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Research on spatial economic structure for different economic sectors from a perspective of a complex network

Listed author(s):
  • Hu, Sen
  • Yang, Hualei
  • Cai, Boliang
  • Yang, Chunxia
Registered author(s):

    The economy system is a complex system, and the complex network is a powerful tool to study its complexity. Here we calculate the economic distance matrices based on annual GDP of nine economic sectors from 1995–2010 in 31 Chinese provinces and autonomous regions,11In this paper, we just study the economy structure in Chinese mainland, and Taiwan, Hong Kong and Macao are not involved. In the following parts, we use ‘region’ to represent a province or autonomous region. The relevant economic data contains the annual GDP of nine economic sectors, and are downloaded from, and we introduce the 31 regions in Appendix A simply. then build several spatial economic networks through the threshold method and the Minimal Spanning Tree method. After the analysis on the structure of the networks and the influence of geographic distance, some conclusions are drawn. First, connectivity distribution of a spatial economic network does not follow the power law. Second, according to the network structure, nine economic sectors could be divided into two groups, and there is significant discrepancy of network structure between these two groups. Moreover, the influence of the geographic distance plays an important role on the structure of a spatial economic network, network parameters are changed with the influence of the geographic distance. At last, 2000 km is the critical value for geographic distance: for real estate and finance, the spearman’s rho with l<2000 is bigger than that with l>2000, and the case is opposite for other economic sectors.

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    Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

    Volume (Year): 392 (2013)
    Issue (Month): 17 ()
    Pages: 3682-3697

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    Handle: RePEc:eee:phsmap:v:392:y:2013:i:17:p:3682-3697
    DOI: 10.1016/j.physa.2013.04.010
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    1. Heimo, Tapio & Kaski, Kimmo & Saramäki, Jari, 2009. "Maximal spanning trees, asset graphs and random matrix denoising in the analysis of dynamics of financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(2), pages 145-156.
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    7. Namaki, A. & Shirazi, A.H. & Raei, R. & Jafari, G.R., 2011. "Network analysis of a financial market based on genuine correlation and threshold method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3835-3841.
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