The application of principal component cluster analysis in environment classification for Chinese cities

In order to investigate the dissimilarities of different cities in China, an approach combining principal component analysis and hierarchical clustering is proposed. Three rather than two principal components are reserved to conduct a more elaborate analysis. Based on corresponding component scores,...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2020-09, Vol.569 (1), p.12040
Main Authors: Wang, Jingcheng, Zhang, Lunwu, Zhang, Dingfei, Zhao, Fangchao, Yang, Xiaokui
Format: Article
Language:English
Subjects:
Adaptability
Cities
Classification
Cluster analysis
Clustering
Cognition
Principal components analysis
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Summary:In order to investigate the dissimilarities of different cities in China, an approach combining principal component analysis and hierarchical clustering is proposed. Three rather than two principal components are reserved to conduct a more elaborate analysis. Based on corresponding component scores, dissimilarity between each city is measured during clustering. These cities are classified into seven types, and they are marked on the map of China. The result of this classification is consistent to our traditional cognition. Therefore, the principal component cluster analysis is suitable for analyzing numerous observations with variables on a large scale. This approach helps to enhance the environmental adaptability of equipments by recognizing the environment type of each city.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/569/1/012040