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Drivers of Urban Sprawl; Lesson 3

Sprawled City
Lesson 3
Drivers of Urban Sprawl
Drivers of Urban Sprawl
A variety of factors drive the proliferation of sprawled areas. Although a few studies link sprawl to only
one major driver, for example 'car-based living' or the shift from public transport to highway
construction, empirical results underline a multifaceted process. The following categories can be used to
classify drivers of urban sprawl: demographic, socio-economic, political, technological and geophysical. A
distinction of pull and push factors is possible, that is attracting and promoting forces.
Demographic Drivers
The Size and Structure of Population
The size and structure of the population have been
shown to affect the extent of built-up areas. Obviously,
all else being equal, the larger the population, the more
space will be required to accommodate all the people.
Kasanko et al. (2006) have provided an overview of
urban sprawl in 15 European cities between the 1950s
and 1990s. They found that, although the annual
growth rate in the built-up areas of these cities had
declined to 0.75 % per year by the end of the 1990s,
population growth was a major driver throughout the
time of urban expansion. However, the effect of
population growth is expected to be small in Europe in
the future, because the European population is
predicted to decline until the end of the century.
Demographic Transictions
Urban Sprawl Causing Migration
Substantial Migration Among European Regions has also
Contributed to Urban Sprawl
Urban Sprawl Leading to Migration
Urban sprawl has continued to increase, even in many
regions in which population decline is already apparent.
Population shrinkage is often related to the perforation of
compact cities, through the demolition of buildings, while
land consumption and the extension of built-up areas outside
city centers are ongoing. The degradation of city centers can
also contribute to inhabitants searching for more desirable
areas. This is particularly relevant for the cities of postcommunist countries, which have unattractive city centers.
Therefore, the renovation of city centers could be an
important contribution to stopping sprawl in these cases.
Causes of Migration
Migration, however, does not apply to only the
economically disadvantaged. For example, elderly
people tend to migrate to regions that offer climatic,
cultural and social amenities. The strong relationship
between people's age and the number of single
households and housing areas suggests that regions
with a higher proportion of elderly people are more
likely to be sprawled, particularly in coastal areas.
European societies are ageing continuously because of
improved health which contributes, to some degree, to
urban sprawl in regions attractive to elderly people.
Socio-economic Drivers
Gross domestic product per capita (GDP) reflects the output per inhabitant in a
given region. Because of the social representation of desirable lifestyles, reinforced
by advertisements that promote increased consumption levels, a higher income is
often related to buying a detached house in the urban periphery rather than an
apartment in the core of the city; accordingly, a higher GDP has been shown to
stimulate urban sprawl in individual countries) on the European continent and
worldwide. However, a higher GDP could also be a result of urban sprawl.
Another important socio-economic factor is lifestyle. In recent decades, a
change in social structure, in terms of higher incomes and liberalization
with regard to the increasing concern about urban sprawl in Europe Urban
sprawl in Europe 33 roles of men and women, has promoted a preference
for individual life fulfilment; this has contributed to the decline in
household size and the increase in the number of households. The
observed correlation between lower household size and the higher
demand for new residential areas confirms the relationship between
built-up areas and the number of people deciding to live alone.
Political Drivers
Politics has the power to establish legislation that can promote
sustainability and prevent urban sprawl. Planning systems, legislative
stipulations, subsidies and taxes play an important role in driving or
moderating urban sprawl. The German government, for example,
subsidizes urban sprawl by offering tax relief on 50 % of investments
made in new houses. The costs of acquiring ground, building houses
and schools, and developing the infrastructure and public transport
services for new residential areas are usually shared among all
citizens; however, sometimes, only a wealthy minority benefit from
living in these newly developed areas. Subsidies for commuting and
the acquisition of automobiles also promote urban sprawl. Stipulations,
on the other hand, can control the creation of built-up areas and can
promote an increase in the density of a given built-up area. For
example, Cheshire and Sheppard (2002) showed that the urban area
of the town of Reading, south-east England, would increase by 26 % if
existing restrictions related to the green belt were softened
Political decision of Leaders
Technological Drivers
There were tremendous technological
developments in the 20th century.
Before this age of industrialization,
places of work and living, in cities or
villages, were close to each other. The
availability of automobiles diminished
the importance of living in the proximity
of places of work and, together with
high costs of living in central urban
areas, was a strong driver of a more
dispersed urban form. dispersion of
dwellings in the landscape and a further
increase in urban sprawl.
As technological development continues,
further innovations in communication
technologies and automatisation are likely
to render working from home more
feasible. This technological change may
reduce the need for commuting, which
could result in an even higher the demand
for labor by large factories imposed the
migration of people from rural to urban
areas because the lack of motorized
transport forced employees to live in
residential areas close to such factories.
The availability of automobiles
diminished the importance of
living in the proximity of
places of work and, together
with high costs of living in
central urban areas, was a
strong driver of a more
dispersed urban form. This
technological change may
reduce the need for
commuting , which could
result in higher dispersion of
dwellings in the landscape.
Geophysical Drivers
Several geophysical components have been shown
to affect the development of built-up areas. The
topography and the presence of irreclaimable
areas, that is areas that are unsuitable for
construction (e.g. glaciers, lakes, etc.), limit the
availability of space for built-up areas and,
therefore, reduce the possibility of urban sprawl.
However, some studies have produced conflicting
results and suggest that the differences are mainly
due to different types of irreclaimable area.
Urban Sprawl In Europe: Objectives and Main Results
The Increasing Concern About Urban Sprawl
in Europe
Although the significance of the urgent challenge of urban sprawl has been recognized
at the European level, the monitoring of urban sprawl in Europe has still not been
established. Urban sprawl, as measured by WUP, also increased in most European
countries by more than 1 % per year between 2006 and 2009, and in many countries
by even more than 2 % per year. This is also the case for mostNUTS-2 regions. The
analysis of sprawl at the 1-km2-grid level showed that sprawl is most pronounced in
wide rings around city centers, along large transport corridors and along many
coastlines (particularly in the Mediterranean countries). Future studies of additional
time-points will allow more detailed temporal comparisons of sprawl.
The Increasing Concern about Urban
Sprawl in Europe
There is an increasing need for, and interest in, the inclusion of indicators of
urban sprawl in systems for monitoring sustainable development, the state of
the environment, biodiversity and landscape quality. The results presented in
this report are intended to be Urban sprawl in Europe used for this purpose,
and they can be updated on a regular basis. The results demonstrate that there
is an urgent need for action. Urban sprawl has serious long-term consequences
and more efforts are needed in order to protect forests, agricultural soils and
other open spaces from urban sprawl. Urban sprawl has never been so high in
Europe as it is today. It presents a fast-growing problem that is now out of
control in many parts of Europe. Therefore, this report also discusses how the
WUP method can be used as a tool for urban sprawl analysis as part of urban
and regional planning, and for performance review. In addition, the report
identifies the most immediate priorities and future research needs.
Methods of Measuring Urban Sprawl
Group 1: Use of many variables in parallel
Group 2: Integration of many variables
Group 3: Measures based on one or a few variables
Methods for Measuring Urban Sprawl
The many aspects of urban sprawl, and the diversity of the definitions of urban
sprawl, can pose a challenge to its quantification. Accordingly, previous studies
have deployed a variety of approaches, which relate to differing interpretations
of urban sprawl. Since there is no general agreement regarding the definition of
urban sprawl, there is no general agreement on how to quantify it. One way of
classifying the existing methods is to group them in terms of complexity.
Group 1: Use of Many Variables in Parallel
Some authors consider many variables together in order to capture urban sprawl. Applied
eight spatial dimensions (density, continuity, concentration, clustering, centrality, nuclearity,
mixed uses and proximity) to describe urban sprawl in 13 large cities in the USA. Similarly,
Solon (2009) studied urban sprawl in the Warsaw metropolitan region using seven landscape
metrics (spatial share, mean patch size, patch size coefficient of variance, mean shape index,
mean nearest neighbor distance, mean proximity index, and interspersion and juxtaposition
index). Several authors used variables related to spatial analysis, but their relationship to
sprawl is not always clear and may, in fact, be questionable. For example, Tsai (2005)
compared sprawled and compact cities using information about population, metropolitan
density, the degree of equal distribution and the degree of clustering, and added
measurements of statistical dispersion (using the Gini coefficient) and spatial relatedness.
Hasse and Lathrop (2003) considered five indicators (density of new urbanization, loss of
prime farmland, loss of natural wetlands, loss of core forest habitat and an increase of
impervious surface) to measure urban sprawl in New Jersey, USA. Torrens (2008) identified
11 characteristics of sprawl and used no less than 42 metrics related to seven of these
characteristics (urban growth, density, social factors, land-use factors, diversity,
fragmentation, decentralization and accessibility) in his study on Austin, Texas, in the USA.
Group 2: Integration of Many Variables
Several authors went a step further and developed an approach that includes
many indices and groups them into fewer variables, or dimensions, by applying
some statistical models. For example, in their study of urban sprawl in Israel,
Frenkel and Ashkenazi (2008) used 13 sub-indices and summarized them under
two characteristics. They measured each sub-index separately for each settlement
unit and combined the values of the 13 sub-indices into an integrated sprawl index
by using a weighting scheme. All data, that is the sprawl index and the 13 subindices, were then used to describe the differences among the settlement units in
terms of sprawl. Gathered information on nine indicators that were intended to
describe density, pattern and surface characteristics of built-up areas in Germany.
These indicators were then further processed through a statistical analysis to
derive information about urban sprawl. These more complicated methods have
the advantage that they integrate the information from the sub-indices. However,
they also have the disadvantage that the additional steps, particularly the use of
statistical models, often make the relationship of each sub-indicator to the overall
sprawl index less transparent and the relationship to any particular definition of
urban sprawl difficult to establish. For example, the determination of principal
components depends on the particular set of landscapes used as input data for the
statistical analysis, and the factor loadings may differ between different input data
sets, which makes direct comparisons of the integrative measures impossible.
Group 3: Measurements Based on One or a Few Variables
A third group of studies tried to simplify the measurement of urban sprawl by using a
single measure or an index based on only a few variables. For example, Yue et al. (2013)
used a sprawl index based entirely on population measures in high- and low-density
growth areas and their relationship to the total population to analyze urban sprawl in
Hangzhou, China. Arribas-Bel et al. (2011) assessed urban sprawl in 209 larger urban zones
(LUZs), now known as functional urban areas (FUAs), in Europe using information about
connectivity, decentralization, density, scattering, the availability of open space and the
land-use matrix, which were integrated into a self-organizing map algorithm. Similarly,
Ewing et al. (2003) applied the Rutgers-Cornell sprawl indicator to measure sprawl in 83
metropolitan areas in the USA. This indicator consists of information about residential
density, plot size, land-use mix, the degree of centering, and street accessibility. In
contrast, Horner (2004) studied urban structure in terms of accessibility in a sample of US
metropolitan areas. He found that residential accessibility patterns are concentric if the
central urban area is the most attractive part of a region, while employment accessibility
tends to be more decentralized, which facilitates decentralized suburban growth.
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