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

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.