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Designing for Complexity: The Challenge to Spatial Design from Sustainable Human Development in Cities



Never in history have we built so much, in so many different places. Population growth, urbanization, and technological change are all pushing us to conceive and design spaces in human societies in new and better ways, aiming toward more environmentally sustainable and socially equitable human settlements. Much of what is happening in the fastest-growing cities in the world today, however, is informal; while vital, this development fails to incorporate advanced knowledge or seize opportunities for leapfrogging solutions. I discuss these challenges from the perspective of what we know scientifically about cities and the formal processes of design in order to illustrate how spatial design enriched by new data and methods of analysis can fulfill its long-avowed aspirations to create open-ended complex systems that are an integral part of networked processes of sustainable human development.
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Technology|Architecture + Design
ISSN: 2475-1448 (Print) 2475-143X (Online) Journal homepage:
Designing for Complexity: The Challenge to Spatial
Design from Sustainable Human Development in
Luís M. A. Bettencourt
To cite this article: Luís M. A. Bettencourt (2019) Designing for Complexity: The Challenge to
Spatial Design from Sustainable Human Development in Cities, Technology|Architecture + Design,
3:1, 24-32, DOI: 10.1080/24751448.2019.1571793
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Published online: 26 Mar 2019.
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Never in history have we built so much in so many different
This is mainly the consequence of two global forces:
growing human populations and increasing urbanization.
Demographic growth is slowing down and is expected to per-
manently abate by midcentury. Africa and south Asia will be the
last world regions to experience a full demographic transition,
well into the twenty-first century. Urbanization is expected to
be with us forever, even if it changes in form and density.
These trends make cities the principal focal points for the
problems and solutions related to change in every human soci-
A number of these challenges are directly related to the
way we design the built environment.
This paper reviews some
of these challenges and opportunities in a new light, empha-
sizing that, thanks to new technologies and methods of analy-
sis, the design of the built environment is coming into a clearer
view as a part of environmentally and socially complex systems,
which are long-lived, adaptable, and open-ended.
Amid widespread interest in linking spatial design to more
positive social and environmental outcomes, there remains a
wide chasm.
This is the result of the gap between hoping that
a building or a street plan performs some desirable function
and actually understanding the articulation of how they do so
and the nature and magnitude of at tainable effects.
At first, this may look like a call for greater precision, but it
is more than that. Architecture and spatial design require mod-
els of human beings and their social behavior as well as models
of natural ecosystems. Spatial designers are not alone in this:
Never in history have we built so much, in
so many different places. Population growth,
urbanization, and technological change
are all pushing us to conceive and design
spaces in human societies in new and better
ways, aiming toward more environmentally
sustainable and socially equitable human
settlements. Much of what is happening
in the fastest-growing cities in the world
today, however, is informal; while vital, this
development fails to incorporate advanced
knowledge or seize opportunities for leap-
frogging solutions. I discuss these challenges
from the perspective of what we know scien-
tifically about cities and the formal processes
of design in order to illustrate how spatial
design enriched by new data and methods of
analysis can fulfill its long-avowed aspirations
to create open-ended complex systems that
are an integral part of networked processes
of sustainable human development.
Designing for
Complexity: The
Challenge to
Spatial Design from
Sustainable Human
Development in Cities
Luís M. A . Bettencourt
Mansueto Inst itute for Urban Innovati on, University of Chic ago
Economists, sociologists, and health practitioners all rely on
models of humans, especially of human cognition and agency
in the complex, dynamic environments of cities.
The problem
is that such models have remained inconsistent and woe-
fully inadequate.
One antidote to this morass is to look at history. Many
architects and urbanists, from Patrick Geddes to Christopher
Alexander, from Kevin Lynch to Jane Jacobs, realized the vitality
of spatial “design without a designer” and often advocated for
an evolutionary approach, which Geddes memorably dubbed
“conservative surgery” in the context of urban planning for
slums in India almost a century ago.
Historical approaches are
informative, but typically do not provide a constructive strat-
egy, especially for the large, rapidly changing cities of our time.
Another approach is to create a more predictive and quanti-
tative “science of cities.” Urban science is an emerging paradigm
for building generalizable knowledge about the processes that
create and sustain cities, working from a basis of new and much
more pervasive empirical evidence (data) and comparative
rw Figure 1 . Mathematical graph anal ysis of street and building
network s can be automated to reveal un derserviced buildings (re d in right
upper pan el [Harare], blu e to orange in right lower panel [Cap e Town]) and
automaticall y propose new street plan s (right lower panel) with minimal
disturbance a nd cost. These plan s can be edited by local st akeholders,
including residen t communities and local go vernments, and provi de a basis
for land records a nd formalization of tenure.
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Designing for Complexity
methods. This sort of approach has been very fruitful in the
last few years at identifying several key properties of cities as
networked complex systems and generating novel methods of
analysis that allow us to better track, assess, and understand
the nature of urban processes and the effects of urban plan-
ning and policy. The critical difference between the current
approach and former applications of scientific knowledge in
urban planning is a much more systematic embrace of evidence
and data that allows researchers and practitioners to test their
ideas, build better theory from validations and falsifications of
hypotheses, and correct the course of practical approaches
when necessar y.
What is at stake then, is much more than the technical develop -
ment of spatial design. What is necessary is the advent of a much
more systematic approach t hat brings together the design of built
environments with the best scientific knowledge of processes of
change in complex natural and social systems.7
Why spatial planning for complex sys tems is hard, why it is vital,
and how it can be done in practice is the subjec t of this paper.
Challenges of Spatial De sign
It is much easier to design a spoon or a chair than a building.
It is much more difficult to design a city. Why? The nature of
spatial design is strongly scale dependent.
On sufficiently
small scales, the objectives of design are well defined: function
can dictate form, together with some aesthetic variation. The
object can be made, tried out, and improved. A fork or a spoon,
a cell phone, or a pen all have these characteristics.
Designing at the scale of buildings is more challenging . Buildings
existed well before formal architecture.
Most buildings in devel-
oping cities today are built by people without formal design edu-
The kind of buildings and urban fabric that results from
this informal process—if it can play out for long enough—has been
lauded as pleasant, picturesque, and more attractive than more
intentionally designed environments.11
This, then, is the first lesson of spatial design: trust cities. Just
as in nature we can have exquisite design without a designer
through the processes of evolution by natural selection, so too
with the human built environment. T hese processes are different,
but successful designs can be achieved in both cases through trial
and incremental improvements. Design is a natural process; spa-
tial design is a necessity and a universal human instinct.
What, then, is the role of formal design? At the scale of
buildings, the answer presents itself clearly. While many peo-
ple have experience building on small scales, almost all build-
ings that have a specialized function, with specifications that
involve advanced knowledge or a larger scope, require for-
mal design.
Furthermore, to ensure public health, safety,
and welfare, societies have developed minimum standards of
education and responsibility for professional designers of the
built environment.
Formal designers, in this sense, contribute specialized knowl-
edge and tools not available to common builders. These allow
for some virtual experimentation around physical design, build-
ing operations (e.g., energy efficiency, sound), its resilience
(to earthquakes or wind), and some of its intended uses by
people and organizations. The result is the achievement of a
structure that would not have been possible at all by more
informal means.
In this sense, the tools and knowledge of the architect are
instruments that expand our imagination. This knowledge
allows us to transcend “timeless” but limited ways of building.
This is what philosopher Daniel Dennett calls “cranes for the
Cranes for the imaginations are tools for inten-
tional formal design that do not replace the logic of evolution-
ary processes, but that do expand their range, speed, and scope.
Architecture and planning tools that work with the natural
processes of cities are just emerging and making it possible to
design along vernacular forces to create an evolution of informal
urban fabrics while reinforcing local needs and character and
promoting social capital and equity.15
A perennial problem of fast-growing cities is their informal
use of land, often in the form of slums. Informal neighborhoods
typically lack addresses, urban services (especially sanitation),
and access to emergency services.
They are often associated
with poverty, health challenges, and environmental degradation .
Formalizing informal settlements, through service upgrades and
land tenure, is often desirable but considered nearly impossible
technically, politically, and financially, especially because these
places are so hard to access and organize.
Traditional urban
planning approaches to these situations involve relocation,
demolition, and de novo redevelopment. Such strategies tend
to perpetuate poverty and exclusion, while creating cheap and
banal urban design.
New methods from urban science allow the accelerated
evolution of these neighborhoods to follow natural urban
They are based in part on the mathematical
analysis of detailed maps, including the development of algo-
rithms to optimize building access, delivery of services, for-
malization of land, and taxation, with minimal disturbance and
cost (Figure 1).
Planning through the development of detailed maps at the
neighborhood level is also an effective way to capture local,
person-centric knowledge, providing a clear vehicle for better
local politics via the coordination of priorities and action from
communities, local governments, and other stakeholders.
The convergence of a networked science of cities, quantita-
tive methods of spatial analysis, and information technology
tools is key to allow users to participate.
The gradual incorporation of informal neighborhoods into cit-
ies—in ways that are physical, but also social and economic—
allows us an especially compelling glimpse at the processes that
create and sustain cities. It is easy to think of cities as sets of
streets and buildings, or as agglomerations of people in space,
but cities are really about connections, about the socioecon omic
and ph ysical links tha t allow each one of us to make a living, obtain
services that make our lives easier, and learn and invest our time
and resources. 20
Focusing on connecti vity points to some of the most impor tant
systemic properties of cities, known as scaling effects (agglomer-
ation effects, for economists).
These have now been measured
in urban systems throughout the world and throughout history.22
They describe how the products of socioeconomic interactions
in cities increase predictably faster than population, usually like
a self-similar power-law function.
Because these quantities
increase on a per capita basis, they account for the fa ct that larger
cities tend to be richer, more expensive, more creative, and more
congested than smaller towns. 24
These effects are explained as the result of the concentration
of social networks in space and time, emphasizing that cities are
devices to create general-purpose network effects.
The term
“network effects” is best known from technology (the phrase
originated in telecommunications); it refers to the phenomenon
where the value created by a group is not proportional to the
group’s numbers, but rather to its interactions.
Network ef fects are the forces that create cities.
Their aver-
age magnitudes are measured through scaling analysis (Figure 2).
This approach plots urban quantities (such as wages, GDP, or
quantities of built space or infrastructure) versus population.
Such relations represent a large number of predictable quantita-
tive general facts about cities and are therefore a basis for urban
science. For example, wages in US metropolitan areas (Figure 2)
increase by about 11% for each doubling of cit y size. In larger cit-
ies such as New York or Los Angeles, people on average make
more money, though they also spend correspondingly more.
To explain these general patterns, urban scaling theory starts
out with individuals
—you or me—and embeds all the socioeco-
nomic interactions we exp erience over time—at home, work, shop-
ping, friendships, etc.—in the built spaces of cities, subject to the
budgets in time and money we are all familiar with. This can be
written down mathematically, but is, of cour se, very complicated to
account and solve for in detail. It turns out that only certain aver-
ages are necessar y to calculate how total urban quantities , Y - such
the size of the economy, the sum of wages, or the cit y’s built area —
depend on population, N, following a relation like:
where Yo is a time-dependent intercept (the value of Y for a
small town with one person) and β is the scaling exponent, which
accounts for the p ercent change in Y with ever y percent change in
a city’s population, N.29
These two quantities are the result of how the spaces of cities
are used, and how daily cost s of movement for people, goods, and
information are counterbalanced by net socioeconomic benefits
of their interactions. Because real cities deviate somewhat from
these average expectations (the black solid lines in Figure 2, left),
residuals from scaling analysis—the vertical deviations of each
point from the black line—also provide a set of population size
independent of urban performance indicators (Figure 2, right).30
The fabric of cities in terms of the detailed connectivity of
their networks is the subject of much work in urban science
presently, using technologies such as cell phones, GPS tracking
for people and vehicles, check-in point data, and more.
methods examine the organization of social and economic net-
works and illuminate the importance of public spaces, their rel-
ative success, and associated issues of equity and segregation.
Urban design aimed at addressing these issues and, generally,
at creating urban connections can also be assessed over time
using emerging socioeconomic network data and associated
methods of spatial analysis.
Complex systems displaying network effects have several
important prop erties. Being more connected allows in dividuals to
specialize further and exchange services and information.
in turn, encourages each individual to pursue new knowle dge that
can be valued by others. This naturally expands and articulates
the range of urban activities in the network generating a self-
organized kind of collective design, which is very hard to create
These effects reveal the essence of cities and point to charac-
teristics of the built environment that nurture them.
The opera
house, the hospital, the fashion district, the marketplace, govern-
ment offices, public spaces, etc., agglomerate people, support
r F igure 2. Scaling analysis of w ages in US Metropolitan Are as (left) from 1969 (blue) to 2016 (brown) showin g network effec ts (exponent, solid
black lines) so that w ith each doubling of city size, wa ges per capita increase b y 11%. T he right panel shows the dev iations (vertical distanc e of each
point from the b lack lines on the left) fro m scaling as a measure of scale -independent per formance (also known as SA MIs): Silicon Valley=black;
Boulder=blu e; Las Vegas=red; New York=yellow, Los Ange les=cyan; Lake Havasu, A Z=green; Mc Allen, TX=purple .
TAD 3 : 1
Designing for Complexity
them, inspire (or discourage) them, and help them in their quest to
connect with others to make a meaningful living.
An emphasis on connections naturally creates more people-
centric design.
In cities, where all people are interdependent,
we can support individuals by facilitating large and diverse net-
works around them. Most challenges of urban development are
directly related to lack of certain types of connectivity, from lack
of urban services to jobs, to political participation. These failures
to realize the potential of the city for everybody turn out to have
a very strong spatial signature at the scale of neighborhoods.
Neighborho od Effects in Cities
One of the most important (unintended) consequences of
past urban planning and policy are distributional effects.
Distributional effects are the adverse consequences of an urban
intervention that affect distinct types of people differently. Many
examples of urban planning in the past have favored richer popu-
lations and majorities but further disadvantaged poor or minority
communities. To the planner’s credit, distributional effects are
very hard to avoid in large dense cities. For example, building the
Cross Bronx Expressway, a six-lane highway through NYC in the
1950s and 60s, did create an important traf fic circulation link, but
arguably rendered adjacent poor neighborhoods even poorer and
more disconnected. One person’s connection can be another’s
barrier. Other common examples are neighborhood gentrifica-
tion, slum clearing, inadequate pub lic housing, redlining, tra ditional
urban renewal, and certain forms of zoning. These negative long-
lived effects are increasingly plain to see, given the much greater
spatial and temporal resolution of both traditional data and new
technologies such as cell phone traces, spatialized credit card use,
and real estate transac tion data.
The stark dif ferences and inequalities between neighborhoods
in any large city is a general phenomen on known as “neighborhood
The inequality and diversity of cities is most often pres-
ent across neighborhoo ds. Important quantities are economic and
racial composition differences, health (including life expectancy),
and violent crime disparities. New quantitative tools allow us to
measure selection effects as they happen through pattern analy-
sis at any spatial scale within cities. These ef fects can be naturally
measured in cities (as in other complex systems) in units of infor-
mation, as the spatial sorting of people creates patterns such as
those in Figure 3, for income. Driving forces for this spatial selec-
tion are various, but real estate markets and especially housing
stocks and amenities that support a diversity of people appear
to be key. In the US and Canada, real estate-driven dynamics
are thought to be the main source of neighborhood segregation
though race and ethnicity have played a strong historic
role and these quantities remain corelated. Better urban planning
and design for the local coexistence and interaction of a diversity
of people, as well as for a more equitable access to urban ser vices
and amenities, is critical to mitigate both ex treme exposure to neg
ative outcomes, especially crime and poor edu cation, and avoiding
nefarious distributional ef fects, while generating oppor tunities for
human development.
Designing and Measuring Urban S ustainable Development
Urban planning is now being called upon to adopt a people-cen-
tered approach, and a more incremental and inclusive stance, as
described in the UN’s New Urban Agenda and in many cities’ sus-
tainable development plans. 39
As a result, a new but increasingly common practice in urban
planning and policy is the setting of ambitious quantitative
r F igure 3. Mean income by ce nsus tract in Atlanta, G A (left) and a measure of spatial s election (right) with red impl ying very atypical n eighborhoods
in terms of incom e distribution versus the metro politan area (strong selec tion) and grey neighborh oods that are microcosms of the c ity as a whole
(weak selection).
sustainability and equity goals for cities on a timeframe of a few
decades. This allows cities to think about their daily operations
more strategically, provides prestige and leadership for mayors,
and gives traction to transformative solutions that would not
arise by chance. For example, the city of Los Angeles has recent-
ly upped its goals and declared that it will be completely carbon
neutral by 2050, while also raising its measurable standards of
socioeconomic equity. The UN-led Sustainable Development
Goals provide the most comprehensive framework for address-
ing challenges of human sustainable development in a systematic
manner by 2030.40
Building quantitative indices that can track change is a funda-
mental step in evaluating progress, which is typically assessed
annually. The most important property of any such multidimen-
sional index is whether its components are complementary or
substitutable. Complementarity can be built out of multiplying
individual components, meaning that all must be achieved at a
high-level for the composite index to rank high. This is at the
core of, for example, the Human Development Index, which
scores a society on wealth, health, and education jointly.
United States currently ranks thirteenth in the world in the HDI
despite high wealth per capita because it scores low among
high-income nations on health. In contrast, indices based on
substitutable quantities allow a society to score high if any of
the components is sufficiently attained, as in a sum of individual
dimensions. Figure 4 shows a development index for neighbor-
hoods that accounts for access to permanent housing, water,
power, and sanitation in a complementary way.
Spatializing these goals exposes the universal reality of
strong neighborhood effects, here also shown in Figure 4,
in terms of inequities of delivery toward objectives. A sys-
tematic analysis of these effects, Figure 4, shows that action
towards goals can create, at first, greater place-based inequal-
ity. Only as the delivery of all components reaches high lev-
els do spatial inequalities subside and eventually disappear. A
better understanding of spatialized trajectories of multidimen-
sional development in cities is critical for an operational under-
standing of vicious and virtuous cycles of growth and decay
and for the construction of more equitable urban design and
policy solutions.
Because we are dealing primarily with existing situations,
urban planning must work within these systems, not hope for
easier tabula rasa (green field) approaches. This requires start-
ing out by understanding context and history, incorporating local
information, while at the same time creating technical solutions
whose quality must be assessed locally over time and in terms
of the entire heterogeneity of the populations affected. This
requires new tools and a new approach to participatory practices
and assessment.
The Uses of Technology an d Data in Urban Design and Policy
As our access to more local situations and quantitative informa-
tion about people and places grows, it is tempting to think that
this will inexorably lead to optimized cities and “perfect” design.
However, this is not likely to be true because of a fundamental
issue: as complex adaptive systems, cities are environments with
very high computational complexity.
This means that, much like
a game of chess—but with millions of pieces and varied rules—
while characterizing the city now via ambient data (i.e., describing
the chess board) is relatively easy, computing all its future states
and picking the best scenario (i.e., playing “chess”) is exceedingly
computationally intensive. This, ultimately, is the main argument
for urban science as a synthesis of generalizable principles about
how cities operate that can be made context-specific in design
and policy as nee ded. There are several uses of data and informa-
tion and communication technologies that evade thes e issues and
that are particularly congenial to the processes of socioeconomic
interactions that make up and sustain cities. A few examples are:
Dynamical Bespoke Mapping
Cities are known to social psychologists to create situations of
cognitive overload and a number of resulting behavioral adapta-
This makes access to spatialized, context-specific infor-
mation and navigation a critical aid. Maps can also convey future
possibilities for collective spaces an d analysis of opportunities and
w F igure 4. Spatialized sustaina ble development index in
Johannesb urg, South Africa (to p) and it s average and standard deviati on
across neighb orhoods in each city in B razil and South Afric a (bottom).
Mid-levels of go al attainment tend to show th e greatest spatial inequalit y
(along the ver tical axis), the solid curve d line shows the highest level
of inequalit y possible. Adapted from C . Brelsford, J. Lobo, J . Hand,
and L. M . A. Bettencour t, “Heterogeneit y and Scale of Sustain able
Developm ent in Cities,” Proceedings o f the National Academie s of Sciences
201606 033 (2017).
Standard Deviation
Sustainable Development Index
TAD 3 : 1
Designing for Complexity
dangers over space and time. Maps will continue to evolve and
become even richer and more dynamical resources for personal-
ized decision support as well as new “cranes for the imagination.”
Real-Time Evaluation and Optimization
Much of what has become recently known as urban informat-
ics deals with ambient evaluation of urban performance and
the development of feedback loop “control-policies” (in the
sense of engineered systems) to run services more effective-
Many short-term problems such as 311 services, running
transit on time, picking up trash, food inspections, etc., are
routine operations. As such, they do not suffer from high com-
putational complexity and can be conceptualized and solved in
terms of emerging tools of dynamical optimization and artificial
intelligence. The design of certain urban services—such as Bus
Rapid Transit—to follow more stereotypical operations so that
they better conform to optimization can also help some of these
strategies and deliver more reliable services.45
Solving Coordination and Political Problems
Data and design, when tied to realities of a shared urban environ-
ment, can help solve co ordination and political problems by allow-
ing various urban stakeholders to identify common problems,
understand processes leading to them, and test solutions in more
transparent and reliable ways.46
Because the inequality of cities is expressed mostly in place-
based ways, starting at the neighborhood level is instructive
and productive.
The neighborhood scale is also convenient
as a social unit (a fact well-known to sociologists) in which peo-
ple can self-organize, identify common problems, and mobilize
toward solutions.48
The challenge with local interventions, however, are their sys-
temic network effects. Many of these linkages, which are at the
root of our (lack of) understanding of processes of human devel-
opment, remain to be studied more systematically. For example,
as we improve urban services, we should reasonably expect that
the local population becomes healthier and has more time. But
are they also likely to develop the means to pay for these ser vices?
What is the (statistical) sequence of events from the perspective
of the individuals served? Can residents respond by using their
time to find better jobs, or invest in their human capital, so that
a virtuous cycle of economic growth and service improvement
can follow? If not, should the provision of education, training, and
potential employment opportunities be part of the intervention?
What is the scope of any urban inter vention that can trigger a vir-
tuous self-reinforcing cycle of positive change for the individuals
involved and for their city?
These questions are important because development poli-
cies are typically not systemic.
They often stall because
many elements around people remain unchanged.
The his-
tory of international development policies is littered with these
sorts of outcomes, as are attempts to deal with many of the
most persistent problems of cities, including poverty, violence,
and poor health.
What is new regarding data and technology is our growing
ability to be aware and track many of these systemic effects like
never before.
In this sense, spatial design in cities is more about
designing social capital as self- sustaining civic, cultural, social, and
political processes, and less about things or spaces. The perma-
nence and communal chara cter of spatial design can uniquely cre-
ate bridges (or barriers) that make all the difference for weaving
together socioeconomic processes in the city.
Designing with
deeper knowledge of these processes is key.
Outlook: New Framework s and Methods for Designing Cities
as Complex Adaptive Systems
Spatial design is a critical ingredient to great cities and an enabler
of open-ended sustainable human development.
happens somewhere, but in cities, everything is more connected
and concentrated in space and tim e, leading to natural avalanches
of ideas and outcomes.54
In most past instances, good design of this kind has emerged
gradually through bottom-up historical processes.
design processes with the same power and scope by intention-
al means requires deeper systemic knowledge and remains a
work in progress.
Because cities are about the spatiotemporal concentration
of human interactions,
buildings and public places play a criti-
cal role in inspiring, housing, and promoting such connections.
Markets, civic buildings, public art sites, and neighborhoods are
all nodes for concentrating diverse social activities, which in
turn ripple city-wide and beyond. They can promote social capi-
tal, reduce transaction costs, and create a congenial stage for
life of many kinds.
Technology is helping to incorporate and coo rdinate an increas-
ingly large amount of information into these processes, including
many technical aspects of spatial design, local knowledge, and
aspirational societal objectives.
As a consequence, designers
must direct their creativity to more systemic problems related to
our increasingly complex urban societies and our relationship to
evolving natural environments.
It has been the holy grail of design to create and sustain com-
plex adaptive systems with the same vitality as cities or ecosys-
I have argued here, based on what we now know about
these complex systems, that we should be able to do that and
more. What is needed is an approach that accumulates general-
izable knowledge about design processes. I emphasized that the
critical scale is the city, neighborhood by neighborhood, and to
some extent also the scale of large buildings and public spaces.
At smaller scales, the objectives of design become clearer and
less challenging both conceptually and technically. At the urban
scale, however, history, local knowledge, choices, memory, sym-
bolism, narrative, and imagination all play out in marvelous ways
that we are yet to fully understand and harness for the design of
great spaces.59
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Economic Life (New York: Vintage Books, 1985); P. Geddes and
J. Tyrwhitt, Patrick G eddes in India (London: Lund Hump hries,
7. S. Kostof, G. Castillo, and R. Tobias, The City Assemble d: The
Elements of Urban Form through History (Columbus: Little,
Brown, 1999).
8. Alexander, Notes on the Synthesis.
9. Kostof et al., The City Ass embled.
10. UN-Habitat, World Cities Repo rt 2016; UN-Habitat, Street s as
Tools .
11. J. Jacobs, T he Death and Life; Kostof, et al., The City Assemble d;
C. Alexander, The Timeless Way of Building (New York: Oxford
Univ. Press, 1979).
12. A. Aksamija, Integrating Innovation in Architecture: Design,
Methods and Technology for Progressive Practice and Research
(Chichester: Wiley, 2016).
13. Alexander, The Timeless Way; Aksamija, Integrating Innovation.
14. D. C. Dennett, From Bacteria to Bach and Back: Th e Evolution of
Minds (New York: W.W. Norton & Co mpany, 2017).
15. C. Brelsford and L . M. A. Bettencourt, “Optimal Reblocking
as a Practical Tool for Neighb orhood Development,”
Environmental and Planning B: Urban Analytics and City
Science 46 , issue no. 2 (2017): 303- 321. https://doi.
org /10 .117 7/239980 8317712715.
16. D. Mitlin and D. Satter thwaite, Urban Poverty in the Glob al
South: Scale and Nature (New York: Routledge , 2013); S. Patel,
C. Baptist, and C. D’Cruz, “ Knowledge is Power—Informal
Communities Ass ert Their Right to the City Through SDI and
Community- Led Enumerations,” Environment and Urbanization
24, issue no. 1 (2012): 13–26; Brelsford et al. , “Toward Cities.”
17. Mitlin and Satter thwaite, Urban Poverty ; Patel, et al.,
“Knowledge is Power”; Brelsford et al., “Optimal Reblocking.”
18. Bettencourt, “Th e Kind of Problem”; Lynch, Good City.
19. Mitlin and Satter thwaite, Urban Poverty ; Patel et al.,
“Knowledge is Power.
20. Bettencourt , “The Kind of Problem”; Jacobs, Th e Death and
21. L. M. A . Bettencourt , J. Lobo, D. Helbing, C. Kühn ert, and G.
B. West, “G rowth, Innovation, Sc aling, and the Pace of Life in
Cities,” Proc. Natl. A cad. Sci. 104, issue no. (2007): 73 01–7306;
S. S. Ros enthal and W. C. Strange, “Evidence on the Nature
and Sources of A gglomeration Economies,” In Han dbook of
Regional and Urban Economics, Volume 4 , ed. J. V. Henderson
and J. F. Thisse (Amsterdam: N orth Holland Publishing, 20 04):
22. L. M. A Betten court, “The Origins of Scaling in Cities,” Science
340 , issue no. (2013): 1438–1441; S. G. Ortman , A. H. F.
Cabaniss, J. O. Sturm, and L . M. A. Bettencourt, “S ettlement
Scaling and Increasing Returns in an Ancient S ociety,” Sci. A dv.
1, iss ue no . (201 5): e14 00 066 –e140 00 66.
23. Bettencourt et al., “Growth , Innovation, Scaling”; Bettencourt,
“The O rigins of Scaling.”
24. Bettenco urt, “The Kind of Problem”; Bettencourt, “The
Origins of Scaling .
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Evolution of Complex Infor mational Networks,” Proc. IEEE
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of S caling.”
27. Bettencour t et al., “Growth, Inn ovation, Scaling.”
28. Bettencourt , “The Origins of Scaling.”
29. Ibid.
30. L. M. A Betten court, J. Lobo, D. Strumsky, and G. B. Wes t,
“Urban Sc aling and Its Deviations: Revealing the Struc ture
of Wealth, Innovation a nd Crime across Cities,” PLoS ONE 5,
issue no. (2010): e13541.
31. M. Schlapfer et al., “Th e Scaling of Human Interactions with
City Size,” J. R. S oc. Interface 11, issue no . (2014): 20130789–
20130789; M. Bat ty et al., “Smart Cities of the Future,” Eu r.
Phys. J. Spec . Top. 214, issue no. (2012): 481–518.
32. A. Smith, The Wealth of Nations (New York: Bant am Classics,
2003); L. M. A . Bettencourt , H. Samaniego, and H. Youn,
“Professional Diversit y and the Productivit y of Cities,” Sci.
Rep. 4, issue no. (2 014); Bettencour t, “Impact of Changing
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of Big Data in Cities,” Big Data 2 , issue no. (2014): 12–22.
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35. UN-Habitat, Worl d Cities Report 2016.
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Miss: Distributional Ef fects of Welfare Reform Exp eriments”
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37. R. J. Sam pson, Great American City : Chicago and the Enduring
Neighborhoo d Effect (Chicago: Univ. of Chicago Press,
2012); Y. M. Ioannides and G. Topa, “Neighborho od Effects:
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40. United Nations, Sustainable Development Goals, 2 015.
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43. Milgram, “The E xperience of Living.”
TAD 3 : 1
Designing for Complexity
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47. Sampson, Great American City; W. J. Wilson, When Work
Disappears: The World of the New Urban Poor (New York:
Knopf Doubleday Publishing Group, 2011).
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Whom Do Residential Co ntexts Matter? Moving Away from
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49. B. Ramalingam, Aid on the Edge of Chaos: Rethinking
International Cooperation in a Complex World (Ox ford:
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as Too ls.
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56. Bettencourt , “The Origins of Scaling.”
57. Aksamija. Integrating Innovation;EDGE Buildings | Build
and Brand Green,” EDGE Buildings (website), access ed
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in Evolution; Alexander, Notes on the Synthesis.
59. Jacobs, The Death and Life; P. Hall, Cities in Civilization (New
York: Pantheon Books, 1998).
Luís M. A. Bettencourtis the Pritzker Director of the Mansueto
Institute for Urban Innovation an d Professor of ecology and
evolution at the University of Chicago, as well as an External
Professor of complex systems at the Santa Fe Institute.His
research emphasizes the creation of new interdisciplinary
synthesis to describe cities in quantitative and predictive ways,
informed by classical theor y from various disciplines and the
growing availability of empirical data worldwide. Trained as a
theoretical physicist, h e has worked extensively on complex
systems theor y and on cities and urbanization. He is the author
of over a hundred scientic papers and several edited books.
Bettencour t’s research has been featured in leading media
venues such as the New York Times , Nature, Wired, New
Scientist, and the Smithsonian.
... Slimme steden richten zich op het inzetten van technologie om de nadelige gevolgen van dit soort ontwikkelingen tegen te gaan. Dat doen ze met slimme toepassingen voor onder andere bezoekers en door de stad te monitoren gebaseerd op digitale vormen van dataverzameling [8]. De genoemde meetinstrumenten ontwikkeld in het ICT-onderzoek begonnen het experimenteerstadium te ontgroeien en worden ondertussen steeds meer in de dagelijkse praktijk van steden toegepast. ...
... Een ander voorbeeld is de bevestiging van de relevantie van de vier door Jane Jacobs geformuleerde randvoorwaarden voor diversiteit [3] op basis van een combinatie van telefoondata uit zes Italiaanse steden met sociodemografische informatie [13]. Er bestaat momenteel nog geen geïntegreerde, algemene science of cities, maar als die er ooit komt wordt verwacht dat het feit dat er een verandering van perspectief is gekomen -weg van plaatsen en individuen en richting verschillende soorten van interactienetwerken -hier een belangrijke bijdrage aan zal leveren [10,8]. ...
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In deze tijden waarin de vraag naar winkelruimte in veel binnensteden terugloopt, worstelen gemeenten om hun binnensteden bruisend te houden. In het project Hightech Binnenstad is een exploratief, praktijkgericht onderzoek uitgevoerd, bedoeld om gemeenten handvatten te bieden voor datagedreven werken met betrekking tot hun binnensteden. We werkten samen in een consortium van acht gemeenten, elf bedrijven en een kennisinstelling. Het doel was om kennis en ervaring op te doen die gemeenten direct toe kunnen passen in hun dagelijkse praktijk en die de bestaande (papieren) binnenstadmonitor kunnen aanvullen of verbeteren. Het project besloeg zes fasen (zie Tabel 1). De eerste fase resulteerde in de volgende onderzoeksvraag: Hoe meet, visualiseer en analyseer je de effecten van interventies in de binnenstad op de bezoekersaantallen en op de beleving en tevredenheid van bezoekers op een fijnmazige maar privacyvriendelijke manier? In de vijf fasen die volgden op deze eerste fase hebben we de onderzoeksvraag beantwoord. We hebben verschillende datasets en meetinstrumenten geïdenti ceerd en ook zelf ontwikkeld. Deze zijn bedoeld om gemeenten te helpen inzicht te krijgen in de effecten van hun interventies die beogen hun binnensteden beter te laten functioneren. De datasets en meetinstrumenten vallen uiteen in drie categorieën: 1) interventies in een ruime zin van het woord (alle variabelen die invloed hebben op de perceptie van een bezoeker van de binnenstad), 2) bezoekersaantallen en 3) bezoekerstevredenheid en -beleving. Tabel 2 laat zien voor welke datasets en meetinstrumenten we in deze drie categorieën gekozen hebben. Met deze datasets en meetinstrumenten hebben we in de binnensteden van drie Nederlandse gemeenten – Amersfoort, Deventer en Zwolle – pilots uitgevoerd. De verschillende datasets en meetinstrumenten zijn samengebracht en gevisualiseerd in een dashboard. Dit dashboard is tijdgebaseerd: door met de muis over een tijdbalk te bewegen kan op een kaart van de binnenstad op het dashboard als het ware door de tijd worden gereisd en kunnen datasnapshots van de binnenstad op verschillende momenten bekeken worden. De bruikbaarheid van het dashboard en de verzamelde data is in een gebruikersonderzoek met twee beleidsmedewerkers van de gemeente Deventer geëvalueerd. Het onderzoek heeft een aantal concrete resultaten en geleerde lessen opgeleverd. We vatten hier de belangrijkste kort samen: • Het ontwikkelde tijdsgebaseerde dashboard en de daarin getoonde data zijn waardevol voor beleidsmedewerkers voor het beantwoorden van vragen met betrekking tot binnensteden. • De realisatie van de datasets, de meetinstrumenten, een dataplatform en het dashboard vraagt om een aanzienlijke eenmalige investering van tijd. Door gebruik te maken van de geleerde lessen uit het project Hightech Binnenstad kan voor gemeenten die iets vergelijkbaars willen realiseren de tijdsinvestering aanzienlijk ingeperkt worden. Deze tijdinvestering kan voor lange tijd zorgen voor tijdsbesparingen. • Data verzamelen over bezoekerstevredenheid en –beleving in binnensteden bleek een relatief onontgonnen terrein. Het project Hightech Binnenstad heeft aandacht besteed aan het inzetten van generieke digitale instrumenten voor deze doeleinden en aan het ontwikkelen en uitproberen van nieuwe. Op basis van data van over binnenstadbezoekers is tevens een voorlopige classi catie van belevingsaspecten van binnensteden gemaakt die gebruikt kan worden voor het formuleren van vragen over bezoekersbeleving ten aanzien van binnensteden. • Vanwege de extra aandacht die gemeenten vroegen voor privacyvriendelijke oplossingen zijn vijf aanbevelingen opgesteld die ze kunnen helpen bij het vormgeven van het maatschappelijk debat over dataverzameling in binnensteden.
... With the complex nature of cities, there is need for a constant adaptation to the changing environment, and this means that the traditional and conventional methods or approaches in planning are no longer adequate to curtail development challenges (Bettencourt 2019). Therefore, the aura of computational urban planning and design techniques becomes more relevant in complementing the old planning methods to achieve sustainable and resilient urban environments. ...
... On the one hand, they overcome previous limitations of urban simulations with new modelling approaches that can capture, test, and interactively demonstrate alternative scenarios of urban dynamics more realistically and more accurately. On the other hand, these frameworks try to raise awareness of the complexity of urban systems, while facilitating policy information and decision-making by helping to envision complex scenarios [115], [116]. The analysis of our results suggests that the kind of street seems to matter more than the raw amount of meters of removed road sections when it comes to strategically improving mobility by closing streets. ...
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Current trends in urban planning aim at the reduction of space for private vehicles to promote alternative mobility, more diverse activities on streets, and reduced pollution for healthier cities. In our study, we evaluate a number of "what-if scenarios" of "city pruning" regarding traffic restrictions for Barcelona by means of realistic, agent-based computer simulations in order to identify their impact on travel performance and the environment. Comparing existing plans designed by the City of Barcelona with variants of those, we find positive counterintuitive effects related to "Braess' Paradox", which result in the reduction of emissions (-8% of main pollutants) and traffic congestion (-14% of travel time) solely by closing some streets to motor vehicles. These findings indicate a further potential to improve the quality of life in cities using positive counterintuitive effects of street repurposing and it is an opportunity for participatory and sustainable city-making beyond the ongoing public debate.
... Often, to meet these goals, urban renewal projects, which repurpose or 'renew' ex-industrial greyfield sites close to central business districts for high-density housing, are proposed. To successfully meet these goals, urban renewal projects require substantial departures from traditional, past urban norms [2,[12][13][14][15][16]. ...
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The design of green infrastructure in urban renewal sites is complex, requiring engagement with existing communities and future sustainable development goals, consideration of existing and future urban forms, changing climatic conditions, and the sites often being in low-lying and flood-prone areas. Traditional street tree decision-making approaches are inadequate for addressing the scale, environmental complexity, and mutability of decisions involved in urban renewal projects—new tree selection approaches that consider complex competing criteria for tree selections addressing stormwater management systems, visual assessment and solar amenity are needed. This paper describes a new method of multi-criteria street design decision modelling that combines outputs from hydrology modelling, digital procedural tree modelling and urban form analysis, with animation and gaming technologies. We evaluate our approach through application to the design of a large-scale, urban renewal project underway in Melbourne, Australia. The results of the study demonstrate the functionality of our model, which allowed the simultaneous output of streetscape visualisation, with tree selection responding to integrated stormwater management infrastructure and flooding, along with the likely overshadowing conditions of urban renewal built-form. Our multi-criteria approach makes a significant contribution to the tools available to urban designers, planners and landscape architects in their pursuit of smarter streetscape design decisions that respond to complex spatial, cultural and climatic urban challenges.
... With the complex nature of cities, there is need for a constant adaptation to the changing environment, and this means that the traditional and conventional methods or approaches in planning are no longer adequate to curtail development challenges (Bettencourt 2019). Therefore, the aura of computational urban planning and design techniques becomes more relevant in complementing the old planning methods to achieve sustainable and resilient urban environments. ...
The volatile development arena has not spared urban planning from the impacts it has wrought. With many changes toward modernity, sustainability , and smart growth, urban planning has seen more changes resulting in rising complexity that has made it difficult to proffer long-lasting solutions to development challenges (Healey 2007). Whereas urban planning is complex, involving various aspects and disciplines which require asynchronous approaches (Batty 2008), much difficulty has resulted in trying to tackle facet by facet especially in Africa (Wilson et al. 2019). It is the purpose of this chapter to shed light on a more better approach to urban planning. The chapter aims at providing an understanding of computational urban planning and explores its importance in helping planners provide better design solutions. It explores how the computational approach has been appreciated in various continents and how Africa fairs concerning the use of computational urban planning techniques in planning. It unravels the efforts of African cities, the challenges they face in trying to use the quantitative models in planning, and the opportunities they have. The desktop study is foundational to this chapter as it is used to capture the center of the argument. Case studies are used in the chapter to assess how computational urban planning has been successful in cities of other continents. Computational urban planning is complementary to the usual planning methods of using CAD software as it allows for quantitative analysis and data assimilation (Wilson et al. 2019). The urban planning environment, being a complex dynamic system, requires the alignment of planning strategies in a way that is compatible with the modern urban environment (Chadwick 1971; Dalberg 2016). It has become imperative for the players in the urban design field to adapt to the changes and become knowledgeable of better techniques that can effectively be used to achieve optimal solutions for urban problems. The process of formulating master plans has been a time-consuming and complicated one as more tasks like data collection and even data analysis are done in a piecemeal way that does not fully solve the planning challenges (Lane 2005; Taylor 2009; Chigara et al. 2013). Thus, applying such traditional planning approaches becomes a slow process which may even encounter hiccups along the way, and by the time the master plans are implemented, a lot more and unanticipated changes would have taken place. Therefore, the adoption of more effective planning methods such as computational urban planning models becomes very necessary and hence should be used early and consistently in the planning process (Wilson et al. 2019). This chapter, therefore, explains the relevance of applying computational urban planning techniques in African cities as an effective means toward designing for a more robust, resilient, and sustainable city environment.
... Since all ARMA models imply the assumption that the time series is stationary, then we must perform a unit root test on r to judge the stationarity of r. e ADF test is used to test whether r is stable. e test results show that the ADF test statistics (absolute value) are all significantly greater than the critical value at the 2%, 8%, and 20% significance levels, and the null hypothesis containing unit roots is [25][26][27]. ere are many application scenarios of visual design in smart cities, and its breadth and breadth involve all walks of life, but it is defined as the field of medical visualization. e fundamental principles of visual design and the level of interactive psychological thinking do have similarities. ...
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With the rapid growth of our country’s economy, fuzzy theory has become more and more widely used in the financial field. This research mainly explores the application of financial cloud based on fuzzy theory in the sustainable development of smart cities. This research will apply the related knowledge of fuzzy theory, combine the traditional information system risk assessment thought and cloud computing system risk assessment, and propose a series of risk assessment models for the cloud computing system. First, design a smart city model to analyze the potential security issues of the financial cloud computing system. The security level model of cloud computing establishes an index system for evaluation objects based on the level of the security model and uses expert evaluation methods to build models for all levels of the risk profile through the analysis-level process and establish a fuzzy relationship model for each evaluation object value. We objectively evaluate the smart city model based on the designed financial cloud platform. Then, specific statistical analysis is performed on the fuzzy relationship model corresponding to the weight value of the evaluation object, the calculation result is finally obtained, and the risk assessment report of the financial cloud computing system is provided. For every 2% increase in trade dependence, the informatization level of smart cities will drop by about 0.03% on average. The findings have long strengthened the overall coordinated development of the financial system, optimized the financial structure, improved development efficiency, promoted close integration of science and technology and finance, and played the role of government leader it was to fulfill. It shows that we need to maximize it and improve the informatization when building smart cities. The level of development is very important for accelerating urban construction.
This chapter explores the importance of computational models and techniques in urban planning and unravels how it has been applied in other continents. It explores the efforts that have been made in Africa and analyzes the challenges in trying to use computational planning methods. It explores possible benefits that can result from using computational methods, some of which include easing master plan formulation. It reviews the existing gaps and opportunities for Africa. The desktop approach is foundational to this chapter as it is used to capture the center of the argument. A few case studies are used in the chapter to assess how computational urban planning has been successful in cities of other continents. The study shows the importance of applying computational urban planning in contributing to sustainability and efficiency in planning. It recommends the use of computational quantitative techniques in Africa and gives recommendable solutions to how African cities can counter planning challenges by using computational models. Computational urban planning models are effective toward accommodating rapid urbanization, easing master plan formulation, and ensuring sustainable and livable city environments.
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El metro es una infraestructura urbana que tiene el potencial de integrar la trama de espacios públicos y tejido edificado en la ciudad. Esto se explora en el Metro de Santiago, tomando como caso de estudio la línea 3. A través del análisis morfológico del entorno de sus estaciones, se contrasta la vocación urbana inicial del sistema con sus actuales lineamientos de diseño. Así se conforma, en suma, un relato de las formas de crecimiento de Santiago y de las estrategias de inserción del metro en ellas, que revela la preferencia actual por el hermetismo y la autonomía de la red.
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Kampung, as a form of Indonesia’s informal urbanism, requires special attention in terms of urban development. Efforts to manage the quality of kampung space have become very important, including planning and development through smart system applications. However, the culture of the community living in kampung in accepting a new system has not been well-mapped. This research aimed to be the beginning of the development of smart kampung system that focuses on identifying the community’s preferences for the concept of this novel system. The research was conducted by a case study in Terban Subdistrict, Gondokusuman, Yogyakarta using Analytical Hierarchy Process methods. Initial findings indicate that Terban community’s preference for the concept of kampung is based on the concept of providing alternative energy and water quality. The research also shows that of smart city projects in the future should include community participation to ensure its applicability, acceptance, and sustainability.
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Fast urbanization is a common feature of many developing human societies. In many cases, past and present, explosive population growth in cities outstrips the rate of provision of housing and urban services and leads to the formation of informal settlements or slums. Slums are extremely varied in terms of their histories, infrastructure, and rates of change, but they share certain common features: informal land use, lack of physical accesses, and nonexistent or poor quality urban services. Currently, about 1 billion people worldwide live in slums, a number that could triple by 2050 if no practical solutions are enacted to reverse this trend. Underlying most problems of slums is the issue of lack of physical accesses to places of work and residence. This prevents residents and businesses from having an address, obtaining basic services such as water and sanitation, and being helped in times of emergency. Here, we show how the physical layout of any neighborhood can be classified quantitatively in terms of its access topology in a way that is independent of its geometry. Topological indices capturing levels of access to structures within a city block can then be used to define a constrained optimization problem, whose solution generates an access network that makes each structure in the settlement accessible to services with minimal disruption and cost. We discuss the general applicability of these techniques to several informal settlements in developing cities and demonstrate various technical aspects of our solutions. Finally, we discuss how these techniques could be used on a large scale to speed up human development processes in cities throughout the world while respecting their local identity and history.
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Significance Most nations worldwide have recently committed to solving their most severe challenges of sustainability by 2030, including eradicating extreme poverty and providing universal access to basic services. But how? Rapid urbanization is creating the conditions for widespread economic growth and human development, but its consequences are very uneven. We show how measures of sustainable development—identified by residents of poor neighborhoods—can be combined into a simple and intuitive index. Its analysis reveals that challenges of development are typically first addressed in large cities but that severe inequalities often result as patterns of spatially segregated rich and poor neighborhoods. A new systematic understanding of these processes is critical for devising policies that produce faster and more equitable universal sustainable development.
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We live in an era of increasing connectivity in human societies and in technology. These structural changes in the ways we interact with each other and with increasingly ubiquitous computational and communication devices have been formalized in research across several disciplines through the dynamics of complex informational networks. Complex networks are (mathematical) graphs, connecting nodes (people, computers) via edges (relationships, wires). While much progress in methods for network analysis has been achieved, the fundamental principles that drive network growth in human societies and in worldwide computer networks remain rather obscure. Mechanistic models for the origin of certain structural graph elements have now become common, but the formal connection between large empirical studies of network evolution and fundamental concepts of information, learning, and social theory remains only latent. To address these issues, I argue here that the most interesting aspect of the dynamics of informational networks in complex systems is that they are the physical manifestations of processes of evolution, inference, and learning, from natural ecosystems, to cities and to online environments. I formalize the general problem of learning and computation in network environments in terms of average structural network changes and propose a conceptual framework to explain the transition from initially static, undifferentiated, and information-poor environments to dynamical, richly diverse, and interconnected systems. I illustrate these ideas empirically by providing examples from cities, and from global computer networks and webs of documents. I finish with an overview of expected changes to urban form and function and to computational hardware under likely technological scenarios.
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There is much enthusiasm currently about the possibilities created by new and more extensive sources of data to better understand and manage cities. Here, I explore how big data can be useful in urban planning by formalizing the planning process as a general computational problem. I show that, under general conditions, new sources of data coordinated with urban policy can be applied following fundamental principles of engineering to achieve new solutions to important age-old urban problems. I also show that comprehensive urban planning is computationally intractable (i.e., practically impossible) in large cities, regardless of the amounts of data available. This dilemma between the need for planning and coordination and its impossibility in detail is resolved by the recognition that cities are first and foremost self-organizing social networks embedded in space and enabled by urban infrastructure and services. As such, the primary role of big data in cities is to facilitate information flows and ...
Labor supply theory predicts systematic heterogeneity in the impact of recent welfare reforms on earnings, transfers, and income. Yet most welfare reform research focuses on mean impacts. We investigate the importance of heterogeneity using random-assignment data from Connecticut's Jobs First waiver, which features key elements of post-1996 welfare programs. Estimated quantile treatment effects exhibit the substantial heterogeneity predicted by labor supply theory. Thus mean impacts miss a great deal. Looking separately at samples of dropouts and other women does not improve the performance of mean impacts. We conclude that welfare reform's effects are likely both more varied and more extensive than has been recognized.
The literature on neighborhood effects frequently is evaluated or interpreted in relation to the question, "Do neighborhoods matter?" We argue that this question has had a disproportionate influence on the field and does not align with the complexity of theoretical models of neighborhood effects or empirical findings that have arisen from the literature. In this article, we focus on empirical work that considers how different dimensions of individuals' residential contexts become salient in their lives, how contexts influence individuals' lives over different timeframes, how individuals are affected by social processes operating at different scales, and how residential contexts influence the lives of individuals in heterogeneous ways. In other words, we review research that examines where, when, why, and for whom do residential contexts matter. Using the large literature on neighborhoods and educational and cognitive outcomes as an example, the research we review suggests that any attempt to reduce the literature to a single answer about whether neighborhoods matter is misguided. We call for a more flexible study of context effects in which theory, measurement, and methods are more closely aligned with the specific mechanisms and social processes under study.