Spatial-Temporal Dynamics of Jiangsu Regional Convergence with Spatial Markov Chains Approach

  • 1. Department of Urban and Resources Sciences, Nanjing University, Nanjing 210093, China;
    2. Nanjing Institute of Geography and Limnology, CAS, Nanjing 210008, China

Received date: 2004-12-21

  Revised date: 2005-06-27

  Online published: 2005-09-25

Supported by

National Natural Science Foundation of China, No. 40301038


Based on the per capita GDP dataset at the county level in Jiangsu province from 1978 to 2000, this paper attempts to apply spatial Markov chains to investigate the spatial and temporal characteristics of regional convergence in Jiangsu. Firstly, all the per capita GDP data in Jiangsu are classified into 4 different classes by annual provincial average. Due to the changes in the regional development strategies over time, the whole period is then divided into two sub-periods (1978-1990 and 1990-2000), and two Markov transition probability matrices for these two periods are estimated respectively for comparison. Secondly, two kinds of maps are accordingly made in order to visualize spatial patterns of class transitions, one for region, and the other for region and its neighbors. Finally, conditioning on each region's spatial lag at the beginning of each year, spatial Markov matrices for the two different periods are constructed. The conclusions are drawn as follows: (1) The process of regional convergence in Jiangsu has been globally characterized by "convergence clubs" since 1978, but this trend in the 1990s is sharpened and statistically different from that of the period from 1978 to 1990. (2) Those regions and their neighbors that both experience upward mobility are located in southern Jiangsu, while the regions or their neighbors that move downwardly are mostly found in northern Jiangsu. (3) Regional per capita GDP class transitions in Jiangsu are highly constrained by their geographical neighbors. If a poor region is surrounded by poor regions, the probabilities of moving upward for the periods from 1978 to 1990 and 1990 to 2000 decrease to 0.148 and 0.025 respectively, while they average 0.2 and 0.042 in traditional Markov matrices. It suggests that poor regions are negatively affected when surrounded by other poor regions. Conversely, if a rich region is surrounded by rich neighbors, the probabilities of moving upward for those two sub-periods increase to 1 and 0.991, while they are 0.987 and 0.984 on average in traditional Markov matrices. It suggests rich regions are positively influenced by other rich regions being surrounded. These empirical analyses provide a spatial explanation to "convergence clubs" detected in traditional Markov method.

Cite this article

PU Yingxia, MA Ronghua, GE Ying, HUANG Xingyuan . Spatial-Temporal Dynamics of Jiangsu Regional Convergence with Spatial Markov Chains Approach[J]. Acta Geographica Sinica, 2005 , 60(5) : 817 -826 . DOI: 10.11821/xb200505013


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