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基于 4-叉树结构的路网数据最近邻查询算法

Algorithm of the Nearest Neighbor Query for Road Network Data Based on Quad-tree Structure

  • 摘要: 针对路网数据存储数据量较大、常规查询算法效率较低的问题,将存储技术与查询算法相结合,提出利用4-叉树结构对路网数据进行均匀划分的最近邻查询算法。首先根据兴趣点使用Voronoi图将空间划分为多个相邻空间单元,利用空间均分法对整个空间区域分区,使每个分区包含若干个空间单元;再使用4-叉树结构创建内存索引数据,降低最近邻查询的数据范围;最后采用OSM(open street map)官网的路网数据进行分区查询实验验证。结果表明,与传统迭代切分法和折半分割法相比,建立在结构化分区上的最近邻查询算法可大大提高路网数据的查询效率。

     

    Abstract: Aiming at the problem that the amount of data stored in road network is large and the efficiency of conventional query algorithm is low, combining storage technology and query algorithm, a nearest neighbor query algorithm was proposed to divide road network data uniformly by using quad-tree structure. Firstly, according to the points of interest, the space was divided into several adjacent spatial units by Voronoi diagram, and the whole spatial region was partitioned by means of spatial mean partition method, so that each partition contained several spatial units. The quad-tree structure was then used to create in-memory index data to reduce the range of data in the nearest neighbor query. Finally, the road network data of OSM (open street map) official website was used to verify the partition query. The results show that compared with the traditional iterative division between points method and half split method, the nearest-neighbor query algorithm based on structured partition can greatly improve the query efficiency of road network data.

     

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