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

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 1:10000 scale demonstrates that the extended GAP-trees are efficient, compact, and easy to implement.