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Journal of Land Use Science 87
information value simply mirrored other indicator values (for example ‘open space per
capita’ compared to ‘share of urbanised land’). One measure (‘effective mesh size’) did not
fit in with the grid approach – attempts to transform the values to cells were not satisfactory.
Two recently developed indicators (‘urban permeation’, ‘sprawl per capita’, Jaeger and
Bertiller 2006, Jaeger et al. 2008) could not be implemented due to some uncertainties
around implementation and data use.
3.4.3. Indicator mix
Initially, we were aiming at an equal amount of static and process indicators for each
dimension. However, in the progress of this study it became clear that a balanced coverage
does not necessarily require exactly the same amount of indicators. As Table 3 shows, the
density dimension consists of one static and two dynamic indicators, the pattern dimension
has two static and dynamic measures each and the surface dimension includes one static and
one dynamic indicator. All in all, this mix allows for a good representation of the respective
dimensions. At the same time, we do not see the current indicator mix as an irrevocable
solution. It should rather be seen as the starting point for an evolving framework, with the
goal to include the most comprehensive indicator set available at this point in time. With new
datasets becoming available and new measures becoming more robust and accepted in the
future, the selection presented here will certainly be re-evaluated.
3.4.4. Description
The first two indicators we implemented cover urban density in people per hectare (2004)
and the change in urban density between 1996 and 2004. Although we consider urban
density to be an important input into urban sprawl evaluation, this indicator is not suitable to
identify where and in what form urban sprawl actually occurs. On this scale, indicators 1 and
2 mainly reflect the degree of urbanisation and the changes that occurred within the observed
timeframe. It is a composite measure of urban land use change and population development,
resulting in a density variable that is useful for efficiency assessments of urban systems.
Because one cannot tell what causes changes in urban density – it may be a result of
population decline or large amounts of new urban area being developed, or both – it is
important to include a measure that is suitable to act as a control variable. For this purpose we
employed the ‘greenfield development rate’ which gives the ratio between the number of
new dwellings (‘Baufertigstellungen: Wohnungen insgesamt’ = completed dwellings) in an
area and the amount of new residential area (‘Gebäude- und Freifläche’ = urban area for
residential use, exclusive of infrastructure, parks etc.). Although there is no cross-reference
between the two statistical elements, that is, the new dwellings are not necessarily located in
greenfield areas, the results are indicative for the intensity of residential land use, thus
illustrating the spatial variance of the demand and supply factors for urban development,
expressed by dwelling densities. In combination with the total figure for urban density, the
greenfield development rate complements the density dimenion we aim to cover.
For the pattern dimension, we selected three static and one dynamic indictor. The
effective share of open space (indicator 4) provides a refined tool to measure ‘undisturbed’
open space. Conceptually, it looks at the amount of open space within an area, where larger
patches score higher values in a resulting index called ‘effective share of open space’. It
gives effect to ecologic considerations that larger habitats are important elements of conservation
strategies. This is of specific value for monitoring biodiversity because it reveals
ecologically valuable qualitative aspects of open space in an area (see Ackermann and