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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aghion, P., Howitt, P.: The Economics of Growth. The MIT Press Cambridge, London (2009)
Balasubramanyam, V.N., Salisu, M., Sapsford, D.: Foreign direct investment and growth in EP and is countries. Econ. J. 106(434), 92–105 (1996)
Balasubramanyam, V.N., Salisu, M., Sapsford, D.: Foreign direct investment as an engine of growth. J. Int. Trade Econ. Dev. 8(1), 27–40 (1999)
Bassanini, A., Scarpetta, S.: The driving forces of economic growth: panel data evidence for the OECD countries. OECD Econ. Stud. 2001(2), 10–56 (2002)
Charlton, M., Brunsdon, C., Demšar, U., Harris, P., Fotheringham, A.S.: Principal component analysis: from global to local. In: The 13th AGILE International Conference on Geographic Information Science, pp. 1–10. Guimarães (2010)
Demšar, U., Harris, P., Brunsdon, C., Fotheringham, A.S., McLoone, S.: Principal component analysis on spatial data: an overview. Ann. Assoc. Am. Geogr. 103(1), 106–128 (2013)
Fotheringham, A.S., Brunsdon, C., Charlton, M.: Geographically Weighted Regression: The analysis of spatially varying relationships. Wiley, Chiceste (2002)
Fotheringham, A.S., Brunsdon, C.: Local forms of spatial analysis. Geogr. Anal. 31(4), 340–358 (1999)
Gollini, I., Lu, B.B., Charlton, M., Brunsdon, C., Harris, P.: GWmodel: An R package for exploring spatial heterogeneity using geographically weighted models. J. Stat. Softw. 63(17), 1–50 (2015)
Goodchilid, M.F.: The validity and usefulness of laws in geographic information science and geography. Ann. Assoc. Am. Geogr. 94(2), 300–303 (2004)
Harris, P., Brunsdon, C., Charlton, M.: Geographically weighted principal components analysis. Int. J. Geogr. Inf. Sci. 25(10), 1717–1736 (2011)
Harris, P., Clarke, A., Juggins, S., Brunsdon, C., Charlton, M.: Enhancements to a geographically weighted principal components analysis in the context of an application to an environmental data set. Geogr. Anal. 47(2), 146–172 (2015)
Jeffers, J.N.R.: Two case studies in the application of principal component analysis. J. R. Stat. Soc. Ser. C (Appl. Stat.) 16(3), 225–236 (1967)
Jiangsu Statistical Bureau (JSB).: Jiangsu tongji nianjian (Jiangsu Statistics Yearbook). Chinese Statistics Press, Beijing (2011)
Kaspari, M., Yanoviak, S.: Biogeochemistry and the structure of tropical brown food webs. Ecology 90(12), 3342–3351 (2009)
Kumar, S., Lal, R., Lloyd, C.D.: Assessing spatial variability in soil characteristics with geographically weighted principal component analysis. Comput. Geosci. 16(3), 827–835 (2012)
Legendre, P., Gallagher, E.: Ecological meaningful transformations for ordination of species data. Oecologia 129(2), 271–280 (2001)
Li, G.D., Fang, C.L.: Analyzing the multi-mechanism of regional inequality in China. Ann. Reg. Sci. 52(1), 155–182 (2014)
Lloyd, C.D.: Local Models for Spatial Analysis, 2nd edn. CRC Press, London (2007)
Lloyd, C.D.: Analysing population characteristics using geographically weighted principal components analysis: a case study of Northern Ireland in 2001. Comput. Environ. Urban Syst. 34(5), 389–399 (2010)
Lu, B., Harris, P., Charlton, M., Brunsdon, C.: The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models. Geo Spatial Inf. Sci. 17(2), 85–101 (2014)
Openshaw, S., Charlton, M., Wymer, C., Craft, A.W.: A mark 1 geographical analysis machine for the automated analysis of point data sets. Int. J. Geogr. Inf. Syst. 1(4), 335–358 (1987)
Stimson, R.J., Stough, R.R., Roberts, B.H.: Regional Economic Development: Analysis and Planning Strategy. Springer, Berlin (2006)
Wu, Q.Y., Cheng, J.Q., Chen, G., Hammel, D.J., Wu, X.H.: Socio-spatial differentiation and residential segregation in the Chinese city based on the 2000 community-level census data: a case study of the inner city of Nanjing. Cities 39, 109–119 (2014)
Acknowledgments
The research is supported by National Natural Science Foundation of China (No. 41271176), Chinese Minister of Education Project of Humanities and Social Sciences (No. 12YJAZH159), and A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Li, Z., Cheng, J. & Wu, Q. Analyzing regional economic development patterns in a fast developing province of China through geographically weighted principal component analysis. Lett Spat Resour Sci 9, 233–245 (2016). https://doi.org/10.1007/s12076-015-0154-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12076-015-0154-2
Keywords
- Regional economic development
- Spatial non-stationarity
- Principal component analysis
- Geographically weighted principal component analysis
- Jiangsu