
The application of principal component cluster analysis in environment classification for Chinese cities
Author(s) -
Jingcheng Wang,
Zhang Lunwu,
Dingfei Zhang,
Fangchao Zhao,
Xiaokui Yang
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/569/1/012040
Subject(s) - principal component analysis , adaptability , hierarchical clustering , cluster analysis , cluster (spacecraft) , scale (ratio) , china , computer science , multiple correspondence analysis , component (thermodynamics) , pattern recognition (psychology) , data mining , artificial intelligence , geography , machine learning , cartography , ecology , archaeology , biology , programming language , physics , thermodynamics
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.