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There are some urban planners and city administrators who
believe that if they had that kind of information they would be able to figure
out when and how to spend public money, allocate equipment and personnel and
restrict and support private efforts most efficiently. They also think they could use the same data
to anticipate certain tipping points so that traffic snarls could be avoided
before they happen, public works staff (including police and fire) could be
deployed where they would be most needed, and infrastructure repairs could be
managed with minimal disruption. With such information, the presumption is, water,
electricity and other city resources could be priced and deployed on a
real-time basis in the most cost-effective way possible. Empty housing and
commercial space could be repurposed almost immediately, and priced to match
the city’s urban development objectives. Taxes and fees could be collected
electronically while feedback from residents could be shared with officials on
a continuous basis. With the right kinds of electronic monitoring (including
sensors of all kinds), sufficient data collection, investment in high-level analytic
capabilities and appropriately trained staff, a city could become a “smart city.” All this digitized data could be displayed in
visual form -- across multiple platforms – so that everything would be easy to
read and understand.
Of course, knowing what problems a city faces, and even understanding
what’s causing them, is not the same thing as being able to respond
effectively. The availability of real time data, even with the most advanced application
of artificial intelligence, won’t make it clear who ought to do what, in what
order, in what way and for whose benefit. These are political choices: “is”
does not lead directly to “ought”
One school of thought, promoted by some economists and
engineers, assumes that the goal of city management should be to maximize
efficiency – eliminate waste and stretch every dollar as far as possible. They want to make sure public that tax
revenue, fees and intergovernmental transfers are allocated in the most cost-effective
fashion. If the goal is to collect trash, arrest criminals, clean-up air and
water pollution, or fight climate change, money shouldn’t be wasted in the
process.
A second school of thought, inspired by ecologists and
advocates of sustainable development, believes that every dollar of public
spending should be used to meet economic, environmental and social needs simultaneously, in ways that takes
account of long-term needs. Efficiency in the short-term isn’t as important to
these thinkers as long-term sustainability (which includes meeting the needs of
both current and future generations in as fair a way as possible). All the data
in the world won’t make it clear what ought to be done. In a democracy, such
choices need to be made through a messy process of reconciling conflicting
interests and values in which the population participates directly. Efficiency
isn’t always the highest priority goal.
My own university, MIT, is thinking about launching a new
undergraduate degree program in Big Data and Urban Science. This would bring
together faculty from urban planning, information science, electrical engineering,
city design and the applied social sciences to prepare undergraduates to build
and operate smart cities. Other universities, as described in a recent issue of
the Chronicle
of Higher Education, are a step ahead. NYU, Northeastern, Carnegie-Mellon, John
Hopkins, University of Illinois, University of Rotterdam and others have
already launched undergraduate and graduate degree programs that seek to merge
teaching about big data and urban studies.
As faculty in all of these programs try to decide what skills
and knowledge they want a new generation of urban scientists to master, there
are six questions I think they need to confront:
1.
What do
we mean by a city? (Are they talking
about activities that take place within a municipal boundary, or will they
focus on a larger set of regional, national and international forces that shape
urban life more generally?)
2.
Do they
think that privacy is a concern? (Do they assume that any and all information
that can be collected, should be collected? And should this information be
available to anybody who wants to use it?
Can people or organizations opt out and keep information about
themselves private?)
3. How will we fend off cyber-attacks on
critical urban infrastructure that have already begun? (If urban science
means greater centralization of information and data management, won’t that
increase vulnerability to attack?)
4.
Who do
they think should be in charge of designing and managing big data systems and
setting the standards used to make judgments about what’s working well and
what’s not? (Will this be a managerial task assigned to various government
agencies, or will elected officials be accountable for how all this information
is collected, analyzed and interpreted?
Will new laws be required to ensure that individual and organizational
rights are protected? Will this require federal, state or local legislation?
Where will enforcement responsibility sit?)
5. Will they be working from and toward an
idealized model of an efficient city, or will they work to preserve historical and
cultural diversity and variation? (I presume that students from all over
the world will want to participate in these programs? Won’t the differences in
culture, laws, and history require very different ways of applying the new
urban science in each country? Should we assume that this is basically a
technical education and teach a kind of “one-world view”? Or, would that be a
terrible mistake?)
6.
Are the
universities involved aiming to prepare public employees (whose job it is to
serve the public interest), or are they
training experts who will sell their services to the highest bidder?
The City As a Place
vs. the City as an Idea
Most efforts to model urban dynamics assume that a city can
be described as a series of “stocks” and “flows” within a set of
boundaries. While there are always
important “feedback loops,” many of which are likely to cause unexpected
consequences, most modeling (and forecasting) efforts begin by postulating a
set of boundaries. But, what if cities,
as many urbanists contend, are largely a
product of a great many extra-territorial (even global) forces? Capital or data
flows originating in other parts of the world may have as much of an effects
as economic and social forces
originating in the city. Moreover, if we say that the operation of many of the
sub-systems in a city reflect the ways that groups of people or institutions
think about things – their perceptions -- how do we include these in the models
we teach students to build? The city is
a physical space affected by global geological and ecological forces. It is
also an idea shaped by millions of individual perceptions. Will it be possible
to make sufficiently simplified models of the city to generate useful insights
and predictions?
What’s Confidential
and What’s Not?
Assume we can answer the first question, and we know which data
are required to make a city substantially smarter. Gathering some of these data will require
tapping into otherwise secure or private data sources. And, even if we argue that individual
identities will be scrubbed, should people and organizations be forced to give
up their privacy in the name of the greater good? In the United States, we are
currently watching a version of this question play out as a National Commission
on Elections and Voting demands that all 50 states to turn over voting data
that may reveal how specific individuals voted. When it comes to financial
records (including tax returns and credit card expenditures), even if these
data are crucial to modelling how a city is doing, should individuals have a
right to keep such personal data confidential? What ethical obligations will we impose ona
new generation of big data analysts or urban scientists?
Cyber-Security in the
Urban Realm
Urban infrastructure is already under attack from hackers
who seek to hold energy, medical, water, sewage and other systems hostage. Each
new layer of encryption added in response just ups the ante. These systems are
vulnerable because individuals are not as conscious of their cyber-security
responsibilities as they should be. Solutions
will require further technological innovation and investment, but that won’t be
sufficient. What will we teach a new
generation about cyber-security and how to maintain it? And, given the vulnerability of critical
urban infrastructure to global attack, should that affect what we guarantee
with regard to privacy and control over personal data?
Knowledge, Power and
Authority
Let’s say a city has put together a comprehensive data
gathering, analysis and visualization operation. Who will have access to the
raw data? Who will have the right to
publish analyses of the information that has been collected? Will the city be willing to share assessments
of things that residents think are going badly?
Will those who want to challenge current office holders be allowed access
and permitted to publish any analysis they like? Who will make decisions about how data should
and should not be interpreted? It’s my assumptions
that managers of smart cities will have “to do” lists that far exceed their
resources. Setting priorities (often in
real time) will require quick decisions, faster than the public can follow. If
all big data about cities were open sourced, would that allow more citizens to
be involved in helping to make decisions that are going to affect them? While real time referenda might be possible,
is that how cities should set priorities and make judgments?
Is There an Ideal
City?
Urban planners are very place-oriented; data scientists are
not. Urban planners want to preserve the
special historical and cultural features of each city. Data scientists, on the other hand, are looking for rules of thumb to describe the
most efficient ways of delivering goods and services in general. They might
inclined to disregard inefficiencies that are a by-product of local history,
culture or values. There’s no point
collecting data if there’s no intention to use it, but putting all these data
to use means measuring how things are going compared to some benchmarks. Should benchmarks be unique to each
community? Or, is the goal of merging
big data and urban science to create “ideal benchmarks” (based on studies of
many cities over time)? Should urban
science be practiced differently in different cities, let alone different
countries?
Public Sector vs.
Private Sector Careers
Urban planning education in North America has been provided
by major colleges and universities for more than 80 years. The majority of graduates of such programs
aspire to work in the public sector or in civil society (e.g. NGOs or public
interest organizations). This is true
regardless of where students originate.
Of course, some graduates find private sector jobs, either temporarily
or permanently in consulting firms or corporations. Whether they are headed to
the public or private sector, students studying urban planning tend to focus on
ways of meeting the needs of the poor and the disadvantaged; they start with a
theory of market failure and look for ways of using public-private
partnerships, regulation, public investment or political advocacy to meet the
needs of those for whom the market tends to fail. The engineers and scientists likely to be
drawn to these new urban science programs may not be so public sector
oriented. Will the new urban
scientists/big data managers who graduate from these programs be public sector/civil
society or private sector oriented?
I see the emergence of interdisciplinary urban science
programs around the world as a good sign. Merging the capabilities of scientists
and engineers with applied social scientists, designers and urbanists
interested in the life of urban residents would be a positive development. We need to provide all the help we can to
people in cities trying to make adjustments and reforms that reflect a
clear-headed awareness of the complex dynamics they face. I worry, though, that
some universities moving in this direction may pay too much attention to the
advice of economists and management gurus obsessed with numerical trends, who
are willing to focus on correlation because they don’t have the tools to
understand causal dynamics. I hope that the applied social scientists and urban
planners will succeed in ensuring that progressive values like concerns about
fairness and sustainability at the core of the training of a new generation of
urban scientists. I’m certainly glad that most of the people involved in these
new efforts appear to be committed to blending schools of thought that have
operated separately for too long.
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