IN MARCH 2017, just as I was completing the first full draft of this book, BARI hosted a conference titled “Data-Driven Research, Policy, & Practice: Lessons from Boston, for Boston.” This conference, like others hosted by BARI and similar centers around the world, convened members of the local civic data ecosystem, spanning the academic, public, private, and nonprofit sectors. The event was distinctive, however, in that we structured it like a society meeting: rather than impose our own vision for urban informatics, we invited members of the community to submit proposals for talks, thereby crowdsourcing the full span of urban data science and policy work occurring in greater Boston. We received 65 proposals, giving rise to a docket of 13 panels, five participatory workshops, and two keynote panels, featuring speakers from more than a dozen universities, public agencies, and nonprofit organizations.
Thanks in part to the decision to invite proposals for talks, the content of the conference was diverse. Some panels were the usual suspects of urban science and policy, such as “Public Safety & Crime,” “Neighborhood Development,” and “Strengthening Education through Data.” Other sessions reflected recent trends; these included “Driving in Boston,” “Human Services and Big Data,” and “Government and Accountability.” Still others were specific to the field of urban informatics itself, including “Open Data and Data Sharing,” “Models for Cross-Sector Collaboration,” and the keynote panel on “Data & Society: Boston in the National Context.” Overall, the event highlighted not only the potential of data and technology to help us better understand and serve the city but also a wide range of ways in which such work was already occurring in Boston. Whereas five years ago BARI was casting about for a proof of concept, here were dozens of examples of the advances that could be made at the intersection of research, policy, and digital technology.
Throughout the conference, there was an underlying tension surrounding “smart cities,” at least in terms of the vision presented by private technology corporations and popularized in the media. It led me to wonder what it means for “a city to be smart.” A smart person is someone who can harvest and synthesize information from various sources and generate new ideas and insights from these efforts. In turn, he or she can respond to challenges and opportunities by creatively and dynamically leveraging that information and the deeper understanding of the world that follows from it. In these regards, a city does not necessarily need sensor systems installed throughout the city to be smart, nor does it need predictive algorithms whose operations are largely hidden from the user. Instead, smartness was readily apparent in the more modest efforts featured by the conference’s panels: linking data across agencies to gain a comprehensive view of the scourge of opioid addiction in Massachusetts; analyzing field interrogation and observational data to determine whether there are racial disparities in Boston’s policing; and using multiple data sources to describe the process of gentrification, enabling local planners to respond to its various components. Each of these examples of “being smart” required attention to local context and collaboration across institutions and sectors, elements that have been lacking from the popular narrative around smart cities. In addition, they illustrate how much can be accomplished with relatively little cost, making it eminently accessible to cities of all sizes.
This book has chronicled an extended example of a city being smart. Boston’s 311 program is a technological policy innovation that has reconfigured how city services handle one of the oldest societal challenges: the maintenance of the commons. It has also generated a data resource that provides a novel window into the care of public spaces and infrastructure. The result has been a deeper understanding of when and why people engage in custodianship but also more general insights on the behavioral dynamics of the commons and how communities realize collective efficacy. At this intersection of policy innovation and research, we found an opportunity to examine the operation of coproduction programs and to evaluate the goals of civic tech. Going further, these studies acted as a vehicle for broader lessons about the practice of urban informatics, including the opportunities and challenges presented by naturally occurring data, the institutional structures underpinning the field, and even some of the issues that these institutions have yet to solve.
In closing, I will use this final chapter to conjecture about the future of the urban commons, following the same three levels described in the Introduction. The first level is the physical commons, which will continue to evolve as urban informatics marches forward. Some changes in service delivery and management will be incremental, but other innovations, such as sensors and automated vehicles, promise to reshape urban space. The discoveries and methodologies from this book will be valuable tools for guiding this work. The second level is the more abstract commons of the civic data ecosystem and the data-driven research, policy, and practice that it can generate. While it is one thing to describe a smart person, it is entirely another to determine what makes a city collectively smart. Here I discuss how we build the institutions that facilitate communication and collaboration across sectors that can support this goal. The third level is how these two commons reflect the future of urban informatics, its implementation, and the institutions that will advance it. It is through them that the field will continue to develop, and I will conclude with thoughts about the shape it will take in the coming years.
The 311 database provided a unique opportunity to explore how urbanites care for public spaces. It was so distinctive that it generated multiple contributions via a single line of inquiry. At the outset, we saw that territoriality was the primary motivation for custodianship, providing a framework for understanding when, where, and why people contribute to the maintenance of the commons (see Chapter 3 ). Emerging from differences in territoriality was a division of labor between typical custodians, who attended particularly to localized, residential spaces; exemplars, who extended their custodianship to “shared” spaces, such as main streets; and city employees, who reported issues more often in institutional and industrial spaces. Each of these three groups contributed in a distinctive way that was necessary for comprehensive maintenance of the com mons (see Chapter 4 ). Furthermore, given the program’s clear collaboration between government and the public, we were able to reconceptualize the public as not just a “citizen” with civic and political motivations but also a multifaceted partner who might contribute to coproduction programs in numerous ways (see Chapter 5 ). We were then able to evaluate how civic tech can best capitalize on these behavioral dynamics through a series of experiments (see Chapter 6 ).
As the structure and management of urban spaces continue to evolve with the rapid pace of technology, the discoveries from this book have inherent applications. I see this as taking two forms. First is the direct relevance to 311 systems and other programs that seek to leverage custodianship in pursuit of the public weal. We might also extend the lessons herein to the more general efforts to reconfigure the urban commons for a new age, including the introduction of sensor systems, autonomous vehicles, and public kiosks. Let us take each of these in turn.
As I described in Chapter 1 , the first 311 systems had the mundane goal of triaging nonemergency issues away from overburdened 911 hotlines. It only gradually became apparent that they also created a natural channel for people’s custodianship, empowering them to participate more actively and directly in the upkeep of their communities. Though this success has made 311 the policy innovation du jour that has been implemented in hundreds of municipalities across the United States, administrators are still working to determine how it best engages constituents, in turn making the maintenance of the urban commons even more efficient. The findings here are directly applicable to the continued development of these best practices.
Chapter 6 has already explored some of the practical implications of custodianship’s basis in territoriality. Advertisements for the system are most effective if they reference the local neighborhood. We also saw the limitations that these motivations can create. Efforts to construct a “bridge to citizenship” might encourage individuals to report more often but still within a relatively narrow geographic range. I have not, however, fully explored the sorts of interventions that might be informed by a division of labor. For example, a government may describe the strength of custodianship across neighborhoods in terms of two dimensions— typical custodians and exemplars—and tailor its services accordingly. If a neighborhood is low in exemplars but not in typical custodians, issues in nonresidential spaces might be more likely to go unreported. A city could then develop a marketing campaign that appeals particularly to exemplars or to the care of these “shared” spaces. They could also deploy public works employees more heavily in such spaces to compensate for the community’s weaknesses. I also alluded in Chapter 7 to the ways that municipalities might use the division of labor to assess whether their physical organization makes them more or less likely to benefit from a 311 system. The overarching point here is that the public experiments in this book are only a first step and that there is much more that can be accomplished in these regards.
Moving beyond 311, we might apply our fuller understanding of custodianship to other recent innovations in the maintenance of the urban commons. For example, many cities and towns across the country have encouraged local residents and organizations to sponsor or take responsibility for some piece of public infrastructure—from a stretch of highway to the flowers in a public planter. Building on the same logic, the city of Boston and Code for America developed an internet platform where residents can adopt a fire hydrant, thereby committing to shoveling it out during a snowstorm. A similar program has subsequently been implemented by other cities that see lots of snow during the winter, such as Rochester, New York. Similar to 311, the smartphone app Waze invites people to report road-quality issues and traffic jams; in this case, instead of notifying the government, they are proactively sharing these alerts with other drivers who might benefit from them. Other projects have been less explicitly about public spaces but have a similar spirit in their attention to localized social contexts. Nextdoor supports private social networks associated with a given neighborhood. Some neighborhood associations have mimicked this by constructing their own Facebook pages. Neighbor.ly is a clearinghouse for local improvement bonds, thereby encouraging and facilitating financial investments in local communities.
Many of these innovations are likely to capitalize on the same territorial motivations that underpin 311, and their own operations will reflect this. They also almost certainly rely on a division of labor, and the managers of each might consider the typology of actors—some very active, others more episodic in their contributions—that support collective efficacy in the program’s main goals. At the same time, Chapter 5 ’s lessons about the public as a multifaceted partner will be essential. Individuals can now contribute to the maintenance and development of their communities through many different programs, and each may call on its own combination of motivations. It would thus be important to conduct case-by-case studies across them to better elucidate how each best engages its users. The true value, however, lies in the combination of these studies, which will offer a panoramic view of the various motivations that govern people’s contributions to their local communities. This will not only support refinements to existing apps and programs but will also highlight new areas of opportunity.
There has been a trend in recent years toward ambitious, or even radical, reconfigurations of urban spaces to better support particular types of activities. We might divide these innovations into three groups. First, there has been an interest in reorganizing streets, sidewalks, and adjacent spaces to better integrate walking and biking with automobile usage. Second, as certain manifestations of telecommunication become obsolete, such as the public pay phone or the firebox, some are imagining what the modern version of these public amenities might be. Third, one of the most prominent stories of “smart cities” has been the instrumentation of public spaces and infrastructure—that is, the installation of sensors that track local conditions on various dimensions, including pollution, weather, and volume of pedestrian and vehicular traffic. These three areas of activity vary in how clearly they are a reflection of “urban informatics”; some are ultratechnological, some are data driven, and some are both, but a handful are simply thoughtful ways to reconceptualize the urban commons.
In 1997, the Swedish parliament introduced Vision Zero, a policy initiative with a goal of eliminating fatalities to bikers and pedestrians on city streets. The philosophy behind it was that user errors and accidents happen, so roads need to be constructed in ways that minimize the possibility that automobiles will collide with other forms of transportation. In parallel, concerns about climate change have led many cities to encourage a mix of transportation modes that lowers the volume of pollutants emitted. These two trends have supported a series of innovations that prioritize walking and biking on city streets, including separated bike lanes, the closing of certain districts to vehicle traffic (e.g., Times Square in New York City), or the rapid proliferation of bike-sharing systems.
Technology and data have not been the primary drivers of these innovations to urban roadways, but they have proved useful for implementation and evaluation. New York City, for example, has used data to determine the effects of their various initiatives on collisions between cars and bicycles. 1 There have also been numerous data-based reports on bike-sharing systems and their impacts. Interestingly, the most common use is for commuting, though in some cities this means that they act more as an adjunct to public transportation than as a replacement for automobiles. 2 In some cases, cutting-edge technology has proved useful in generating new and useful data. In 2016, Boston’s Safest Driver competition encouraged volunteers to download an app that tracked their driving patterns, including average speed, sharp braking, and phone use while behind the wheel. Whereas similar apps had been used by insurance companies to evaluate safe driving in the past, the goal here was to better understand how driving patterns vary across the city.
Alongside projects centered on safer mixed-use roads, companies such as Google and Uber are promising the future of automobile transportation in the form of autonomous vehicles and are piloting the technology in multiple cities. There have been numerous arguments that this will be even safer because the cars will be able to communicate with each other and operate on complementary algorithms—put in simple terms, imagine a world in which no vehicle ever makes an unexpected move and can directly communicate its intentions to the cars around it with complete fidelity. While the focus has been on the technological advances that have made such dreams a reality, there remains an underappreciated question as to how they will transform daily life. For instance, a fleet of public or shared vehicles (à la Zipcar) may obviate the need for personal vehicles in cities, picking up and dropping off passengers on demand. Such a development would radically alter the urban commons in a way that no other technology can match, raising a variety of questions for urban planning: How do we construct streets to accommodate both the autonomous nature of vehicles and the human-directed movement of bikes and pedestrians? Will the most effective arrangements be the same as those we have constructed to manage the interaction between human-driven cars and other modes of transportation? Drop-offs and pickups are quite different from the typical park-and-exit process. Do we need to redesign sidewalks and building fronts to better manage this dynamic? Relatedly, the city will need far fewer parking spaces, and they will likely be concentrated rather than distributed along the sides of all streets. What do we do with this reclaimed space? Do we install new amenities, make larger bike lanes, put in strips of green space, or something completely different? Such changes will not occur overnight or without conscious effort. Again, data can help us evaluate candidate innovations, allowing us to arrive at optimal solutions iteratively and efficiently.
The two other areas of innovation in public spaces—the replacement of obsolete telecommunications infrastructure and the introduction of sensor systems—are emblematic of smart cities. One of the strong attractions of these projects, but also their most prominent weakness, is that they often appear to assume the answer is technology, regardless of the question. In the case of replacing outdated public telecommunications infrastructure, the consensus appears to be kiosks—that is, public tablets that provide wireless internet, access to government services, and related amenities—though there is some debate as to the value they offer. Sensors are even more exaggerated in their combination of cutting-edge technology and underspecified contribution. While technology companies have argued that detailed knowledge of conditions across the city will be able to lower the city services budget in various ways, it is not entirely apparent which specific applications will produce those dividends.
Given the open questions surrounding kiosks and sensor systems, it is ironic that they often overshadow the extensive impact that urban informatics is already having through more modest data analyses and technological innovations. It is not just that this latter set of projects is creating value that deserves attention but also that they are generating lessons about the effective use of modern digital data and civic technology. These lessons, in many cases, provide a road map for turning smart cities innovations into effective tools for helping cities be smart. Starting with the lessons from this book, let us take a closer look at the problem of measurement. Sensor data are deceptively hard to interpret. While they track specific conditions at a place, we have little understanding of the real-world events that are creating these conditions. For example, students in one of my classes used data from a sensor project in Boston called the Local Sense Lab as the basis for their class projects in fall 2016. Separate projects assumed that the noise sensor was a proxy for vehicular traffic, pedestrian traffic, public performances in the area, and subway cars pulling in and out of the nearby station. The problem is that if a single measure is a proxy for multiple phenomena, it is useless for differentiating between them.
The problem of interpreting sensor data will require their triangulation with other data sources. Administrative data might help us to specify exactly when the trains came and went using transit data, or the days and times when there was a public performance nearby from permit and licensing data. We could use social media check-ins to estimate actual pedestrian activity. We might even conduct in-person audits, as in Chapter 2 ’s streetlight and sidewalk assessments, to relate signatures of one or more sensors to objective observations. In some cases, we may discover that they are picking up information we never anticipated. In the case of the Local Sense Lab, one of my students actually visited one of the noise sensors in question, only to discover that it had been installed right next to the output vent for a local building’s HVAC system. Thus, at least some of the temporal “signal” detected by the sensor was merely the building’s patterns of heating and cooling. All told, sensors in isolation can tell us relatively basic things about the chemical and ambient composition of the environment but are also vulnerable to microspatial idiosyncrasies. When combined with other data sources, however, they have the potential to be effective proxies for a host of characteristics and events of interest.
The problem of measurement is largely a technical one, meaning it is tractable with some hard work and ingenuity. The potentially greater challenge facing these tools is a philosophical one. What is the definition of “value”? What can a sensor system provide people that they want and need? This is a problem of both definition and social process. If we want to make a comparison, the policy mandates of Vision Zero have embedded in them a clear set of objectives (i.e., no pedestrian or biker fatalities) that acted as the basis of evaluation. The same is true for the Promise Neighborhoods grant program’s emphasis on learning environment and student outcomes. But such objectives have not been defined for sensor systems. In some sense, this arises from one of their greatest advantages. They are the epitome of what Tim O’Reilly refers to as a “platform,” meaning they can support the vast proliferation of new innovations. 3 That said, they are then by definition unable to preordain the value that these subsequent uses pursue—or what one might call “value-free.”
I would advocate for an inclusive approach to defining the public value that might be gained from sensors and kiosks. More than just a negotiation between technology companies and city hall, these conversations need to incorporate the voices of community organizations and their members. This involves more time and cost but is far more likely to achieve the stated goal of developing products that serve the needs and interests of the public. What do people want sensor systems to be able to do? How would they like to interact with the data and have it communicated to them? Are there places they want to be heavily instrumented and others where they would see this as an invasion of privacy? In the case of kiosks, they reflect the natural evolution of the public pay phone, but what form should they take? There are debates about size, with some claiming the Google Sidewalk Labs’ LinkNYC “Times-Square-ifies” neighborhoods with its large advertisements. Others might wonder whether, with the increasing ownership of smartphones, we really need to replace pay phones with a new public telecommunications device or instead should reclaim that sidewalk space for pedestrian traffic. Is the kiosk itself worth the investment of capital? Cities should ask these questions of their residents, as their answers will provide guidance for using what are otherwise value-free technologies.
Finally, we need to reconsider whether technology is necessarily the answer to every question. There may be cases where a low-tech solution is as good as, or even better than, the high-tech one. A colleague of mine from the city of Boston illustrated this nicely at a conference of the MetroLab Network a few years ago during a discussion on sensors. He said (and I paraphrase), “These sensors are nice, but I already have this sensor [holds up his iPhone], and it travels around in the pocket of nearly every Bostonian. In a lot of cases this tells me everything I need to know.” He went on to tell the story of Street Bump, an app that uses smartphone accelerometers to identify and report potholes while people are driving. When they tested the app in city-owned vehicles, they found that it could identify inconsistencies in pavement, but when they compared the data with 311 records, the app told them nearly nothing that residents had not already reported. In this case, the high-tech solution might make for a compelling story, but it was no more effective than empowering the residents of the city through 311. This is probably true in a lot of cases, as humans are technically sensors for everything, not just for pollutants, noise, or bumps in the road. There are, of course, times when a sensor might be valuable. We have seen in this book streetlight outages that languished as pedestrians failed to report them. Research on ShotSpotter, which detects gunshots, has also found that many shootings, especially those occurring at night, would go unreported without the system. 4 Residents may recognize dirty air or water but have no ability to identify particular pollutants. City services, then, will need to identify how humans and sensors complement each other in tracking the conditions of the city.
How does a city become smart? Unlike a person, a city is not a single entity. It is instead an assemblage of public agencies, universities, corporations, and community organizations, each with their own distinct operations and incentives. With that in mind, we might still borrow from cognitive psychology the concept of executive functioning , which is the ability of an individual to coordinate multiple processes in the pursuit of stated goals and outcomes. For a city, the same capacity rests in the organization of the civic data ecosystem. This is not to say there needs to be a central “executive” directing all research and policy efforts but rather that for a city to be smart there must be institutions in place that enable communication, knowledge sharing, collaboration, and even a conscious sense of community among those using data and technology to better understand and serve the city.
In order to describe the roles of different institutions in supporting urban informatics, we first need to define the field and how we think it will operate moving forward, something I first addressed in Chapter 1 . At the conference in 2017, we noted something interesting about attendance. A nontrivial number of people joined for only one or two sessions, primarily focusing on those that were strongly associated with their own work. Criminologists and members of the Boston Police Department attended the session on “Public Safety & Crime,” community advocates attended the session on “Neighborhood Planning,” and so on. From this perspective, the constituency of BARI looked less like the members of a unitary field and more like a pastiche of disciplines, all of which had been energized by the opportunity to ask questions about cities using data of unprecedented scope and detail. The same looseness is visible in the national and international landscape of the field. It does not yet have a canon of foundational theoretical precepts. Nor does it have stand-alone departments, instead operating largely through cross-disciplinary centers that act as conveners. It does not even have its own journal. It is early yet, so it is probably unfair to treat these weaknesses as symptoms of anything more than a nascent field, but there may be a lesson in them nonetheless.
If the institutions that support urban informatics are to be successful, they will need to focus on those themes that are sufficiently unifying to create a field with collective interest. I can think of two. The first is the original basis of the field: modern digital data and technology. In order to capitalize on these new resources, the field has constructed a toolbox of analytic techniques that are applicable across disciplines. On its own, however, this theme would cast urban informatics not so much as its own field but rather as a methodological adjunct for the existing disciplines of sociology, criminology, public health, planning and design, policy, engineering, and computer science. The second unifying theme that I see is a panoramic view of the city itself. It is both natural and convenient to divide society into its components, to study and manage education, public health, crime, transportation, and the other domains independently. But all of these domains intersect. High school students take public transit. Crime in a neighborhood can create stress that leads to mental health issues. Gentrification alters the local context, and thereby the environment experienced by residents, for good and for bad. It is this holistic approach to the city that I believe excites the more ardent practitioners of urban informatics, those individuals who attended our conference from beginning to end, who wanted to understand urban communities in their entirety. If the city is a stage on which all aspects of behavior and society might be observed, then the aspiration of the field, if it is to realize a separate intellectual identity, is to watch the entire performance, not just one set of characters or passage of dialogue.
These two themes—one a methodological toolbox, the other a conceptual vision—create the mandate for the institutions that will support urban informatics moving forward. In the remainder of this section, I describe how each of the sectors—academia, public, private, and nonprofit—contribute to the civic data ecosystem, thereby creating a division of labor in the urban (data) commons. I conclude by discussing the forms that institutions with the goal of facilitating coordination and collaboration might take.
I start with academia in part because I am an academic and it is easiest to start with oneself. I also start here because, in the end, academics are responsible for many of the cutting-edge breakthroughs that will fuel the field. The opportunity for urban scientists is great, as I have discussed at length. As the world has become increasingly digital, instrumentation and data have followed suit, greatly expanding the breadth and detail of available knowledge. This forces us to develop novel methodologies necessary for analyzing the data, which then instigate new theories that have the potential to transform our basic understanding of cities. These are tangible contributions that will diffuse into the other sectors. The methodologies will eventually become widely available. The theories offer a conceptual foundation for new ideas and innovations. In this way, the work of academia can directly translate into practical advances for the city writ large. This is especially true if researchers choose to partner with policymakers to explore mutually beneficial extensions and applications of their work.
As important as academia’s efforts to advance knowledge are its educational programs. The students in these programs are the ones who will carry the new methodologies and theories of urban informatics into the mainstream. Master’s programs in urban informatics and related topics have proliferated in recent years, and classical programs in planning or urban studies have incorporated data courses into their curriculum. The students graduating from these programs are the next generation of policymakers, practitioners, and community organizers, and they will be equipped with the new skills and ideas generated by the field. Even those who do not aspire to be data scientists per se can still bring to their future jobs an understanding of what these tools are and how they could be useful.
As I mentioned at the opening of this section, there are currently no departments of urban informatics. The discipline instead lives in centers. Because there is no need for urban informatics to subsume the panoply of existing disciplines that it touches, this strikes me as the right model at this time. If provided with sufficient funding from both internal and external sources, centers have the flexibility and independence to operate as conveners, working across the silos created by disciplines, institutions, and even sectors. Through well-designed programming, a center can stimulate the needed conversations, foster cutting-edge work, and nudge research in new and experimental directions. It can also be a main conduit for communications between the research community and the other sectors, thereby connecting potential collaborators. The one tricky part here is making sure that the centers, which typically are research driven, are sufficiently aligned with the relevant educational programs, which for administrative reasons are typically run by departments.
One might argue that the emergence of urban informatics has had its greatest impact on the public sector. Academia and private corporations have been very much involved, but they were already convinced of the potential of digital data and technology. This same embrace of the future has only recently become popular in city halls around the country, and the transition has been rather rapid. Since New York City introduced the first team dedicated to in-house data-driven projects, other cities have adopted the same model. Similarly, a number of cities have replicated the role of Boston’s Mayor’s Office of New Urban Mechanics as an “R&D team for the city.” We are now beginning to see a second stage in the development of these sorts of teams. As they have provided early evidence of their value, the ensuing demand within city hall is outstripping their capacity. Consequently, a metastasis is on the horizon, with individual agencies and departments implementing their own internal analytics teams. This is already starting to happen in New York City and Boston.
The success and growth of in-house analytic and innovation teams means that cities will conduct more and more of their own analyses and studies. Though it might seem counterintuitive, I think this actually strengthens the future of city-university partnerships. Admittedly, one of the weaknesses of academics is that we are incentivized to do a project only if it contributes to the scholarly literature. If a possible project has applied value to a public agency but lacks a fundamental advance, it is of limited interest to a scientist. This has not been too great a deterrent thus far because the novelty of the data and technology have generated both public value and intellectual contributions. As these initial advances in methodology and knowledge become established, however, their replications and applications will be increasingly derivative, having less impact on scholarship. This does not need to be a dead end for city-university partnerships, though, as two simultaneous shifts are occurring. First, data analytics teams have sufficient talent to enable cities to pursue these applied studies. Second, the initial advances have opened up a new set of cutting-edge questions to which research-policy collaborations can turn their attention. This ability to focus on the new frontier will maintain the field’s forward movement while also keeping academics heavily engaged. One of my colleagues describes research-policy collaborations as a beneficial cycle of discovery and application. This is the same dynamic but at an institutional level: discovery across the first stratum of data and urban science has laid the groundwork for new teams within city hall that are trained in those advances, to be followed by another round of scientific advancement, in which the city teams will be trained, and so on.
Public agencies also face new challenges in data infrastructure. As urban informatics moves into more complex problems and more ambitious efforts to forecast events and conditions, there is a need for integration across many different data streams—the records of multiple agencies, the readings of sensors, and the information shared by private corporations might all be utilized in any given project. It is up to public agencies to identify the integrations that would be necessary to inform daily operations and long-term planning. Without this, the elements of the pulse of the city will sit fragmented and limited in their potential, not unlike a hospital where readings of heart rate, blood pressure, body temperature, and x-rays are kept separate and never examined in concert. This enhanced data infrastructure would be a boon for the public sector but also for the broader civic data ecosystem and its productivity. Though the leadership of public agencies is likely to be critical to this process, they will certainly need help. First, academics and private corporations can contribute to the development of the technical infrastructure for integrating disparate data streams. Second, such integrations raise serious questions about the appropriate use of proprietary data provided by private companies, as well as personally identifiable data describing individual constituents. The appropriate models for data storage and sharing in such cases have not yet been determined and will be an important part of this conversation.
I have spoken little about private corporations in this book and at times have been a bit hard on them. I do not intend this book as a critique of a for-profit model of urban innovation. Rather, city-university partnerships should have more connection with private corporations because there will eventually be a need to develop business models that make the products of urban informatics sustainable. For example, we saw in Chapter 7 how SeeClickFix, a private vendor, was a key player in scaling BOS:311 from Boston to other municipalities statewide. Private corporations have the incentive to perfect and mass-produce a usable product for public consumption, whereas academics do not. This makes private corporations the third leg of a stool of research, development, and deployment, along with academia and government.
Most critiques of the private sector are rooted in a broader concern that technology companies often seem to be trying to go it alone. Many large-scale smart cities implementations are perceived as having little collaboration with academics and limited ability to be customized for the needs of the client city. For those companies that see such openness as a potential threat to proprietary technology, a possible solution is to think of their product as a “platform,” as extolled by Tim O’Reilly. 5 He argues that the most influential advances, such as Microsoft Windows or the iPhone, are special because they enable a vast population of bright people to create new tools on top of it, thereby growing its value exponentially. I suggested earlier that this is probably how we should treat citywide sensor systems—as the infrastructure that undergirds widespread innovation on the city’s behalf. Corporations that embrace this route will want to work closely with both the public sector and academia in order to ensure that the promised value is realized.
I have spent even less time speaking about private foundations in this book, but they play an important role as well, in part as a funder but also as an advocate. It almost goes without saying, but foundations do more than make grants. They must decide what issues matter to them, how they want their resources to contribute to society, how these ideals translate into grant programs, and then which projects to support. Given their contribution of capital for service providers and researchers, they are as important as anyone to shaping a city’s organizational landscape. This makes them a crucial part of the civic data ecosystem, one that has the opportunity to play a visionary role. They are unique in their ability to articulate the present and future needs of a city without being beholden to political expediencies, expectations of academic originality, or a profit margin. By combining this intellectual independence with their ability to make grants, they are not only positioned to call for research on certain topics but can directly drive work in that direction.
Because I spent the entirety of Chapter 8 on community organizations and related nonprofits, I have little additional to say about them here. To recap, they can play an essential role as infomediaries that translate data resources into public value. This set of institutions is the furthest behind in terms of their current facility with data, but that can certainly change as partnerships increase, educational programs grow, and the talents needed become more widespread. In many ways, those of us who are skilled in data science need to package and discuss our projects in forms and language that are accessible to this population, because they will be able to do what we cannot: align the content and its implications directly with the needs and interests of local communities. The integration of community organizations more fully into the civic data ecosystem will be largely experimental in the coming years, from training in data portals, to conversations about the implementation of sensor systems, to specific research projects. The necessary next steps will be to build models of interaction that empower community organizations to both speak for the public and bring the implications back to their constituencies.
If each of the sectors—academic, public, private, and nonprofit—succeeds in playing the roles that I have articulated, that still leaves open the question of institutions that create the connections between them. That is to say, someone must bring together these various talents and capacities to form a functional civic data ecosystem, an urban commons that generates a collective “smartness” that broadly benefits the city. Numerous models for solving this problem of coordination have arisen. In many, a partnership between the city’s department of innovation and technology (or similar agency) and a single academic center forms the foundation for all local research-policy collaboration. In places with a larger, more complex ecosystem, one or more institutions work together to convene the many entities that might contribute to the conversation. In Boston, BARI and the Mayor’s Office of New Urban Mechanics have accepted this responsibility, a job we share with a variety of other partners. Distinctively, the Los Angeles Housing Library has assumed this role with the public libraries. Other cities have seen the creation of independent nonprofits, such as Chicago’s UI Labs and Envision Charlotte, that help to broker cross-sector, data- and technology-driven collaborations. Often referred to as “test beds,” they are more accurately described as social infrastructure for identifying local needs, connecting partners who might attend to them, and mediating access to the necessary resources. At this point in time, there is no clear “best” model, though we may learn more in the coming years. What stands out as important is that there be intentional engagement from at least one institution in each sector, with the public and academic sectors being the most critical, followed closely by private corporations and community organizations.
There are three functions that these leading institutions must fulfill. The first and most apparent is to convene. How this is best achieved—through events and conferences, creating “affiliates” of various sorts, or seed grant programs—has been explored extensively, and cities across the country will continue to do so. Of particular interest is how to develop an agenda in a collaborative way that imparts coherence to the work while not stifling the independence of the many members. The second essential function is to facilitate education. As scientists advance what we can do with data, these skills need to be transferred to the other sectors. Various models of this are being tested, from traditional partnerships around master’s programs and certificates to specialized fellowships for policymakers. On the other hand, academics often could use a greater understanding of the practical challenges that members of the other sectors face on a day-to-day basis. The potential for multiway learning of this sort between the sectors is broad and is ripe for innovation.
The last function for leading institutions of this sort, and one for which I do not yet have a good answer, is governance. At the moment, urban informatics runs on the enthusiasm of its early adopters and their zeal for collaboration. In a sense, we are both the constructors and custodians of this particular urban commons. But it is important to recall the lessons of Elinor Ostrom, who wrote more than one book describing the need for norms that set the ground rules for participating in a community and for institutions that ensure that those norms are followed. 6 How do we require that people share their findings with the original data producer or with the community as a whole? How do we confirm that a given analysis and its implications are sound before a policymaker makes a decision based on them? What are the expectations for reporting in a publicly accessible way? These are the questions that sit before us now, but their number and urgency will only increase as the field continues to grow. This book has chronicled a series of projects on custodianship, but they are just the tip of the iceberg—one line of inquiry inspired by one technological innovation and one data set among many. As these opportunities proliferate, we will need to think seriously about how we maintain this particular urban common.