空间数据划分是空间索引、并行GIS数据分解以及分布式数据管理与调度等问题的核心环节之一。针对点数据集多目标空间划分问题,引入Hilbert空间填充曲线和空间分布模式探测过程,提出针对规则、随机和聚集分布模式的点数据集空间划分方法。实验结果表明,该方法能够在缺少覆盖范围信息的条件下准确判定空间分布类型,该方法能够兼顾空间聚集性、数据量均衡与空间重叠度3种约束条件。

Optimal partitioning of spatial dataset is an import concern for many data management context such as spatial indexing, parallelizing GIS and distributed data access.This paper aims to discuss an adaptive spatial data partitioning method based on Hilbert space-filling curve and spatial pattern detection.A spatial data partitioning approach is proposed for spatial data partitioning of regular, random, and clustering objects, respectively.We show that the proposed method can achieve optimal partitioning point dataset without point dataset boundary information.We also show that the proposed method can adapt to available information about data distributions, data sizes and clustering.