Constrained tGAP for generalisation between scales: the case of Dutch topographic data

P. van Oosterom (Editor), Arta Dilo, Peter van Oosterom, Arjen Hofman

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    Abstract

    This article presents the results of integrating large- and medium-scale data into a unified data structure. This structure can be used as a single non-redundant representation for the input data, which can be queried at any arbitrary scale between the source scales. The solution is based on the constrained topological Generalized Area Partition (tGAP), which stores the results of a generalization process applied to the large-scale dataset, and is controlled by the objects of the medium-scale dataset, which act as constraints on the large-scale objects. The result contains the accurate geometry of the large-scale objects enriched with the generalization knowledge of the medium-scale data, stored as references in the constraint tGAP structure. The advantage of this constrained approach over the original tGAP is the higher quality of the aggregated maps. The idea was implemented with real topographic datasets from The Netherlands for the large- (1:1000) and medium-scale (1:10,000) data. The approach is expected to be equally valid for any categorical map and for other scales as well.
    Original languageUndefined
    Pages (from-to)388-402
    Number of pages15
    JournalComputers, environment and urban systems
    Volume33
    Issue number5
    DOIs
    Publication statusPublished - Sep 2009

    Keywords

    • EWI-17623
    • CR-E.2
    • Topographic data
    • Spatial data structures
    • IR-70980
    • Constrained tGAP
    • Automatic map generalization
    • Generalization
    • METIS-266494
    • Object matching

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