An area merging method in map generalization considering typical characteristics of structured geographic objects. Issue 3 (4th May 2021)

• Record Type: Journal Article
• Authors: Li, Chengming; Yin, Yong; Wu, Pengda; Wu, Wei
• Is Part Of: Cartography and geographic information science. Volume 48:Issue 3(2021)
• Page Start: 210
• Page End: 224
• Publication Date: 2021-05-04
• Subjects: Map generalization -- merging operation -- structured geographic objects -- typical spatial characteristics -- conflict adjustment; Cartography -- Periodicals; Geographic information systems -- Periodicals; 526
• DOI: 10.1080/15230406.2020.1863862
• Languages: English
• ISSNs: 1523-0406

Abstract

ABSTRACT: Merging is an important operation in the map generalization of land-cover and other coverages. We define structured geographic objects as collections of adjacent areas with homogeneous semantics that are regularly arranged as spatial structures. Existing studies have concentrated on unstructured objects, which will lead to the structured ones losing part or even most of the typical characteristics during merging. Therefore, as a supplement to the existing mature merging method, a targeted method was proposed in this paper to address the merging problem of structured geographic objects. First, structured geographic objects were classified into four typical patterns, and they were identified automatically according to seven spatial structure parameters. Second, a Miter-type buffer transformation was introduced to extract the overall boundary of structured geographic objects, and areas inside the overall boundary were processed with the most appropriate merging operations for their pattern. Finally, the corresponding merged results of structured geographic objects were inserted back into the merged result of the original land-cover data by using the NOT operation, and the spatial conflicts near the boundary were adjusted. We test our method for a dataset of geographical census data for a city in China. The experimental results revealed that compared with state-of-the-art method, the proposed method produces more reasonable generalization result by effectively identifying and maintaining the typical spatial structures; moreover, the proposed method also preserves the planar tessellation characteristic of land-cover data and the balance of area variation in each land-cover class.