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17624 Constrained set-up of the tGAP structure for progressive vector data transfer
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Haunert, J. H. and Dilo, A. and van Oosterom, P. (2009) Constrained set-up of the tGAP structure for progressive vector data transfer. Computers & Geosciences, 35 (11). pp. 2191-2203. ISSN 0098-3004 *** ISI Impact 2,474 ***

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A promising approach to submit a vector map from a server to a mobile client is to send a coarse representation first, which then is incrementally refined. We consider the problem of defining a sequence of such increments for areas of different land-cover classes in a planar partition. In order to submit well-generalised datasets, we propose a method of two stages: First, we create a generalised representation from a detailed dataset, using an optimisation approach that satisfies certain cartographic constraints. Second, we define a sequence of basic merge and simplification operations that transforms the most detailed dataset gradually into the generalised dataset. The obtained sequence of gradual transformations is stored without geometrical redundancy in a structure that builds up on the previously developed tGAP (topological Generalised Area Partitioning) structure. This structure and the algorithm for intermediate levels of detail (LoD) have been implemented in an object-relational database and tested for land-cover data from the official German topographic dataset ATKIS at scale 1:50 000 to the target scale 1:250 000. Results of these tests allow us to conclude that the data at lowest LoD and at intermediate LoDs is well generalised. Applying specialised heuristics the applied optimisation method copes with large datasets; the tGAP structure allows users to efficiently query and retrieve a dataset at a specified LoD. Data are sent progressively from the server to the client: First a coarse representation is sent, which is refined until the requested LoD is reached.

Item Type:Article
Research Group:EWI-PS: Pervasive Systems
Uncontrolled Keywords:spatial data structures, progressive data transfer
ID Code:17624
Deposited On:19 April 2010
ISI Impact Factor:2,474
More Information:statisticsmetis

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