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Proceedings Paper

Study on disparity of regional economic development based on geoinformatic Tupu and GWR model: a case of growth of GDP per capita in China from 1999 to 2003
Author(s): Feixue Li; Manchun Li; Jian Liang
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Paper Abstract

Regional disparity of economic development in China is always greatly concerned by investigators domestic and abroad, and sets of models have been used in the analysis. Spatial dependence, which is hidden in the data with spatial attributes, usually is not taken into account in classical statistics methods, such as ordinary linear regression(OLR) model. Along with the development of spatial statistics, more and more attentions are paid on spatial interactions between observations in the study of regional disparity. Geographically weighted regression (GWR) is a simple but effective model to recognize spatial variation and local difference, which considers the influence of the spatially non-stationarity of the variables. In this study, GWR model and Geo-Informatic Tupu were used to analyze the disparity of regional economic development in China, taking GDP per capita in 1999 and 2003 as a case, which is usually used to measure level of economic development. GDP per capita in the 338 cities in 1999 were selected to simulate GDP per capita in 2003 and to analysis non-stationarity of the growth of GDP per capita. Using Geo-Informatic Tupu, A series of maps were processed to display patterns of local parameter estimates, such as local r-squares, the residual sum of squares, local residual and so on, to gain a better understanding of the degree of spatial non-stationarity in a relationship over space. We tested for geographic heterogeneity in the parameters and compare them to estimates obtained from global regression approaches. The results suggested there was heterogeneity in the regression coefficients across broad regions of China, and a one-size fits all approach to describe growth processes appeared simplistic. The GWR model improved over the OLR model, and it was able to better explain the variation in the data and to simulate GDP per capita with smaller errors than the OLR models.

Paper Details

Date Published: 6 August 2007
PDF: 9 pages
Proc. SPIE 6754, Geoinformatics 2007: Geospatial Information Technology and Applications, 67543A (6 August 2007); doi: 10.1117/12.765494
Show Author Affiliations
Feixue Li, Nanjing Univ. (China)
Manchun Li, Nanjing Univ. (China)
Jian Liang, Nanjing Univ. (China)

Published in SPIE Proceedings Vol. 6754:
Geoinformatics 2007: Geospatial Information Technology and Applications
Peng Gong; Yongxue Liu, Editor(s)

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