GIS spatial data partitioning method for distributed data processing

Yan Zhou; Qing Zhu; Yeting Zhang

doi:10.1117/12.739790

14 November 2007 GIS spatial data partitioning method for distributed data processing

Yan Zhou, Qing Zhu, Yeting Zhang

Proceedings Volume 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications; 679008 (2007) https://doi.org/10.1117/12.739790
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

Spatial data partitioning strategy plays an important role in GIS spatial data distributed storage and processing, its key problem is how to partition spatial data to distributed nodes in network environment. Existing main spatial data partitioning methods doesn't consider spatial locality and unstructured variable length characteristics of spatial data, these methods simply partition spatial data based on one or more attributes value that could result in storage capacity imbalance between distributed processing nodes. Aiming at these, we point out the two basic principles that spatial data partitioning should meet to in this paper. We propose a new spatial data partitioning method based on hierarchical decomposition method of low order Hilbert space-filling curve, which could avoid excessively intensive space partitioning by hierarchically decomposing subspaces. The proposed method uses Hilbert curve to impose a linear ordering on the multidimensional spatial objects, and partition the spatial objects according to this ordering. Experimental results show the proposed spatial data partitioning method not only achieves better storage load balance between distributed nodes, but also keeps well spatial locality of data objects after partitioning.

Citation Download Citation

Yan Zhou, Qing Zhu, and Yeting Zhang "GIS spatial data partitioning method for distributed data processing", Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 679008 (14 November 2007); https://doi.org/10.1117/12.739790

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