Journal cover Journal topic
Proceedings of the ICA
Journal topic
Volume 1
Proc. Int. Cartogr. Assoc., 1, 8, 2018
https://doi.org/10.5194/ica-proc-1-8-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Proc. Int. Cartogr. Assoc., 1, 8, 2018
https://doi.org/10.5194/ica-proc-1-8-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  16 May 2018

16 May 2018

Experiments to Distribute Map Generalization Processes

Justin Berli1, Guillaume Touya1, Imran Lokhat1, and Nicolas Regnauld2 Justin Berli et al.
  • 1Univ. Paris-Est, LASTIG COGIT, IGN, ENSG, F-94160 Saint-Mande, France
  • 21Spatial, Cambridge, United Kingdom

Keywords: Generalization, Partitioning, Parallelization

Abstract. Automatic map generalization requires the use of computationally intensive processes often unable to deal with large datasets. Distributing the generalization process is the only way to make them scalable and usable in practice. But map generalization is a highly contextual process, and the surroundings of a generalized map feature needs to be known to generalize the feature, which is a problem as distribution might partition the dataset and parallelize the processing of each part. This paper proposes experiments to evaluate the past propositions to distribute map generalization, and to identify the main remaining issues. The past propositions to distribute map generalization are first discussed, and then the experiment hypotheses and apparatus are described. The experiments confirmed that regular partitioning was the quickest strategy, but also the less effective in taking context into account. The geographical partitioning, though less effective for now, is quite promising regarding the quality of the results as it better integrates the geographical context.

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