An improved partitioning method for dissolving long and narrow patches. (May 2020)

• Record Type: Journal Article
• Authors: Li, Chengming; Wu, Pengda; Yin, Yong; Wu, Wei
• Is Part Of: Computers & geosciences. Volume 138(2020)
• Publication Date: 2020-05
• Subjects: Map generalization -- Map dissolution operation -- Partitioning -- Grid process -- Land-use data; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2019.104358
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Automatic thematic map generalization always needs to deal with large datasets whose processes are computationally intensive, and partitioning strategy has been proved to be one of the effective ways to make the generalization of massive data scalable and useable in practice. Nevertheless, when partitioning strategy is introduced to the dissolution operation of long and narrow patches (LN patches) in land-use data, traditional partitioning methods are not conducive to balancing the computation load on machines for each cell in the grid, and grid boundaries easily affect dissolution accuracy and stability of the process. Hence, this paper proposes an improved partitioning method for dissolving LN patches. First, massive patches are meticulously partitioned into varieties of cells based on the area balance of LN patches. Second, the grid boundaries are corrected considering the shape characteristics of the adjacent LN patches. Finally, the actual data from the national geographic census at 1:10000 scale in Guizhou and Yunnan Province in China are used for validation. The experiment shows that the proposed method can improve the computation efficiency and preserve the accuracy at the boundary for dissolution operation of massive data. Highlights: Partitioning method for dissolution operation considering the computation load balancing. Partitioning grids are meticulously partitioned accounting for the area balance of long and narrow patches. Avoid forming new fragmented patches at boundaries by grid boundary correction. Efficiency and rationality of the proposed method are verified through massive data.