Experiments to Distribute and Parallelize Map Generalization Processes

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 less effective when 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.

Keywords:

• generalization
• partitioning
• parallelization

ORCID

Guillaume Touya http://orcid.org/0000-0001-6113-6903

Additional information

Notes on contributors

Guillaume Touya

Guillaume Touya is a senior researcher at the LASTIG, IGN France (the French mapping agency), and head of the COGIT research team. He holds a PhD and habilitation in GI science from Paris-Est University. His research interests focus on automated cartography, map generalization and volunteered geographic information. He currently leads the MapMuxing (https://mapmuxing.ign.fr) research project on mixing cartography and human-computer interaction issues.