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Analysing regional industrialisation in Jiangsu province using geographically weighted regression

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  • Yefang Huang

    (Department of Geography and Resource Management, Center for Environmental Policy and Resource Management, Joint Laboratory for Geoinformation Science, The Chinese University of Hong Kong, Shatin, Hong Kong (e-mail: Lucy-huang@cuhk.edu.hk, Yeeleung@cuhk.edu.hk))

  • Yee Leung

    (Department of Geography and Resource Management, Center for Environmental Policy and Resource Management, Joint Laboratory for Geoinformation Science, The Chinese University of Hong Kong, Shatin, Hong Kong (e-mail: Lucy-huang@cuhk.edu.hk, Yeeleung@cuhk.edu.hk))

Abstract

. Industry is the most important sector in the Chinese economy. To identify the spatial interaction between the level of regional industrialisation and various factors, this paper takes Jiangsu province of China as a case study. To unravel the existence of spatial nonstationarity, geographically weighted regression (GWR) is employed in this article. Conventional regression analysis can only produce `average' and `global' parameter estimates rather than `local' parameter estimates which vary over space in some spatial systems. Geographically weighted regression (GWR), on the other hand, is a relatively simple, but useful new technique for the analysis of spatial nonstationarity. Using the GWR technique to study regional industrialisation in Jiangsu province, it is found that there is a significant difference between the ordinary linear regression (OLR) and GWR models. The relationships between the level of regional industrialisation and various factors show considerable spatial variability.

Suggested Citation

  • Yefang Huang & Yee Leung, 2002. "Analysing regional industrialisation in Jiangsu province using geographically weighted regression," Journal of Geographical Systems, Springer, vol. 4(2), pages 233-249, June.
    • Handle: RePEc:kap:jgeosy:v:4:y:2002:i:2:d:10.1007_s101090200081
    DOI: 10.1007/s101090200081
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