Geoscience Reference
In-Depth Information
generalisation process is implemented with ArcGIS tools that have been
combined and customized utilizing the ModelBuilder environment and the
Python programming language. The result is a
tool that establishes a fully
automated generalisation environment which produces the final datasets.
No user interaction is required except from the identification of the data-
sets.
3.5 Results
The above rules and methods have been successfully applied to a subset of
the ERM dataset that covers the region of Germany.
Figure 8
presents a
comparison of the generalised EGM Built-up areas (polygons) to the exist-
ing EGM Built-up areas (polygons). A comparison of the new EGM Built-
up areas (points) to the existing EGM Built-up areas (points) can be found
in
Figure 9
. In terms of objects the compliance is 100% for Administrative
Units, 100% for Built-up Areas (polygons) and 93% for Built-up Areas
(points). This difference is due to the initial ERM dataset. A considerable
number of Built-up Areas in the point and the polygon dataset have “no
data” population values and thus cannot be selected in order to be present
to the new EGM dataset. Differences in the geometry are expected since
the EGM dataset has not been produced by the generalisation of the ERM
dataset. It has been independently created by the NMCAs utilizing small
scale datasets.
In the future, the rules will be applied to a multi-national dataset, in order
to adjust any parameters used, and eventually to a pan-European dataset.
All the rules stated in the framework of ESDIN project covering all
INSPIRE Annex I themes will be later incorporated in the above GIS tool.
As a result a powerful platform for the automated production of a small
scale pan-European spatial dataset from a medium scale one based on
generalisation will be available to NMCAs and Eurogeographics.
