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

被引：1
|
作者：
Wang, Jingcheng [1 ,2 ,3 ]
Zhang, Lunwu [1 ,3 ]
Zhang, Dingfei [2 ]
Zhao, Fangchao [1 ,3 ]
Yang, Xiaokui [1 ]
机构：
[1] Southwest Technol & Engn Res Inst, Chongqing 400039, Peoples R China
[2] Chongqing Univ, Coll Mat Sci & Engn, Chongqing 400044, Peoples R China
[3] CSGC Key Lab Ammunit Storage Environm Effects, Chongqing, Peoples R China
来源：
2020 THIRD INTERNATIONAL WORKSHOP ON ENVIRONMENT AND GEOSCIENCE | 2020年 / 569卷
关键词：
D O I：
10.1088/1755-1315/569/1/012040
中图分类号：
P5 [地质学];
学科分类号：
0709 ; 081803 ;
摘要：
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.
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页数：8
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