A partitioned collapse method for long and narrow patches. (February 2022)

• Record Type: Journal Article
• Authors: Li, Chengming; Yin, Yong; Wu, Pengda; Wu, Wei
• Is Part Of: Computers & geosciences. Volume 159(2022)
• Publication Date: 2022-02
• Subjects: Partitioned collapse -- Topological inconsistency patterns -- Topological reconstruction; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2021.104984
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Collapse is a basic generalization operation in land-use data multi-scale representation. It is a compute-intensive operation and involves complex topological transformations. A feasible way to improve the computation efficiency is to adopt a parallel strategy, which means that the collapse of a large spatial range of long and narrow (LN) patches via a partitioning-based approach. However, many topological inconsistencies occur around the partition-cell boundaries. Hence, we propose a partitioned collapse method for LN patches and focus on the reconstruction problem of topological inconsistencies. First, topological inconsistencies were classified into four patterns based on their morphological characteristics. Second, for each of these patterns, a corresponding algorithm was formulated to reconcile the topological inconsistencies. Finally, our approach was validated using the national geographical condition data of Chishui City, Guizhou Province. The experiment results show that our method generates smooth split lines from land-use patches by effectively eliminating topology inconsistency patterns near the partition-cell boundary lines and is 8.7 times more efficient than the traditional unpartitioned method within a city region. Highlights: Partition methodology of collapse operation for long and narrow patches generalization. Topology consistency maintenance during partitioned collapse operation. Reliability and efficiency analysis by comparing with an un-partitioned collapse method with a small-area data. Efficiency and superiority verification by statistical analysis with a large-area data. No potential conflict of interest was reported by the authors.