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Quantitative Relations between Topological Similarity Degree and Map Scale Change of Contour Clusters in Multi-Scale Map Spaces

Bibliographic Data

ID22031432
AuthorsRong Wang (0000-0003-1070-1460, Lanzhou Jiaotong University), Haowen Yan (0000-0002-2792-3425, Lanzhou Jiaotong University, corresponding author), Xiaomin Lu (0000-0002-6206-251X, Lanzhou Jiaotong University)
Year2022
Volume11
Issue4
Pages268
Publication date2022-04-18
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi11040268
OpenAlexW4224240701
LanguageEN
Citations received1
References cited22

Quantitative relations between topological similarity degree and map scale change of multi-scale contour clusters are vital to the automation of map generalization. However, no method has been proposed to calculate the relations. This paper aims at filling the gap by proposing a new approach. It firstly constructed a directed contour tree by pre-processing of unclosed contours, and then developed a quantitative expression of topological relations of contour cluster based on directed contour tree. After this, it employed 108 groups of multi-scale contour clusters with different geomorphological types to explore the changing regularity of topological indices with map scale. Last, it used 416 points to calculate the quantitative relations between topological similarity degree and map scale change by curve fitting method. The results show that the quantitative expression of multi-scale topological indexes is closely related to the contour interval change, and power function is the best fit among the candidate functions

Cartographic generalization · Cartography · Combinatorics · Contour line · Generalization · Geography · Mathematical analysis · Pure mathematics · Quasi-open map · Topological map · Computer Science · Data Management and Algorithms · Geographic Information Systems Studies · Image Retrieval and Classification Techniques · Mathematics · Artificial Intelligence

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Unique citing works1
Citations per year1
Citation span2025 - 2025 (1)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 1

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