Volume 4, Issue 5, October 2015, Page: 109-114
Descriptive Study of 2009-2013 China Area per Capita GDP
Renhao Jin, School of Information, Beijing Wuzi University, Beijing, ChinaSchool of Information, Beijing Wuzi University, Beijing, China
Fang Yan, School of Information, Beijing Wuzi University, Beijing, China
Jie Zhu, School of Information, Beijing Wuzi University, Beijing, China
Received: Jul. 7, 2015;       Accepted: Jul. 22, 2015;       Published: Sep. 17, 2015
DOI: 10.11648/j.jwer.20150405.11      View  3804      Downloads  99
Abstract
This paper studied area level per capita GDP data from 2009 to 2013 in China. The bar chart, bubble chart and map chart are used to display a growth trend on area per capita GDP. It is pointed out that areas with higher Per Capita GDP have relative lower growth rate on Per Capita GDP. Moran's I coefficients and Geary's C coefficients are used to measure the Spatial autocorrelation in the Per capita GDP data. The results of Moran's I coefficient and Geary's c coefficients test showed that global spatial autocorrelation are accepted, while local spatial autocorrelation are rejected.
Keywords
China GDP, Area per Capita GDP, Spatial Analysis
To cite this article
Renhao Jin, Fang Yan, Jie Zhu, Descriptive Study of 2009-2013 China Area per Capita GDP, Journal of World Economic Research. Vol. 4, No. 5, 2015, pp. 109-114. doi: 10.11648/j.jwer.20150405.11
Reference
[1]
Anselin, L. (1995). Local indicators of spatial association – LISA. Geographical Analysis 27, 93--115.
[2]
Chegut, A. M., Eichholtz, P. M. A, Rodrigues, P. J. M. (2015). Spatial Dependence in International Office Markets. The Journal of Real Estate Finance and Economics. Volume 51, Issue 2, pp 317-350.
[3]
Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society B 36, 192--225.
[4]
Dawson, Graham (2006). Economics and Economic Chenge. FT / Prentice Hall. p. 205. ISBN 9780273693512.
[5]
Getis, A. and Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis 24, 189--206.
[6]
Griffith, D. (1992). What is spatial autocorrelation? Reflections on the past 25 years of spatial statistics. l’ Espace Ge´ographique 21,265--280.
[7]
Griffith, D. (1996). Spatial autocorrelation and eigenfunctions of thegeographic weights matrix accompanying geo-referenced data. The Canadian Geographer 40, 351--367.
[8]
Griffith, D. and Chun, Y. (2015). Spatial Autocorrelation in Spatial Interactions Models: Geographic Scale and Resolution Implications for Network Resilience and Vulnerability. Networks and Spatial Economics.
[9]
"GDP (Official Exchange Rate)". CIA World Factbook. Retrieved June 2, 2012.
[10]
Liu, Y., Schen, C., Li, Y. (2015). Differentiation regularity of urban-rural equalized development at prefecture-level city in China. Journal of Geographical Sciences.
[11]
Mardia, K. and Marshall, R. (1984). Maximum likelihood estimation ofmodels for residual covariance in spatial regression. Biometrika 71,135--146.
[12]
Mohebbi, M., Wolfe, R., Jolley, D. (2011). A poisson regression approach for modelling spatial autocorrelation between geographically referenced observations. BMC Medical Research Methodology.
[13]
Peck, G. (2013). Tableau 8: The Official Guide by Peck, George (2013) Paperback. McGraw-Hill Osborne Media.
[14]
Richardson, S. and He´ mon, D. (1981). On the variance of the samplecorrelation between two independent lattice processes. Journal ofApplied Probability 18, 943--948.
[15]
Tiefelsdorf, M. and Boots, B. (1995). The exact distribution of Moran’s I.Environment and Planning.A 27, 985--999.
[16]
SAS Institute Inc, (2008). SAS/STAT® 9.2 User’s Guide: The variogram Procedure (Book Excerpt). NC: SAS Institute Inc, Cary.
Browse journals by subject