That is to say, for very large spatial databases, QR-tree processes more superiority than R-tree.
也就是说，对于大型空间数据库索引来说，QR -树与R -树相比整体性能上具有较明显的优势。
This paper classify a series of methods on spatial database index, and introduce the QR-tree index structure with very large spatial database.
在综述现有空间数据库索引技术的基础上，提出了一种面向大型空间数据库的QR-树索引方法。
QR-tree, a spatial index structure based on R-tree and a kind of space partition method using Quad-tree, is proposed. Its data structure and algorithms are also stated.
提出了一种基于R-树与“四叉树”空间层次划分的空间索引结构QR-树，给出了其数据结构和算法描述。
QR-Tree index combines the advantage of Quadtree and R-Tree, not only serves the need of high storing efficiency, but also avoid of many invalidation query, in order to achieve good query capability.
树索引结合四叉树和R-树的各自优点，既可以满足较高的存储效率，又避免太多的无效查找，达到较好的查找性能。
QR-Tree index combines the advantage of Quadtree and R-Tree, not only serves the need of high storing efficiency, but also avoid of many invalidation query, in order to achieve good query capability.
树索引结合四叉树和R-树的各自优点，既可以满足较高的存储效率，又避免太多的无效查找，达到较好的查找性能。
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