CollaGen: Collaboration between automatic cartographic Generalisation Processes - Advances in Cartography and GIScience - page 545

Geographic Spaces
We define a geographic space as a geographically meaningful extract of
the data that can be a relevant input for a given generalisation process
(Touya 2010). The use of geographic spaces in CollaGen is useful for both
optimising the use of the existing generalisation processes and partitioning
the data to avoid the processing of very large datasets. The geographic
spaces ( Figure 2 ) can be areal (e.g. urban or rural area), thematic (e.g. road
network or vegetation) or both areal and thematic (e.g. mountain roads).
Figure 2: (a) buildings in black, urban areas in red and rurban areas in blue. (b) vegetation
thematic space on the same area. (c) mountains roads space (in the rectangle).
It can be noticed that with such a definition, the geographic spaces do not
form a mathematical partition as metric spaces can overlap and thematic
spaces cross metric spaces. Spaces can be cut in several portions in order
to keep small spaces and minimise processing time.
Moreover, some emerging spaces can be managed by CollaGen: they are
sub-spaces where conflicts remain unsolved. During the generalisation of a
space by a given process, the observation component can identify conflict
clusters (close conflicting objects) that emerge as sub-spaces to be general-
ised by another process than the one processing the whole space (§0).
Formalised Knowledge in CollaGen
Formalised cartographic generalisation knowledge is necessary to allow
process collaboration. The model designer (e.g. we are the CollaGen
model designer) has to provide a generalisation ontology and sequencing
rules ; a process developer (the one that makes a new generalisation
process available for collaboration) has to provide a process description ;
the user (the one that generalises data) has to provide generalisation con-
straints and operation rules ( Figure 3 ).