Geoscience Reference
In-Depth Information
Haunert 2007) are a small sample of the available cartographic generalisa-
tion processes.
If so many processes have been developed over the years, it is due to the
impossibility to solve the complex problem of generalisation with a single
process. Indeed, every process is only completely relevant for a limited
part of the generalisation problem. Some processes are well adapted to par-
ticular
landscapes
: AGENT (Ruas 1999, Barrault et al. 2001) is designed
for urban generalisation while GAEL (Gaffuri 2007) may be specialised to
deal with high relief landscapes. Moreover, some are only relevant for the
generalisation of a specific data
theme
: (Haunert 2007) is dedicated to land
use generalisation for instance. Added to that, some are relevant for solv-
ing a limited part of the cartographic
conflicts
resulting from scale change:
for instance, the simulated annealing process of (Ware et al. 2003) is de-
signed for solving proximity conflicts. Finally, some mix the three previ-
ous cases: the Elastic Beams (Bader et al. 2005) are relevant for road overlap
conflicts (
theme
and
conflict
).
Either automatically producing map series or on-demand mapping requires
to be able to generalise any landscape, data theme or solve any necessary
kind of conflict, which is not possible using a single existing process.
Rather than developing a new complete generalisation process, which
seems a bit rash, the objectives of our research is to benefit from the exist-
ing processes and make them work together. We propose a new frame-
work,
Collaborative Generalisation
(CG), to make processes collaborate
to correctly generalise a entire map.
The second part of the paper describes the CG approach and the CollaGen
model. The third part focuses on the results obtained with CollaGen. The
fourth part draws some conclusions and proposes future plans.
2- Approach and Methods
2.1 The Collaborative Generalisation Framework
Automatic generalisation research first tried to answer to the questions
“why, when and how to generalise?” (McMaster and Shea 1988, Brassel
