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With these three dimensions (and indicators representing them) the spatiotemporal
characteristics of urban sprawl can be measured suf ciently. Following this logic, we argue
that the degree of urban sprawl in a given study area increases
when more urban land is present in that area,
when the urban land-use patches are more dispersed, and
when the urban density of an area is lower.
The change of land-use composition can be regarded as a first key dimension of urban
sprawl. Urban growth usually brings large-scale conversion processes of nonurban to
urban land uses with it, accompanied by increasing percentages of artificial, impervious
surfaces. Imperviousness has been linked to several environmental degradation phenomena
such as the urban heat island effect, poor air quality, increased stormwater runoff, and surface
water pollution (Alberti, 2005; Arnold and Gibbons, 1996; Moglen and Kim, 2007). The core
areas of metropolitan regions are especially affected by high levels of imperviousness.
A second dimension of sprawl refers to urban land-use patterns and change. According
to this dimension, urban sprawl describes the transition of a compact urban form into a
dispersed and fragmented land-use pattern (Jaeger et al, 2009). A typical feature of sprawl
is an irregular, discontinuous urban form with a highly fragmented mosaic of different land
uses. Researchers claim that changes in land-use patterns are responsible for an ef ciency
reduction of urban services such as road infrastructure or sewer systems (Burchell et al, 1998;
Doubek and Zanetti, 1999). There is also evidence that spatially dispersed urban functions
contribute to larger travel distances (Cervero and Murakami, 2010; Naess, 2007). In addition,
the fragmentation of natural habitats through a process of perforation, dissection, and isolation
of natural or seminatural ecosystems is reported to be an effective predictor of species loss
and pressure on biodiversity (Jaeger, 2000; Theobald et al, 1997).
Finally, urban sprawl is usually characterised by declining urban densities (Angel, 2011).
On the city level, decreasing values and attening gradients are an outcome of low-density
developments at the urban fringe. Density losses within the urbanised area are an effect of
household dynamics and rising af uence, in shrinking areas also of demographic transformation
processes. Numerous studies provide evidence that low densities reduce the economic
ef ciency of urban services and increase demand for motorised transportation (Burchell and
Mukherji, 2003; Burchell et al, 2002; Ewing et al, 2008; Newman and Kenworthy, 2006;
Speir and Stephenson, 2002). Some research suggests that people living in low-density areas
are likely to walk less and are more likely to suffer from obesity and obesity-related chronic
diseases than people who live in less sprawling areas (Ewing et al, 2003). At the same time,
it is also fair to say that some of these ndings may not be as clearcut as single study results
suggest [see, for example, the replique on Ewing et al (2003) in Eid et al (2007)].
On the basis of this conceptual framework, we selected eight indicators that represent the
three dimensions of urban sprawl [composition, pattern, density (see table 1 and the online
appendix, http://www.dx.doi.org/10.1068/a4580). The rationale for the selection of indicators
was to meet the following three criteria, in order of priority. First, we want to cover each
dimension of urban sprawl with at least one static (expressing the situation at a certain point
in time) and one dynamic indicator (measuring the development between two points in time).
Unfortunately, due to data limitations, the change in density could not be measured. Second,
this contribution intends to include indicators that are well documented and of widespread
use in quantitative research on urban development (for example, share and growth of urban
areas, urban density, soil sealing). Finally, we aim to test and implement a range of possible
indicators for the pattern dimension of urban sprawl. This eld of indicator development
is relatively new and relies heavily on spatial analysis methods that capture aspects like
fragmentation of open space or dispersion of urban areas. It requires detailed geodata that
have only recently become available—at least on a countrywide scale.