Measuring The Suburbanization of world
cities
with Remote Sensing Data
Christopher Chase-Dunn
Institute for Research on World-Systems
University of California-Riverside
John R. Weeks
Geography
San
Diego State University
v. 1-25-04, 11172 words
ASTER image of
Los Angeles.
The
problem of sustainable urbanization is crucial for the human encounter with the
consequences of our ballooning environmental footprint. Over half of the human
population of the Earth now lives in very large cities, and these have spread
rapidly over the land as population densities within cities have decreased and
cities have spread into huge city-regions. Our research project is developing a
methodology for measuring the rates and the nature of the areal expansion of world
cities and the patterns of decreasing population density in order to know
whether or not urban sprawl is accelerating or slowing down. We are also studying changes in the world
city-size distribution over the past three decades. The size distribution of world cities has been flattening since
the 1950s as megacities in the non-core countries have caught up in terms of
overall population size with the global cities of the core. We are examining this trend closely to see
if it has leveled off or accelerated.
And we are studying differences among the cities of the core and the
non-core with respect to the rates and nature of urban sprawl and the changing
structure of the built environment.
Our
project is developing measures for using satellite data to study changes in
world cities over the last three decades. Global coverage, large spatial scale,
easily standardized spatial format, and relatively low cost make satellite data
attractive for studying world cities and city-regions. The main problem is that
the satellite technology has changed very rapidly and so the comparison of
early with recent satellite data requires a sophisticated approach to
measurement error. In order to develop algorithms for solving this problem our
project is focusing on five cities in North America, Africa, and the Middle
East where we already have done research and have access to ancillary data on
land-use patterns that will enable us to develop techniques for decomposing
change into that which is due to measurement error and that which is real
change as indicated by the classification of data from the satellite imagery.
The nature of the human built environment has huge consequences for
humanity itself and increasingly large impacts on the global biosphere. Social
scientists have long studied the emergence of sedentism and the growth of
settlements, but only recently has attention been drawn to the relationship
between cities and the environment. Our study of the global city system and the
suburbanization of world cities has important implications for theories of
urban growth and the development of settlement systems, and for the
relationship between urbanization and the environment.
The project is producing an educational World
Cities Time-Map, a web-accessible spatio-temporal GIS that presents
animations of growth and suburbanization of the largest cities on Earth over
the period from 1950 to 2005. The World Cities Time-Map is designed to
provide information that will be valuable for public and private
decision-makers on the issues of sprawl and sustainable urbanization.
Measuring the Suburbanization of World
Cities with Remote Sensing Data
Changes in the global city size distribution,
especially its flattening as megacities have emerged in the non-core, have important
implications for theories of urban growth, globalization and the future of
global inequalities. Whether or not
rates of growth and suburbanization are accelerating, steady, or decreasing,
and how these may vary between core, peripheral and semiperipheral societies in
the world-system, will have important implications for theories of urban growth
and for the future of urban sustainability.
However, a fundamental problem faced by all researchers is the lack of
consistent data on the spatial extent of cities and, related to that, on the
changing population size and density of urban places in different parts of the
globe. This research project is developing new methods for using remotely
sensed data from satellites to study the growth of world cities and
city-regions, patterns of urban multinucleation, rates of suburbanization and
trends in the global size distribution of cities. The first stage of our research is designed to test alternative
strategies of using remotely sensed data to capture information about city
growth between 1980 and 2005 that can then be used to test theories of urban
growth, city systems, and urban sustainability. We propose to do this for five cities—two core and three non-core
cities, building on work already underway by the researchers. The second stage is to extend the analysis
beyond the five cities, ultimately producing data for the 251 world cities that
we have identified. The third stage is
to develop techniques by which urban spatial extent and urban population dynamics
can be modeled backwards in time, allowing us to “reverse engineer” the
theories of city growth and city system development.
The objectives of our research are the following: (1) define the
characteristics of urban places and the population living in urban places that
can be measured by the classification of data derived from remotely sensed (RS)
imagery; (2) test alternative methods of classifying RS data to most accurately
capture the changes taking place in the spatial and demographic nature of cities;
(3) test alternative methods of evaluating the measurement error inherent in
using different remote sensors to measure changes over time and differences
among places; (4) use the results of (2) and (3) to develop an algorithm to
decompose observed change in an urban place as derived from imagery into its
three constituent components of classification error, remote sensor error, and
real change; (5) use the measured real change to test theories about city
growth, especially suburbanization, and about its historical and potential
future effect on world city systems and urban sustainability; (6) disseminate
the results of both the methodological and theory-testing components of the
research to a wide range of potential users of this information, through traditional
academic outlets, as well through a website that incorporates time-mapping in a
way that will make the results accessible to a wider lay audience, including
high school students; and (7) use the results to lay out the plan to move from
stage one to stage two of our overall research program.
BACKGROUND AND
SIGNIFICANCE
Systems of cities represent human interaction networks
and their connections with the built and natural environments. Logically, the
study of city systems is a subcategory of the more general topic of settlement
systems. Once humans began living in fairly permanent hamlets and villages it
became possible to study the interactions of these settlements with one
another. Settlements are rarely ever intelligible without knowing their relations
with the rural and nomadic populations that interact with them. Archaeologists,
ethnographers and, of course, geographers, map the ways in which human
habitations are spread across space, and this is a fundamental window on the
lives of the people in all social systems.
The spatial aspect of
population density is one of the most fundamental variables for understanding
the constraints and possibilities of human social organization. The “settlement
size distribution” – the relative population sizes of the settlements within a
region-- is an important and easily ascertained aspect of all sedentary social
systems. And the functional differences among settlements are a basic feature
of the division of labor that links households and communities with larger
polities and interpolity systems. The emergence of social hierarchies is often
related to size hierarchies of settlements. And the building of monumental
architecture in large settlements has been closely associated with the
emergence of more hierarchical social structures – complex chiefdoms and early
states.
The role
of city systems in the reproduction and transformation of human social
institutions has been altered by the emergence and predominance of capitalist
accumulation, and by the control over mortality that has emerged within the
same historical-economic context. By their very nature urban populations
require a “surplus” of agricultural production in order to survive because
urban places are inherently non-agricultural.
Furthermore, prior to the control over mortality, the crowding of people
into urban environments almost invariably increased the spread of disease and
raised the level of mortality compared to rural places, and so the rate of
natural increase in most urban places for most of human history was negative.
For this reason, urban places needed constantly to recruit from the countryside
in order to sustain population size. Thus, whereas most of the important cities
of agrarian tributary states were centers of control and coordination for the
extraction of resources and labor from vast empires by means of
institutionalized coercion, the most important cities in the modern world have
increasingly supplemented the coordination of force with the manipulations of
money and the production of commodities. Obviously military force is still an important element of power in
the modern world-system, but the uses of military power have been fundamentally
altered by the predominance of capitalist accumulation. Furthermore, recruitment of labor has been
fundamentally turned on its head by the dramatic decline in mortality taking
place over the past centuries, which has (a) lowered death rates in cities more
than in rural areas, producing positive rates of natural increase, and (b)
lowered death rates in rural areas to levels well below fertility, producing
high rates of rural population growth, leading to a redundancy of the rural
population which then produces a steady flow of migrants to the cities in
search of jobs (Davis 1972; 1973; Weeks 2002).
The long
rise of capitalism was promoted by semiperipheral capitalist city-states,
usually maritime coordinators of trade protected by naval power. The Italian
city-states of Venice and Genoa are perhaps the most famous of these, but the
Phoenician city-states of the Mediterranean exploited a similar interstitial
niche within a larger system dominated by tributary empires. The niche
pioneered by capitalist city-states expanded and became more predominant in the
guise of core capitalist nation-states in a series of transformations from
Venice and Genoa to the Dutch Republic (led by Amsterdam) and eventually the Pax
Britannica coordinated by the great world city of the nineteenth century,
London (Chase-Dunn and Willard 1994). Within London the functions mentioned
above were spatially separated: empire in Westminster and money in the City. In
the twentieth century hegemony of the United States these global functions
became located in separate cities (Washington, DC and New York).
Thus the role of cities in world-systems
changed greatly as capitalism became the predominant mode of accumulation over
the last 500 years, and as death rates dropped precipitously over the past 200
years. In earlier world-systems the
biggest cities were empire-cities based on the ability of states to extract
resources using institutionalized coercion (armies, bureaucracies, etc.)
Capitalist cities existed, but they were in the semiperipheral spaces between
the large tributary empires. With the rise of Europe we have capitalist cities
becoming the most important cities in the whole world-system. This is
especially obvious with the rise of Amsterdam, London and New York – the world
cities of the capitalist era.
Globalization
The great
wave of globalization in the second half of the twentieth century has been
heralded (and protested) by the public as well as by social scientists as a new
stage of global capitalism with allegedly unique qualities based on new
technologies of communication and information processing. Some students of
globalization claim that they do not need to know anything about what happened
before 1960 because so much has changed that the past is entirely
non-comparable with the present. Most of the burgeoning literature on global
cities and the world city system shares this breathless presentism. All social
systems have exhibited waves of spatial expansion and intensification of large
interaction networks followed by contractions. The real question is which aspects
of the most current wave are unique and which are functional repetitions of
earlier pulsations.
Clearly
one of the unique elements of globalization is that it too is closely related
to the drop in mortality, because that is what set in motion the enormous
increase in worldwide population growth.
Control over mortality, and subsequently control over fertility as well,
took place not coincidentally within the realm of the capitalist world cities. Those cities created a social structure
within which scientific knowledge could flourish by stimulating a strong market
for the products of scientific research.
These included discoveries about the causes of communicable disease,
followed by discoveries of how to control communicable disease. Initially the benefits of these improvements
in life expectancy were limited to people living within the core countries, and
it was in the countries of Europe and North America that population first began
to grow rapidly in the modern world.
Between World Wars I and II these technologies began to be transferred
beyond the core, especially from the U.S. to Latin America. However, after
World War II, death control technology was spread globally, especially through
the work of various United Nations agencies, but funded by the governments of
core countries. Since declines in
mortality initially affect infants more than any other age group, there tends
to be somewhat delayed reaction in the realization of the effects of a
mortality decline until those people who would otherwise have died reach an age
where they must be educated, clothed, fed, and jobs and homes created for them
on a scale never before imagined. The response of core countries to this
enormous increase in demand in the rest of the world has been a large part of what
is seen as globalization.
World
Cities
According to the theorists of
global capitalism it was during the 1960’s that the organization of economic
activity entered a new period expressed by the altered structure of the world
economy: the dismantling of industrial centers in the United States, Europe and
Japan; accelerated industrialization of several Third World nations; and
increased internationalization of the financial industry into a global network
of transactions (Sassen 1991). With the emerging spatial organization of the
new international division of labor, John Friedmann identified a set of theses
known as the “world city hypotheses” concerning the contradictory relations
between production in the era of global management and political determination
of territorial interests (Friedmann 1986).
Saskia
Sassen and others have further elaborated the “global city hypotheses.” Global
cities have acquired new functions beyond acting as centers of international
trade and banking. They have become: (1) concentrated control locations in the
world-economy that use advanced telecommunication facilities, (2) important
centers for finance and specialized producer service firms, (3) coordinators of
state power, (4) sites of innovative post-Fordist forms of industrialization
and production, and (5) markets for the products and innovations produced
(Sassen 2001a, 2000, 1991; Brenner 1998; Yeoh 1999; Hall 1996; Friedmann 1995).
These structural shifts in the functioning of cities have “impacted both the
international economic activity and urban form where major cities concentrate
control over vast resources, while financial and specialized service industries
have restructured the urban social and economic order” (Sassen 1991, pg 4).
During the 1990’s, for example, New York specialized in equity trading, London
in currency trading, and Tokyo in size of bank deposits (Slater 2003).
Beaverstock, Smith and Taylor (1999) use Sassen’s focus on producer services to
classify 55 cities as alpha, beta and/or gamma world cities based on the
presence of accountancy, advertising, banking/finance and law firms. The “Globalization and
World Cities Study Group and Network” at Loughborough University have
developed a website (http://www.lboro.ac.uk/gawc/) that is a valuable resource
for the study of systems of world cities
The most important assertion in the global cities
literature is the idea that the global cities are cooperating with each other
more than the world cities did in earlier periods. The most relevant earlier
period is the Pax Britannica, especially the last decades of the
nineteenth century. If this hypothesis is correct, the division of labor and
institutionalized cooperative linkages between contemporary New York, London
and Tokyo should be greater than were similar linkages between London, Paris,
Berlin and New York in the nineteenth century. Obviously, communications
technologies were not as developed in the nineteenth century, though
intercontinental telegraph cables had already been laid, and Japan was not yet
a core power in the world-system. But the nature and strength of coordination
and cooperation among the world cities of the nineteenth century needs to be
examined in order to support the hypothesis of greater contemporary integration
that the global cities literature assumes.
Another
important hypothesis of the global cities literature is based on Saskia
Sassen’s (1991) observations about class polarization and the casualization of
work within globalizing cities. The research of Gareth Stedman Jones on Irish immigration
into London’s East End in the mid-nineteenth century (Jones n.d.) shows that a
somewhat similar process of “peripheralization of the core” was occurring
during the Pax Britannica. To be sure, much of the research on the
global city system has been based on case studies of particular cities.
Researchers generally seek to identify the processes leading to a specific
city’s emergence and positioning within the larger system (Baum 1997;
Grosfoguel 1995; Todd 1995; Machimura 1992; Kowarick and de Mello 1986). Janet
Abu-Lughod (1999) traces the developmental histories of New York City, Chicago,
and Los Angeles through their upward mobility in the world city system. While
these U.S. metropoles share similar characteristics with other world cities,
they have too many differences in geography, original economic functions,
transportation, and political history to serve as much more than fascinating
cases for comparative analyses of globalization.
City Regions
Another
phenomenon of recent urbanization is the emergence of city-regions, large areas
in which big cities are located rather closely to one-another and intervening
areas are mainly suburbanized. Urban geographers have noted that populations in
the rural areas and small towns of core countries are thinning and people are
concentrating in these city regions (Scott 2001; Simmonds and Hack 2000). The
city region phenomenon is made plain by examining Figure 1, a global map of
city lights at night produced from satellite images.
All the continents have city regions, but the
largest are those found in the eastern half of the United States and the
western portion of Europe, with several other regions also displaying this
phenomenon. These city regions are linked together spatially by overlapping
surburban areas, and it is for that reason that our focus in this research is
on the phenomenon of suburbanization. We will develop a method of spatially
bounding multicentric city-regions that will enable is to quantitatively
compare these with one another in terms of spatial and demographic sizes,
population density, and settlement size distributions. We shall also study
differences in their macrourban structures.
Suburbanization
From the ancient world
until the industrial age most cities had a monumental non-residential center
surrounded by relatively high-density residential districts, with density
largely limited by the technology available for creating structures either well
below or well above ground level. Walled cities enclosed these high-density
residences, but when the cities grew, suburban districts of rather lower
densities formed outside of the old wall. The spatial extent of these cities
was also limited by the fact that most traffic was pedestrian. This
radioconcentric pattern continued to characterize cities in the industrial age
despite the geometric decline of transportation costs produced by the
steam-powered railways. Improved
transportation arose earliest and most quickly in the capitalist core cities
and so their urban form was first affected by these developments, especially
during the second half of the nineteenth century when the central portions of
cities like London, New York, and Paris were rebuilt. This was, however, only an interstitial period between the
earlier pedestrian cities and the modern automobile-driven (literally)
city. New cities, such as Los Angeles,
built during the automobile age from the early twentieth century to the present
have been increasingly characterized by being multicentric and low-density
(Dear 2000; 2002). This also
characterizes many of the new cities in non-core developing countries, which
have also been based on automobile (or at least motorized bus) dependency. And of course the low-density and
multicentric pattern has been added to old concentric-style cities as they have
experienced further growth during the automobile age. This is accentuated by so-called edge cities (Garreau 1991). Once again, these changes are most obvious
in the core capitalist cities, but this urban form has diffused quickly to the
non-core cities as they have been inundated with population increase.
There is a vast
literature on the ecology or spatial form of urban places, but we distinguish
three main types of modern urban macrostructure:
(1) Type A:
concentric-radial cities organized around a central business district with
transportation corridors radiating out from it;
(2) Type B: multicentric
low density cities that are mainly “suburban” with relatively small
non-residential centers dispersed across the built-up landscape (e.g. L.A.);
and
(3) Type C: a mixture of
these two where the older concentric structure has become edged by a newer
multicentric and low-density region.
We posit that, despite
some gentrification processes, city growth in the future will largely occur in
the suburban areas of cities, through the dual processes of suburban
intensification (in-fill of existing suburban areas), and suburban
extensification (urban sprawl).
These are the processes that lead to the creation of the edge cities that
eventually become surrounded by their own suburbs and thus become new urban
nuclei in the sea of suburbanization.
Therefore, if we are to understand the emerging global city system, we
must have a good handle on the way in which urban places are evolving,
especially in terms of suburbanization.
The
Global City System
We
want to study changes in the global city-size distribution because we are
interested in the relationship between cities and power, and because the
apparent flattening of the global city-size distribution discovered in the
1980s raises interesting questions about the upper limits of the sizes of
megacities. Why did the global city-size distribution flatten out after 1950,
modifying a pattern that had existed throughout the British and U.S. hegemonies
in which the most powerful country had the largest city and there was a
hierarchy of city population sizes revealed by the world’s largest cities
(Chase-Dunn 1985)? Roland Fletcher
(personal communication) contends that contemporary institutional and
infrastructural inventions only allow for megacities to function at maximum
populations of around twenty millions and this serves as a kind of ceiling
effect which has allowed cities in the non-core to catch up in terms of
population size with the largest cities in the most powerful states. This may
be what has produced the flat global city-size distribution that emerged after
1950. Fletcher’s notion of an upper limit on the sizes of large contiguous
cities might also be part of the explanation for the emergence of city-regions
rather than gigacities (the logical phase beyond megacities).
In order to study the
global city-size distribution and the phenomenon of city-regions we are
developing new methods of spatially bounding cities and city-regions using
satellite data. Spatially bounding cities has long been problematic because
information is often organized in terms of juridical boundaries. We
hypothesize that the overall rate of urban growth is correlated with economic
growth at the level of the world-system as a whole. We expect to find faster
rates of urban population growth during periods of faster economic growth, and
that urban growth is cross-nationally correlated with economic growth. In
peripheral regions the relationship with economic growth may be reduced because
migration to cities is driven by the redundancy of rural populations. Thus at
the level of the whole world-system we predict that the rate of urban growth
declined after 1980 as the world economy moved into stagnation. In order to
investigate this we will use census data since 1950 on the world largest cities
and data on GDP growth on the countries in which these cities are located.
We also
hypothesize that the rate of suburbanization is related to overall economic
growth and to the level of development in the world economy. We expect slower
and less suburbanization in less developed countries and fewer cities of the
Los Angeles type. Our suppositions here are based on the differences in income
and the affordability of automobile transportation to poor families. We would
expect that suburbanization in semiperipheral urban regions such as Mexico City
is higher density because poor urban residents are unable to purchase their own
vehicles and are unable to purchase large residential lots.
We also
hypothesize that the relative investments in mass transportation compared to
road-building affect the rate of suburbanization. Here we must control for the
age of existing urban infrastructure because it is much more expensive to
rebuild than to build on vacant land. We can estimate the historical age of our
cities, that date at which they reached population sizes of 100,000, and use
this as a control in our analysis of the effects of mass transportation
investment and road building.
We also
suppose that the size and density of city-regions are related to global
differences in the level of development, and that once these features of city
regions are taken into account it will turn out that the global
city-region-size hierarchy is indeed related to economic and political/military
power as it has been in the past. Developing countries have succeeded in
building very large megacities, but their city-regions are not as large and
dense as those in the core. Thus once we get the unit of analysis
right--city-regions rather than single urban agglomerations—we are likely to
find that the old association between power and settlement size continues to
hold in the modern world-system.
Measuring the growth of suburbanization requires
standardized categories of population density that are comparable across cities
in different regions, cultures and levels of national development. We propose to use remotely sensed imagery to
classify land cover according to an urban gradient that distinguishes the mix
of land cover associated with urban, suburban, and rural land uses. We will calibrate these measures to census
and ancillary land-use data from administrative sources in order to create
replicable algorithms that can be used globally to describe and model changes
in urban macrostructures and thus in the global city system. Part of the calibration will involve the
estimation of population size (and thus density) from the satellite imagery. Measurement of the
population sizes of cities is not without difficulties. How can we know the
number of people who reside in Los Angeles today? We use the most recent
census, a survey of “residents”
conducted by the U.S. federal government. What are the spatial 
boundaries of “Los Angeles”? Do we mean the city of Los Angeles, Los
Angeles County, the contiguous built-up area that constitutes “greater Los
Angeles,” or a definition based on the proportion of the local population that
is employed in “Los Angeles”? Does “Los Angeles” include San Diego? Nighttime
satellite photos of city lights imply a single unbroken megalopolis from Santa
Barbara to Tijuana: So where is Los
Angeles? We use the contiguous built-up area as our main way of
spatially defining cities. Urban geographers have made considerable progress on
the task of using satellite data to spatially bound cities (Weber 2001).
Our work has clear
significance for the testing of theories about city-regions and the way in
which they contribute to the overall world-system that has emerged since the
creation of capitalist cities several centuries ago. It also has significance for the increasingly fruitful ways in
which remotely sensed imagery has become a powerful adjunct to the methods used
in the social sciences. Higher spatial
resolution imagery that covers the entire globe with increasing temporal
resolution, combined with emerging methods of quantifying those images in ways
that make sense of the urban scene, have allowed us to think about testing
theories in ways that were previously unimaginable. Thus, the results from this research will contribute to a growing
linkage between methods originated in the physical and natural sciences and
theories developed in the social sciences.
This offers a potentially new outlook as researchers are better able to
delineate the intertwining of the physical and social worlds.
The
Research Team
This
project builds on the NSF-funded work of both of the Co-PIs, and the project is
staffed by a set of researchers who have substantial expertise in all aspects
of the work that we are developing.
Christopher Chase-Dunn is Distinguished Professor of Sociology and
Director of the Institute for Research on World-Systems at the University of
California-Riverside. He received his Ph.D in Sociology from Stanford
University in 1975. Chase-Dunn has done crossnational quantitative studies on
the effects of dependence on foreign investment. His recent research focuses on
intersocietal systems, including both the modern global political economy and
earlier regional world-systems. He is doing research on the causes of empire
expansion and urban growth (and decline) in the Afroeurasian world-system over
the last 4000 years. Chase-Dunn is the founder and co-editor of the electronic Journal of
World-Systems Research and the
Series Editor of two book series on global social change published by the Johns
Hopkins University Press. In 2001 he was elected a Fellow of the
American Association for the Advancement of Science. In 2002 he was elected
President of the Research Committee on Economy and Society (RC02) of the
International Sociological Association.
John
R. Weeks is Professor of Geography and Director of the International Population
Center at San Diego State University (SDSU).
He also holds an appointment as Clinical Professor of Family and
Preventive Medicine at the University of California, San Diego, School of Medicine. As noted above, he is currently the
Principal Investigator on a project funded by the National Science Foundation
(BCS-0095641) to apply remotely sensed imagery and GIS to an analysis of the
Arab Fertility Transition, focusing on Egypt and Jordan. He received his Ph.D. in Demography in 1972
from the University of California, Berkeley, where his mentor was the eminent
sociologist and demographer Kingsley Davis, one of the most important analysts
of world urbanization. Davis’s work also greatly influenced Chase-Dunn research
on city systems. Dr. Weeks will be linked to the project through a contractual
arrangement with San Diego State University.
The project will permit expertise to be gained in these areas of
research by a graduate student and an undergraduate student at the University
of California, Riverside, and by two graduate students at San Diego State
University.
GENERAL
PLAN OF WORK
Our work will focus on
eight tasks that will proceed in tandem, with the results coming together in
March of 2006: (1) quantification of data for the entire population of world
cities, to be used to test theories about city systems; (2) classification of
satellite imagery for five cities for three dates each; (3) estimation of
measurement error due to satellite imagery; (4) linking of census data and, where
available, ancillary land use data with the data from the satellite imagery;
(5) calibration of the census and land use data with the census land cover
classification variables; (6) estimation of “real” change over time and space
in the spatial extent and population density of each of the study sites; (7)
collation of all results in order to test theories about the role of
suburbanization and related urban processes on change occurring in core and
peripheral cities; (8) dissemination of results and establishment of plans for
Phase II of the overall project.
Data for all World
Cities
We have identified 251 cities as the largest
on Earth in 2000. These cities on all continents and in Oceania serve as the
main focus of our overall project. We will also study the ten largest
city-regions, which include: Southern California/Northwestern Mexico,
Midwestern-Eastern United States and adjacent Canada, Central Mexico,
Northwestern South America, the River Plate urban region, Southeastern Brazil,
Southeastern South Africa, Europe-North Africa-Western Asia, South Asia, China
and Japan. The project will build a data set with quantitative measures on 251
cities, all the countries in which these cities are located, ten city-regions,
and variable characteristics of the world-system as a whole. These data sets
will be used to test the hypotheses (above) about the causes of different kinds
of urban growth. This phase of our study focuses mainly on the period between
1984 and 2005, though we will also use census data since 1950 and some remote
sensing data before 1984. The recent time period of 1984 to 2005 is dictated
largely by the availability of suitable satellite imagery. LANDSAT imagery prior to 1982 was at 79-meter
resolution, which we judge to be too coarse for meaningful urban analysis. The
LANDSAT TM, with a higher spatial resolution of 30 meters, went up in 1982, but
the imagery archive does not go back consistently before 1984. Every city
is covered from the mid-1980s to the present, so it will be possible to choose
the best scene for the specific date (looking for imagery taken at the same
time of year, so that seasonal vegetation doesn't confound the analysis). We are also
collaborating with the Urban Environmental Monitoring project (UEM 2000) and
will make use of the ASTER results of that study.
Study Sites
for data from Satellite Imagery
In
order to evaluate the utility of using remotely sensed data for the study of
city-regions, we will focus on five targeted cities: Los Angeles (USA), San
Diego (USA), Cairo (Egypt), Amman (Jordan) and Accra (Ghana). These targeted
cities will be used to develop our mulitiple indicator measurement error model
(see below) for sorting out apparent temporal differences that are due to
changes in remote sensing technology rather than due to real changes in the
cities we are studying. These cities are both similar and different in ways
that will be helpful in building our measurement model. Los Angeles, San Diego,
Cairo and Amman are in semiarid and Mediterranean climates, whereas Accra is in
a tropical climate. Los Angeles and San Diego are recently built low-density,
multicentric cities (Type B), whereas Cairo, Amman and Accra are
concentric/radial older cities with newer suburban fringes (Type C). These are cities for which Dr. Weeks
already has some of the imagery needed for this study. For Los Angeles, Cairo and Accra, we are
leveraging work carried out by Weeks and his associates in two previous
NSF-funded projects--Grant
BCS-0095641 (which was discussed above), and BCS-0117863 (Doctoral Dissertation
Research: Environmental Context of Social Vulnerability to Urban Earthquake
Hazards, funded from 8/1/01 through 7/31/03, which supported the dissertation
research of Dr. Tarek Rashed, now Assistant Professor of Geography at the
University of Oklahoma). Thus, we are
able to leverage nearly $30,000 worth of multi-spectral and panchromatic, high
and medium spatial resolution imagery for this project by focusing on those
three cities. From other sources, Dr.
Weeks already has some of the imagery required for San Diego and Accra. Thus, the selection of these cities is
grounded both on the basis of a good comparative fit, and also on the ability
of the researchers to accomplish more than would otherwise be possible, by
building on work already supported by NSF.
Classification of the Remotely Sensed Imagery
For each of the five cities, we propose to use Landsat TM
imagery for 1984 (the first year for which TM are data for all five cities),
1994, and 2004 (available at about the time of funding of this project). This will provide a consistent set of data
from the same type of platform and sensor.
The principle issue with the TM data is that with a spatial resolution
of 30m the accuracy of the land cover classification in urban areas can be
problematic. We have dealt with this
problem in our current research by using “soft” classification methods, rather
than “hard” methods, as discussed below.
We are also interested, however, in the extent to which we may learn
more about urban places by using higher resolution imagery. Thus, for each of the five cities we propose
to compare the 2004 TM classification results with the results of classifying
ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery,
which has three spectral bands in the visible near-infrared (VNIR) range at a
spectral resolution of 15m.
Additionally, for San Diego, Cairo, and Accra, we will be able to
compare the TM and ASTER results with results from much higher resolution
Ikonos (5m multispectral, available for San Diego), and Quickbird (2.4m
multispectral, available for Cairo and Accra).
These data, which are already in the SDSU archive, obviously refer to
dates prior to 2004 (2000-2002), but that small difference in temporal
resolution should not be problematic for our analysis.
In
order to appreciate the value of remotely sensed imagery for analysis of urban
places, it is crucial to understand exactly what information can be extracted
from such images. The image itself is
composed of a two-dimensional array of pixels from which radiant energy has
been captured for an area on the ground that is equal to the spatial resolution
of the image. The information recorded
for each image depends upon the particular sensor, but the brightness within a
given band is assigned a digital number.
The combination of digital numbers representing relative reflectance
across the different bands of light yields the spectral signature of that
pixel. Particular types of land cover
(e.g, vegetation, soil, water, impervious surface) tend to have unique spectral
signatures. The more bands that a sensor has the more detailed can be the land
cover classification. If there are only
a few bands, it is possible to differentiate vegetation from non-vegetation,
but with more bands it may be possible to differentiate a field of corn from a
field of wheat or, within the urban area it may be possible to differentiate a
tin roof from a tile roof. The typical
tradeoff in imagery is that lower spatial resolution imagery will tend to have
more bands (i.e., higher spectral resolution) than higher spatial resolution
imagery. Our team’s experience working
with imagery for urban places suggests thus far that higher spatial resolution
is more important in characterizing an urban place than is the number of bands
available for analysis (Rashed and Weeks 2003; Rashed et al. 2000; Rashed et al.
2001; Weeks 2003c), and Aplin (2003) has reported similar conclusions. This is
because the built environment is, obviously, configured differently than the
natural environment, and the two most useful ways that we have found of
quantifying urban places from imagery are in terms of (1) the proportional
abundance or composition of fundamental land cover classes; and (2) the
spatial configuration of the pixels identified with each land cover
class.
A
common first task in using the data recorded for each pixel is to determine
what type of land cover is represented by that pixel. Do the data represent vegetation (and perhaps a specific type of
vegetation), or bare soil, water, shade, or an impervious surface (such as the
roofing material of a building or the asphalt or cement of roads)? These surface materials are the basic
building blocks of natural and built environments and each type of land cover is
associated with a particular spectral signature. The higher the spatial
resolution the more accurately a pixel can be classified into basic land cover
types because it is more likely that the pixel will include only one type of
land cover. On the other hand, for
lower resolution images, the more likely it is that the pixel will represent a
mixture of different land covers, forcing a decision about how to appropriately
classify the image.
In
a “hard” classification, each pixel in an image is assigned to one land cover
class, using one of several statistical algorithms to determine the final
choice. However, in all but very
high-resolution imagery, the area represented in a pixel from an urban scene is
likely to be a mix of land covers. For
this reason, we have favored a classification scheme known as spectral mixture
analysis (SMA), which “unmixes” each pixel into its constituent land cover
classes and assigns a percentage of land cover class to each pixel. This is
often called a “soft” classification approach because each pixel is described
not in terms of a single land cover, but rather in terms of the proportional
abundance (fraction) of each land cover class.
The SMA approach assumes that a landscape is formed from continuously
varying proportions of idealized types of land cover with pure spectra, called
endmembers (Adams, Smith, and Gillespie 1993). Endmembers are abstractions of
land cover materials with uniform properties present in the scene. In an urban
environment, these may include impervious surfaces, vegetation types, water
bodies, and bare soils (Ridd, 1995).
Linear
SMA is the process of solving for endmember fractions, assuming that the
spectrum measured for each pixel represents a linear combination of endmember
spectra that corresponds to the physical mixture of surface components weighted
by their areal abundance. However, if a pixel is modeled by fewer endmembers
than required, the unmodeled portion of the pixel spectrum will be partitioned
into the resultant fractions, thus increasing the model error for that pixel
(Roberts et al. 1998). To correct for this problem, we have
successfully tested the applicability of an algorithm utilizing the technique
of multiple endmember spectral mixture analysis (MESMA) to measure the physical
composition of urban morphology from a Landsat Thematic Mapper (TM)
multispectral image (Rashed et al.
2003 (forthcoming)-a). It has been suggested that urban morphology is “the
physical appearance of social reality”(Pesaresi and Bianchin 2001:56). The
potential of MESMA to contribute to urban morphological analysis lies in its
ability to quantify the physical composition of urban areas occasioned by human
activity at different geographic scales.
In
classifying the data by land cover class, we have previously employed Ridd’s
(1995) V-I-S (vegetation, impervious surface, soil) model to guide a spectral
mixture analysis of medium-to-high resolution multi-spectral images for Cairo
for 1986 and 1996, in a manner similar to methods used by Phinn and his
colleagues for Brisbane, Australia (Phinn
et al. 2002), and by Wu and Murray (2003) for Columbus, Ohio. The V-I-S model views the urban scene as
being composed of combinations of three distinct land cover classes. An area that is composed entirely of bare
soil would be characteristic of desert wilderness, whereas an area composed
entirely of vegetation would be dense forest, lawn, or intensive fields of
crops. At the top of the pyramid is
impervious surface, an abundance of which is characteristic of central business
districts, which are conceptualized as the most urban of the built
environments.
We
have added another component to Ridd’s physical model—shade/water—following the
work of Ward, Phinn, and Murray (2000) suggesting that the fourth physical
component improves the model in settings outside of the United States. When combined with impervious surfaces in
urban areas it becomes a measure of the presence of multi-story buildings
(based on the shadows cast by buildings).
When combined with vegetation it provides a measure of the amount of
water in the soil and the shade cast by tall vegetation (largely trees that may
serve as windbreaks in agricultural areas).
In combination with bare soil it is largely a measure of any shadows
cast by trees, although there could be some component of shade from large
buildings in heavy industrial areas.
Once
an image is classified according to land cover types, information from other
sources may be used to make inferences about the way in which the land is being
used, since land use is a socially derived category. From this process, variables may be created that describe the
environmental context of a specific place.
Thus, spatial aggregation of the land cover data for all pixels in an
area (such as a census tract) yields a measure of the area’s land cover composition. To these we add algorithms for quantifying
the spatial configuration of the pixels of specific land cover classes
(known as “patches”) in a given area (such as a census tract) (McGarigal 2002).
These landscape metrics were developed originally for applications in landscape
ecology, but have recently been discovered to have considerable potential value
for describing the urban environment (Herold, Scepan, and Clarke 2002).
The
landscape metric algorithms allow us to produce several indices of the way in
which each land cover class is organized spatially. These include, in particular, shape complexity and
isolation/contiguity of class types, based on concepts of fractal geometry as
applied to geography (see, for example, Lam and De Cola 1993). Work is only now
beginning on the creation of what might be thought of as a “reference library”
of landscape metrics that are consistently related to particular kinds of urban
places. Indeed, the study by Herold,
Scepan and Clarke (2002) is one of the very first of its kind. This is, of course, one of the ways in which
the proposed research is moving into quite literally uncharted territory, but
the potential is very high that these measures will allow us to create much
more meaningful quantifiable indices of the built and natural environments
related to the urbanness. For example, the proportional abundance of
vegetation, as classified from the imagery, tells us whether there is any
vegetation within a given urban area.
The contagion index of landscape metrics can then tell us how clumped
together the patches of vegetation are.
If this index is high, we can expect that the vegetation represents a
true green space -- perhaps a park-- rather than randomly placed plants. The
high-resolution panchromatic imagery will allow us to confirm whether our
interpretation of the landscape metric appears to be correct. As another
example, two neighborhoods may have identical proportional abundances of
impervious surface, reflecting (literally) the roofs of buildings. The landscape metric that measures the
perimeter-area ratio then tells us how those pixels are arranged. If the ratio is very low, then the image has
probably captured a large building (which is probably connected to local
infrastructure), whereas if the ratio is high, the image has captured a number
of different buildings, implying that as the ratio increases, the number of
individual buildings is going up. If
there are a number of small buildings in a neighborhood with little vegetation
and perhaps some bare soil, then we may infer that the neighborhood can be
characterized as a low-rise slum (with small homes that are probably not
connected to infrastructure). If the
ratio is lower and the proportional abundance of vegetation is higher, then we
may infer that the neighborhood is a more prosperous residential area. As the perimeter-area ratio increases with
the same amount of vegetation, we may infer that the size of the buildings is
increasing, perhaps indicating higher-status villas. Once again, these interpretations can be checked by reference to
the high-resolution panchromatic imagery. Members of the research team already
have experience utilizing the Fragstats program (http://www.umass.edu/landeco/research/fragstats/fragstats.html)
working with remotely sensed data for Cairo, Egypt and Amman to establish a
baseline of metrics for use with the data for the five study sites selected for
this research.
Measurement
Error
We will develop multiple indicator measurement
error models of the spatial and demographic features of the world largest
cities and city-regions in order to quantify spatial and population growth and
patterns of suburbanization from 1984 to 2004. For example, Daniel Pasciuti
has developed a measurement error model approach to estimating the population
sizes of preindustrial cities from a large set of proxy indicators (see
https://irows.ucr.edu/research/citemp/estcit/estcit.htm). We will apply this same approach to the
technology change measurement problem. Multiple indicator measurement error
models utilize structural equations modeling to estimate the relationships
among a set of proxy indicators of an underlying (latent) variable characteristic
of a substantial number of cases (Bollen 1989). We will develop these measurement models in order resolve the
problem of measurement error due to changing satellite imagery technology over
the period we wish to study. These
methods, derived from social science research, will then be evaluated in
comparison with more traditional physical science based measures of RS
measurement error.
This problem is closely
analogous to other efforts to study change over time in which earlier, less
complete and more error-prone data are used in conjunction with later, more
complete and more accurate data. The problem is to sort out the real change
from the apparent changes that are due to early measurement error. The
structural equations approach allows us to estimate the sizes of errors in the
earlier estimates and to improve those estimates based on measurement error
models constructed using the later and better instruments. In time-series
analysis it is common to exploit overlaps between different measures to understand
the measurement differences. This is what we will be doing when we build a
measurement error model that estimates the relationships among indicators, and
then uses these estimates to correct earlier estimates for which we do not have
all the information.
Linking RS
data with Census and Other Data
Once
the RS data are classified, the land cover data will be aggregated at the
census block-group level for each city for each date under consideration, landscape
metrics will be calculated, and an urban gradient score will be
calculated. This will build upon the
work already 
underway by Weeks and his associates (see, for example, Weeks 2003c;
Weeks et al. 2003). The idea is to create an urban gradient index
from the RS data and then calibrate that index to the census and other data
available for that census block-group.
The basic idea is illustrated in Figure 3 below. City and urban region
characteristics (area size, population size, urban structural type, population
densities, etc. will be estimated using geocoded census data (ground truth),
high resolution remote sensing imagery, LANDSAT (TM), ASTER imagery and other
imagery. The models that we are able to build using our five target cities will
be rough estimates that will be refined in the second stage of our study when
we turn to the analysis of all 251 largest cities. For most of these we will
have only general estimates based on census data (e.g. total population, total
land area). We will have both ASTER and TM imagery after 2001, and so will be
able to test and improve our model using these cases.
Analyses
As
long ago as 1980, Clayton and Estes (1980), building on the pioneering work by
Tobler (1969), demonstrated the close relationship in the United States between
high resolution remotely sensed imagery and census data. Lo (1995) showed that high-resolution
multispectral SPOT imagery could be used to model population and dwelling unit
estimation in the Hong Kong metropolitan area, although he found that at the
micro level of spatial scale the results were less accurate than for larger
areas. However, improved estimating techniques led subsequently to acceptable
results at the census tract level in Atlanta (Lo, 2003). Sutton and his
associates (Sutton et al. 1997, 2001; Sutton 2003) have shown that night-time
light imagery (see Figure 1 above) is correlated with population density, but
the spatial resolution (one kilometer) of these results is coarser than we
believe is required in order to adequately model the urban processes of
suburbanization. Our method uses higher
resolution imagery and incorporates measures not only of land cover abundance,
but also land cover configuration.
Thus, we have a relatively complex set of RS measures with which to
associate population data at the zonal level (typically the census tract). This
linkage will be accomplished using regression methods, following the lead of
other researchers (see Harvey 2003 for a review), but we will then use
dasymetric mapping techniques (see, for example, Yuan, Smith, and Limp 1997;
and Weeks et al. 2000) and allometric growth modeling techniques (see Longley
and Mesev 2001; and Lo 2003) to fine-tune the estimates to a grid that will be
larger than a single pixel, but substantially less than one kilometer.
We will use structural
equations modeling and time-series analyses to test the causal propositions
delineated above at three levels of analysis: cities, city-regions and the
world-system as a whole. We will also use our geocoded data to build a
Time-mapped GIS for scientific visualization of urban growth processes and for
our educational World Cities web presentation. The TimeMap®
Project (http://www.TimeMap.net) is a temporal geographical information system
(TGIS) that utilizes standardized and interoperable web-enabled datasets to
produce animated maps that show change over time (Johnson 2000).
This
proposed research project is developing new methods for using remotely sensed
data from satellites to study the growth of world cities and city-regions, the
rates of suburbanization and trends in the global size distribution of cities. Whether or not rates of growth and
suburbanization are accelerating, steady, or decreasing, and how these may vary
between core, peripheral and semiperipheral societies in the world-system, will
have important implications for theories of urban growth and for the future of
sustainability.
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