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Analyzing regional economic development patterns in a fast developing province of China through geographically weighted principal component analysis

Author

Listed:
  • Zaijun Li

    (Nanjing Normal University)

  • Jianquan Cheng

    (Manchester Metropolitan University)

  • Qiyan Wu

    (Nanjing Normal University)

Abstract

Understanding the spatial structure of regional economic development is of importance for regional planning and provincial development strategy making. Taking Jiangsu Province in the economically richest Yangtze Delta as a case study, this paper aims to explore regional economic development level on a provincial scale. Using the data sets from provincial statistical yearbook of 2010, eleven variables are selected for statistical and spatial analyses at a county level. Both the traditional principal component analysis (PCA) and its local version—geographically weighted PCA (GWPCA)—are employed to these analyses for the purpose of comparison. The results have confirmed that GWPCA is an effective means of analyzing regional economic development level through mapping its local principal components. It is also concluded that the regional economic development in Jiangsu Province demonstrates spatial inequality between the North and South.

Suggested Citation

  • Zaijun Li & Jianquan Cheng & Qiyan Wu, 2016. "Analyzing regional economic development patterns in a fast developing province of China through geographically weighted principal component analysis," Letters in Spatial and Resource Sciences, Springer, vol. 9(3), pages 233-245, October.
    • Handle: RePEc:spr:lsprsc:v:9:y:2016:i:3:d:10.1007_s12076-015-0154-2
    DOI: 10.1007/s12076-015-0154-2
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    3. Luca Salvati, 2018. "Population growth and the economic crisis: understanding latent patterns of change in Greece, 2002–2016," Letters in Spatial and Resource Sciences, Springer, vol. 11(2), pages 105-126, July.
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    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other

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