tech-focused endeavors. As such, we are always interested in how modern digital data and technology can help us to better understand the social workings of the city, and thus never really needed to engage in course correction away from an overemphasis on efficiency or fancy-but-superficial products. Second, we had a stated mission to nurture a thriving civic data ecosystem in greater Boston in which we would engage with and convene any and all researchers, policymakers, practitioners, private corporations, and community leaders that were interested in using data and technology in their work. This has led us to be active in conversation with dozens of collaborators and colleagues, some of whom are highly technical, others of whom are qualitative researchers or community-based organizations. It provides a broad perspective on what the people and communities and civic leaders of Boston really care about, and the sorts of work that we all should be doing, not just BARI but the broader research-policy community of which we are a part.
Turning to the evolution of our work, I have to give particular credit to my colleagues at the Mayor’s Office of New Urban Mechanics (MONUM), a rather distinctive office that describes itself as the civic R&D team for the city. We have worked together from the beginning, thinking conceptually about how we pursue projects that have the greatest impact. They have been real leaders in developing programming that engages the public on questions of technology-oriented policy. And with every project, with every bit of learning they gain from interactions with their constituents, we iterate a bit more on our model. Thanks to these insights, BARI is in the process of shifting its mission statement from being just about using data and technology, to how we can use it specifically to advance equity, justice, and democracy. Because these are the things that Boston wants and needs right now.
DSW: That’s awesome—really impressive. Now we get to 311! Tell us how it became your special focus.
DTO: In a sense, by convenience, but in another sense, because it was the obvious case study crying out for attention. In 2011, as BARI was just getting started, we were casting around with our partners at MONUM for a good proof of concept for our data-driven research-policy approach. We realized the 311 was the perfect answer. It was a brand-new policy system that was little understood and had been gaining traction with municipalities across the country. It generated a clean database that had novel content—no one had ever systematically documented hundreds of thousands of moments in which everyday urbanites took action to maintain and improve public spaces and infrastructure. As such, there was a natural dual opportunity: to learn about what I have since dubbed “custodianship in the urban commons,” what we might call a new scholarly line of research; and to use those discoveries to illuminate the inner workings of the 311 system itself, and to support the further refinement of this civic innovation. 311 was also in keeping with the discussion about values above because it was all about democracy: how does a technological system incorporate constituents more effectively into public maintenance?
DSW: One important result of your inquiry is that most people use 311 to report dysfunctions in their immediate neighborhoods, not the city as a whole. Yet, there are exceptions to this rule. Could you please elaborate?
DTO: One of the earliest findings—and still one of the most striking—was that 80% of reports referenced issues that were within two blocks of the home of the person reporting it. Taking it further, the median distance of a report from the reporter’s home is seven meters. Literally right in front of the person’s home! Likewise, 80% of users never breach this two-block barrier. That said, as you note, the other 20% of individuals and reports are present in the data, and there are people who have reported from all over the city. One of my favorite examples is a person who after Hurricane Irene apparently went all around the city finding fallen trees to report; after further investigation, I’m confident he was acting as a private citizen and not in any official capacity.
From an analytic perspective, it is intriguing to consider both of these behaviors—local and wide-ranging reporting. But from a practical perspective, it is important to keep in mind that 311 is a sociotechnical system that depends on its inputs. If 80% of the inputs are within two blocks of a person’s home, the municipal leaders who build and manage it should recognize that that is the energy that makes 311 work. That has major implications for how one would organize and articulate outreach to communities.
DSW: Another important result is that some people are motivated to report physical dysfunctions, such as a pothole, and other people are motivated to support social infractions, such as graffiti. This sounds a lot like the distinction between cooperators and punishers in game theory models. Could you please elaborate and is that a valid comparison?
DTO: That’s a very interesting analogy. The result you describe is exactly correct. We find a division of labor between individuals that specialize in “naturally-occurring deterioration” on the one end and “manmade incivilities” on the other. Interestingly, the latter also report in surveys that they are more concerned with enforcing norms and protecting the neighborhood. You could certainly make the case that this aligns well with classical models of cooperation—“I want to contribute to my community”—and punishment—“I want to stop people who hurt my community.” It is actually akin to our colleague Omar Eldakar’s model of selfish punishment in which he argues that the effort associated with both cooperation and punishment add up, meaning it becomes advantageous for individuals to pick one or the other (note: Eldakar also makes the argument that punishers are often themselves selfish and want to eliminate competition for taking advantage of the group, though that does not fit as cleanly within this particular set of results).
DSW: For 311 to function for the benefit of the city as a whole, it will need to correct for inevitable biases in its use by the various ethnic and socioeconomic categories. How do you go about measuring and correcting for these biases?
DTO: This was one of the first questions we tangled when we started the project. At the time we simply wanted to measure neighborhood conditions through the reports, treating them as the “eyes and ears of the city.” We realized quickly that this would be impossible if the eyes and ears of the city were hearing and seeing better in some neighborhoods than others. So we conducted an audit study wherein I worked with a team of nine undergraduate students at UMass Boston, where I was teaching at the time, to identify street light outages across the city. We then used the 311 database to determine how quickly they were reported by community members. Likewise, the City assessed the quality of every sidewalk in Boston, and we aligned those data with reports of broken sidewalks. This allowed us to quantify the question, “Given a street light outage, broken sidewalk, or, presumably, other public issues, what’s the likelihood someone in this neighborhood would come along and report it?” We also found that you could predict this propensity to report by tabulating the number of custodians living in a neighborhood, based on registered accounts, allowing us to automate the measure. This was extremely useful for then adjusting measures of neighborhood conditions based on not only the quantity but also the likelihood of reports.
These early discoveries redirected our work in two major ways on the scientific and policymaking sides. For me, it revealed that custodianship itself was the more interesting and immediately accessible question to be studied within these data. Sure, we may have solved the measurement issue, but this question of why one would report issues in the public space was more fertile ground for inquiry and for understanding how this policy innovation will (or will not) impact urban neighborhoods. In the book, one of the chapters actually explores how different neighborhoods are more or less able to maintain their infrastructure based on the level of custodianship, as well, setting the stage for discussions of equity in the effectiveness of 311 systems.
At the same time, leaders at the City of Boston looked at our map of custodianship and saw a major flaw—the equitability of the 311 system depends on the assumption that everyone uses it. If that’s not true, then the system might just perpetuate existing disparities. They have since started to develop ways to use 311 reports in creative ways that allow them to prioritize their responses to neighborhoods not only based on who called first but also on the level of need. This has started with a program called StreetCaster that focuses on sidewalks, and we have had the privilege to assist them on the analytic side of this work.
DSW: In your book, you describe an effort to make 311 available to all of the towns and cities in Massachusetts. Two things interest me about this effort. First, it involved a collaboration with a for-profit company. Second, the likelihood that a given town or city adopted 311 was based on a different set of factors than the likelihood of an individual using 311 within the city of Boston. Please elaborate on both!
DTO: The project you describe was called Commonwealth Connect, a portmanteau of Commonwealth of Massachusetts (one of three!) and Citizens Connect, the original name of the City of Boston’s 311 app. The program was intended to scale Citizens Connect to any municipality in the Commonwealth that wanted a 311 system. In order to accomplish this, the Commonwealth partnered with SeeClickFix, a company with its own 311-like platform for reporting public issues. SeeClickFix’s business model is to set up annual contracts by which it organizes and communicates the reports received within a particular municipality to its public works department, and in this case, it became the manager for the database behind the Commonwealth Connect app. The municipal contracts were also subsidized by the state government. This partnership is a great illustration of how private corporations are essential to smart cities work. A single city or research-policy collaboration can generate any number of interesting innovations, but the impact is going to be local unless there is a model and incentive for scaling. There is a need to turn that innovation into a product, otherwise, there is no straightforward way to transfer the technology to other locales, and no resources to customize it once there.
Your second note was striking to us as well. We anticipated that the demographic correlations we saw in Boston would be the same across municipalities. Not so. It turned out that a town or city’s use of the app was entirely unrelated to demographics. Instead, the major deciding factors lay with city government itself. Did the city actively promote the platform? Did it use it effectively internally? These things were vital for signaling to the public that this service was a priority and that the reports it received would be responded to seriously.
These two points might seem loosely connected within a single study, but they both point to different sides of the same coin—how do we expand smart cities technologies in ways that they can be everywhere, not just in the major metropolises? One is the question of scaling and the economic model of technology transfer. I think private corporations play a vital role here. Policymakers are beholden to their own municipality. Academics are incentivized to do something innovative only once and then are expected to do the next innovative thing. Private corporations are needed to imagine how these innovations become products. (It’s also notable the role that the state government played here as well.) Second, how do we get all those other cities involved? We found through surveys that there are pitfalls at every stage, from adoption to implementation to follow-through. Why one city succeeds in working with technologies and another doesn’t is idiosyncratic, but it takes very close attention to local culture and bureaucracy in every case. Without this community-by-community approach, we will never be able to scale these innovations.
DSW: That brings us to my experience in Binghamton. Despite the goodwill and efforts of a lot of people, the moving parts just didn’t come together. The institutions were part of the problem more than part of the solution. For example, in a school for at-risk students that was demonstrably successful, we had to orient a new principal to our methods every year for three years before the school was terminated by a new superintendent who trusted her gut over our numbers. I call this “the paradox of programs that work but don’t survive or spread”. The rapid cultural transmission of best practices requires overcoming this problem. Any suggestions?
DTO: Institutions are challenging beasts! Sometimes they are beset by turnover, as you describe. How can one implement mindful change in the presence of constant random change? Other times they are too resilient, always wanting to revert to old habits and shy away from new ideas and ways of doing things. In other words, no change allowed! In some ways, DNA offers a valuable metaphor here. It is perfectly structured to be both a vehicle for the preservation of old ways with just enough vulnerability to mutation to enable evolution.
The question is, how do we find that same sweet spot in institutions? Hard to say. I think it clearly needs some support from leaders at the highest levels, like when Mayor Menino in Boston established the Mayor’s Office of New Urban Mechanics. But there also needs to be enough quorum among the rank-and-file to embrace these attitudes, or it will fall apart. Second, DNA has a natural, external set of selection pressures that guide a population of initial variations toward actual adaptations. What are the selection pressures we put in place for ourselves? We are returning to the theme from above, but there’s another wrinkle here. Let’s set aside the “efficiency is the answer” assumption and ask if innovation occurs without any clear objective. The strongest mechanism for selection at that point is the public discourse—political talking points, media coverage, etc. After the places where innovation fails entirely, the next saddest storyline is when it becomes nothing more than a vehicle for flashy press releases that prioritize the impression of doing something transformative over the transformation itself.
I don’t think this answers your question except to give a framework for when something works or doesn’t. I think what you are doing (what we did together) in Binghamton is the best you can do as an academic. Come to the table ready to collaborate. Make clear, accessible, articulate arguments as to why data and technology can be useful and why you make a natural partner for civic innovation. If the policymakers or practitioners don’t hear or can’t understand what you’re saying, then they won’t be able to partner effectively and there is no project to be had at that time. That’s not to say you should give up, though. Creativity and persistence might get you somewhere yet!
DSW: I’m delighted to include urban planning and the smart cities movement as part of this “Third Way” series of conversations. Keep up the great work!
DTO: Thanks, David, for having me. It’s been a great conversation. I appreciate you considering the topic of smart cities / urban informatics as a good illustration for the series.
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