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90 S. Siedentop and S. Fina
Munich, Berlin, Hamburg). Furthermore, we assume that large parts of new housing units in
these regions were developed on infill sites within the urban fabric.
In combination, the picture painted by these three indicators shows that urban sprawl is
more likely to occur in areas where already low densities continue to decrease, and where
new urban area is being utilised inefficiently. This is particularly the case in the north-eastern
parts of the country, but also to some degree in parts of Rhineland-Palatinate, Thuringia,
Saxony, and Bavaria.
4.2. The pattern dimension
The pattern indicators presented here contain three static (effective share of open space,
patch density, mean shape index) and one dynamic measurement (openness index). Indicator
4 shows the effective share of open space, measuring to what degree urban land uses
‘fragment’ undisturbed non-urban environments. In general, the implementation presented
here shows that the habitat quality of open space is much higher in the countryside and
negatively affected in the urbanised areas. Low values appear along the urban agglomerations.
High values dominate in sparsely developed areas (small settlements, low population
numbers). The resulting map correlates to a large degree with the share of urbanised land
(indicator 8, discussed below), but the effective share of open space does not merely produce
reciprocal results: the actual configuration and shape of urban areas does have an impact as
well. The patch density (indicator 5) shows that the dispersal and fragmentation impact
caused by urban areas is highest not necessarily in, but around the main urban centres. It is
clear that large cities have low values because their corresponding patches (= urban areas)
are single entities that exceed the reference area (100 hectares, see for example Hamburg,
Berlin, Munich). At the same time, rural areas with very few settlements have also low
values. 1 The higher values therefore dominate in the suburban and exurban regions around
the main urbanised areas (see the band of patch densities with values higher than 11 running
from southwestern Northrhine-Westphalia to central Baden-Württemberg, in central lower
Saxony, in Saxony, Saarland in the southwest, and also around Berlin and some parts of
Bavaria). The conclusion is that these areas are more difficult to service with regards to
technical and social infrastructure, with a negative impact on (fiscal) resource efficiency. The
mean shape index (indicator 7) reveals how compact (low values) or irregular (high values)
urban forms are. Linear urban forms along road segments (strip development) cause urban
forms to become irregular, and so do historically or topographically preconfigured linear
settlement patterns (for example traditional street villages in Saxony, settlements along rivers
or in valleys like in the Stuttgart region). The most dominant conjoint area of high mean
shape index values, however, is the highly urbanised Rhine-Ruhr region. Here, cities are not
single entities, but seamlessly merged settlements that have grown together over the course
of time. As a result, the urban area polygons are highly irregular. The assessment of these
urban area ‘networks’ in an urban sprawl sense is controversial: from an efficiency point of
view there will be more opportunities for synergies, depending on density. From an
environmental point of view, natural habitats are severely disturbed. The last pattern
indicator – the openness index – reflects on the level of integration of new urban areas
into the urban compound. The corresponding map shows large areas of blank cells. This is
due to the fact that there weren’t any new urban developments in these cells to analyse, or,
more precisely, no newly urbanised areas were classified within these areas in the CORINE
land cover framework. The distribution in the remaining cells on the map shows that the
lowest values can be found in areas with smaller shares of urbanised land. In contrast, new
developments in highly urbanised areas have highest values. This is most likely due to the