Divide China's Economic Regions in 2019 Based on Cluster Analysis and Principal Component Analysis

Zhichao Zhan; Yongquan Jin; Meihua Dong

doi:doi:10.11648/j.ijsd.20210704.11

| Peer-Reviewed

Divide China's Economic Regions in 2019 Based on Cluster Analysis and Principal Component Analysis

Zhichao Zhan, Yongquan Jin, Meihua Dong

Published in International Journal of Statistical Distributions and Applications (Volume 7, Issue 4)

Received: 5 July 2021 Accepted: 26 July 2021 Published: 5 November 2021

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

In recent years, China's economic development has been very rapid. While China is developing rapidly, each province has contributed its share, but in different regions, economic development is different. Different regions must have advantages in different aspects, so in order to divide China's 31 provinces into different categories. In order to get the ranking of the provinces that have the greatest impact on China's economy. We first adopt the method of principal component analysis to reduce the dimensions of 11 variables that affect the economic factors of each province, and obtain two principal components to reflect all sample information. Then, perform dimensionality reduction and cluster analysis on the obtained data, and use the sum of squared variance (WARD) method to perform cluster analysis on the two principal components. Finally, the social development of 31 provinces in my country is divided into 4 categories. It is concluded that Beijing and Shanghai are first-level developed provinces, Jiangsu and Guangdong are second-level developed provinces, Hebei, Sichuan, Hunan, Shandong, Henan, Shanxi, and Hubei are third-level developed provinces, Tianjin, Hainan, Tibet, Qinghai, Ningxia, Inner Mongolia, Jilin, Gansu, Xinjiang, Fujian, Chongqing, Liaoning, Anhui, Shaanxi, Jiangxi, Guizhou, Yunnan, Heilongjiang, and Guangxi are four-tier developed provinces. I hope our results can help relevant departments.

Published in	International Journal of Statistical Distributions and Applications (Volume 7, Issue 4)
DOI	10.11648/j.ijsd.20210704.11
Page(s)	83-88
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Principal Component Analysis, Cluster Analysis, Economy, Squared Deviations Method

References

[1]	Data Sources: http://www.stats.gov.cn/.
[2]	Edited by Gao Huixuan. Applied Multivariate Statistical Analysis. Beijing: Peking University Press. 2005.01.
[3]	SAS Software and Statistics Application Course/Wang Yuanzheng, Editor-in-Chief Xu Yajing. Beijing: Machinery Industry Press, 2007.1.
[4]	Li Na. Evaluation of the coordinated development of China's regional economy based on AHP cluster analysis [j]. Microcomputer Application, 2021, 37 (04): 151-153.
[5]	Chen Jiaqi, Xiang Guangxin. Research on the Comprehensive Evaluation of Rural Regional Economic Development Differences in Hunan Province Based on Principal Component Analysis [J]. China Economic and Trade Guide (Secondary), 2021 (08): 71-74.
[6]	Wu C X. Research on urban residents' income based on principal component analysis and cluster analysis [J]. Journal of Huangshan University, 201, 23 (03): 7-10.
[7]	Meng Q. Comprehensive evaluation of economic development quality in Anhui Province based on principal component analysis [J]. Journal of Xichang University (Natural Science Edition), 201, 35 (02): 43-48.
[8]	Ji Xionghua, Bai Zongming, Song Zhufang. Evaluation of government economic governance capability based on Principal Component Analysis [J]. Journal of Yan'an Vocational and Technical College, 201, 35 (03): 1-4+13.
[9]	Fan Yameng. Evaluation of comprehensive economic strength of Counties in Chongqing based on principal component analysis [J]. Guangxi Quality Supervision Review, 2021 (05): 73-74.
[10]	Liu Yao, XIONG Jianping. Comparison of tourism development level in Hubei province based on principal component analysis and cluster analysis [J]. Tourism Survey, 2021 (10): 153-158.
[11]	Hu Jiangxia, Luo Zhigao, Wen Chuanhao. Statistics and Decision, 201, 37 (12): 82-85.
[12]	Shan N. County economic pattern and its difference evolution and mechanism analysis in Jiangsu Province [J]. Journal of Inner Mongolia University of Finance and Economics, 20119 (03): 104-107.
[13]	Jiang Yonghong, Zhang Yindan. Competitiveness evaluation and Promotion countermeasures of prefecture-level cities in Shaanxi Province: Based on factor analysis and cluster analysis [J]. Journal of Shaanxi University of Administration, 201, 35 (02): 9-14.
[14]	Yang J K. Research on the structure of Chinese residents' consumption expenditure -- based on factor analysis and cluster analysis [J]. Modern Business, 2021 (14): 7-9.
[15]	TAN Y. Analysis of regional economic differences based on cluster analysis model -- a case study of Sichuan Province. China Market, 2021 (14): 4-7.
[16]	Gao Huixian, Xue Yulian, Fang Zhong. Journal of Fujian Normal University, 2021, 39 (02): 189-195.

Cite This Article

Plain Text BibTeX RIS

APA Style

Zhichao Zhan, Yongquan Jin, Meihua Dong. (2021). Divide China's Economic Regions in 2019 Based on Cluster Analysis and Principal Component Analysis. International Journal of Statistical Distributions and Applications, 7(4), 83-88. https://doi.org/10.11648/j.ijsd.20210704.11

Copy | Download

ACS Style

Zhichao Zhan; Yongquan Jin; Meihua Dong. Divide China's Economic Regions in 2019 Based on Cluster Analysis and Principal Component Analysis. Int. J. Stat. Distrib. Appl. 2021, 7(4), 83-88. doi: 10.11648/j.ijsd.20210704.11

Copy | Download

AMA Style

Zhichao Zhan, Yongquan Jin, Meihua Dong. Divide China's Economic Regions in 2019 Based on Cluster Analysis and Principal Component Analysis. Int J Stat Distrib Appl. 2021;7(4):83-88. doi: 10.11648/j.ijsd.20210704.11

Copy | Download

@article{10.11648/j.ijsd.20210704.11,
  author = {Zhichao Zhan and Yongquan Jin and Meihua Dong},
  title = {Divide China's Economic Regions in 2019 Based on Cluster Analysis and Principal Component Analysis},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {7},
  number = {4},
  pages = {83-88},
  doi = {10.11648/j.ijsd.20210704.11},
  url = {https://doi.org/10.11648/j.ijsd.20210704.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20210704.11},
  abstract = {In recent years, China's economic development has been very rapid. While China is developing rapidly, each province has contributed its share, but in different regions, economic development is different. Different regions must have advantages in different aspects, so in order to divide China's 31 provinces into different categories. In order to get the ranking of the provinces that have the greatest impact on China's economy. We first adopt the method of principal component analysis to reduce the dimensions of 11 variables that affect the economic factors of each province, and obtain two principal components to reflect all sample information. Then, perform dimensionality reduction and cluster analysis on the obtained data, and use the sum of squared variance (WARD) method to perform cluster analysis on the two principal components. Finally, the social development of 31 provinces in my country is divided into 4 categories. It is concluded that Beijing and Shanghai are first-level developed provinces, Jiangsu and Guangdong are second-level developed provinces, Hebei, Sichuan, Hunan, Shandong, Henan, Shanxi, and Hubei are third-level developed provinces, Tianjin, Hainan, Tibet, Qinghai, Ningxia, Inner Mongolia, Jilin, Gansu, Xinjiang, Fujian, Chongqing, Liaoning, Anhui, Shaanxi, Jiangxi, Guizhou, Yunnan, Heilongjiang, and Guangxi are four-tier developed provinces. I hope our results can help relevant departments.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Divide China's Economic Regions in 2019 Based on Cluster Analysis and Principal Component Analysis
AU  - Zhichao Zhan
AU  - Yongquan Jin
AU  - Meihua Dong
Y1  - 2021/11/05
PY  - 2021
N1  - https://doi.org/10.11648/j.ijsd.20210704.11
DO  - 10.11648/j.ijsd.20210704.11
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 83
EP  - 88
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20210704.11
AB  - In recent years, China's economic development has been very rapid. While China is developing rapidly, each province has contributed its share, but in different regions, economic development is different. Different regions must have advantages in different aspects, so in order to divide China's 31 provinces into different categories. In order to get the ranking of the provinces that have the greatest impact on China's economy. We first adopt the method of principal component analysis to reduce the dimensions of 11 variables that affect the economic factors of each province, and obtain two principal components to reflect all sample information. Then, perform dimensionality reduction and cluster analysis on the obtained data, and use the sum of squared variance (WARD) method to perform cluster analysis on the two principal components. Finally, the social development of 31 provinces in my country is divided into 4 categories. It is concluded that Beijing and Shanghai are first-level developed provinces, Jiangsu and Guangdong are second-level developed provinces, Hebei, Sichuan, Hunan, Shandong, Henan, Shanxi, and Hubei are third-level developed provinces, Tianjin, Hainan, Tibet, Qinghai, Ningxia, Inner Mongolia, Jilin, Gansu, Xinjiang, Fujian, Chongqing, Liaoning, Anhui, Shaanxi, Jiangxi, Guizhou, Yunnan, Heilongjiang, and Guangxi are four-tier developed provinces. I hope our results can help relevant departments.
VL  - 7
IS  - 4
ER  -

Copy | Download

Author Information

Zhichao Zhan

Mathematics Department, Yanbian University, Yanji, P. R. China
Yongquan Jin

Mathematics Department, Yanbian University, Yanji, P. R. China
Meihua Dong

Mathematics Department, Yanbian University, Yanji, P. R. China

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Zhichao Zhan, Yongquan Jin, Meihua Dong. (2021). Divide China's Economic Regions in 2019 Based on Cluster Analysis and Principal Component Analysis. International Journal of Statistical Distributions and Applications, 7(4), 83-88. https://doi.org/10.11648/j.ijsd.20210704.11

Copy | Download

ACS Style

Zhichao Zhan; Yongquan Jin; Meihua Dong. Divide China's Economic Regions in 2019 Based on Cluster Analysis and Principal Component Analysis. Int. J. Stat. Distrib. Appl. 2021, 7(4), 83-88. doi: 10.11648/j.ijsd.20210704.11

Copy | Download

AMA Style

Zhichao Zhan, Yongquan Jin, Meihua Dong. Divide China's Economic Regions in 2019 Based on Cluster Analysis and Principal Component Analysis. Int J Stat Distrib Appl. 2021;7(4):83-88. doi: 10.11648/j.ijsd.20210704.11

Copy | Download

@article{10.11648/j.ijsd.20210704.11,
  author = {Zhichao Zhan and Yongquan Jin and Meihua Dong},
  title = {Divide China's Economic Regions in 2019 Based on Cluster Analysis and Principal Component Analysis},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {7},
  number = {4},
  pages = {83-88},
  doi = {10.11648/j.ijsd.20210704.11},
  url = {https://doi.org/10.11648/j.ijsd.20210704.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20210704.11},
  abstract = {In recent years, China's economic development has been very rapid. While China is developing rapidly, each province has contributed its share, but in different regions, economic development is different. Different regions must have advantages in different aspects, so in order to divide China's 31 provinces into different categories. In order to get the ranking of the provinces that have the greatest impact on China's economy. We first adopt the method of principal component analysis to reduce the dimensions of 11 variables that affect the economic factors of each province, and obtain two principal components to reflect all sample information. Then, perform dimensionality reduction and cluster analysis on the obtained data, and use the sum of squared variance (WARD) method to perform cluster analysis on the two principal components. Finally, the social development of 31 provinces in my country is divided into 4 categories. It is concluded that Beijing and Shanghai are first-level developed provinces, Jiangsu and Guangdong are second-level developed provinces, Hebei, Sichuan, Hunan, Shandong, Henan, Shanxi, and Hubei are third-level developed provinces, Tianjin, Hainan, Tibet, Qinghai, Ningxia, Inner Mongolia, Jilin, Gansu, Xinjiang, Fujian, Chongqing, Liaoning, Anhui, Shaanxi, Jiangxi, Guizhou, Yunnan, Heilongjiang, and Guangxi are four-tier developed provinces. I hope our results can help relevant departments.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Divide China's Economic Regions in 2019 Based on Cluster Analysis and Principal Component Analysis
AU  - Zhichao Zhan
AU  - Yongquan Jin
AU  - Meihua Dong
Y1  - 2021/11/05
PY  - 2021
N1  - https://doi.org/10.11648/j.ijsd.20210704.11
DO  - 10.11648/j.ijsd.20210704.11
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 83
EP  - 88
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20210704.11
AB  - In recent years, China's economic development has been very rapid. While China is developing rapidly, each province has contributed its share, but in different regions, economic development is different. Different regions must have advantages in different aspects, so in order to divide China's 31 provinces into different categories. In order to get the ranking of the provinces that have the greatest impact on China's economy. We first adopt the method of principal component analysis to reduce the dimensions of 11 variables that affect the economic factors of each province, and obtain two principal components to reflect all sample information. Then, perform dimensionality reduction and cluster analysis on the obtained data, and use the sum of squared variance (WARD) method to perform cluster analysis on the two principal components. Finally, the social development of 31 provinces in my country is divided into 4 categories. It is concluded that Beijing and Shanghai are first-level developed provinces, Jiangsu and Guangdong are second-level developed provinces, Hebei, Sichuan, Hunan, Shandong, Henan, Shanxi, and Hubei are third-level developed provinces, Tianjin, Hainan, Tibet, Qinghai, Ningxia, Inner Mongolia, Jilin, Gansu, Xinjiang, Fujian, Chongqing, Liaoning, Anhui, Shaanxi, Jiangxi, Guizhou, Yunnan, Heilongjiang, and Guangxi are four-tier developed provinces. I hope our results can help relevant departments.
VL  - 7
IS  - 4
ER  -

Copy | Download