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Extensions of GAP-tree and its implementation based on a non-topological data model

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Abstract:

This paper discusses extensions of GAP-trees from three aspects and its implementation based on non-topological structure in order to enhance access to large vector data sets. First of all, we apply cartographic generalization rules to build a generalization procedure of the GAP-tree, which makes coarse representations more consistent with human cognition. Second, we replace the three-dimensional (pseudo-) Reactive-tree index with a 2D R-tree index and a B-tree index to improve the system efficiency. Finally, we compress a binary GAP-tree into multi-way GAP-trees in order to reduce data redundancy. The shallower multi-way GAP-trees not only eliminate redundant data but also accelerate the system's response time. The extensions have been successfully implemented in PostgreSQL. A test of Beijing's land-use data at the 1:10 000 scale demonstrates that the extended GAP-trees are efficient, compact, and easy to implement.

Keywords: GAP-trees; Large vector data set; Multi-way; Non-topological; Rules-based

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/13658810701602120

Affiliations: 1: LREIS, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, PR China 2: LREIS, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, PR China,College of Information and Electrical Engineering of China Agriculture University, Beijing 100083, PR China 3: LREIS, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, PR China,School of Geosciences and Environment Engineering, Central South University, Changsha 410083, PR China

Publication date: January 1, 2008

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