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The Performance of the Smart Cities in China—A Comparative Study by Means of Self-Organizing Maps and Social Networks Analysis

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  • Dong Lu

    (School of Business, Sichuan Normal University, Chengdu 610101, China)

  • Ye Tian

    (School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China)

  • Vincent Y. Liu

    (School of Business, Macau University of Science and Technology, Taipa, Macau)

  • Yi Zhang

    (Department of Mathematics, Tongji University, Shanghai 200092, China)

Abstract

Smart cities link the city services, citizens, resource and infrastructures together and form the heart of the modern society. As a “smart” ecosystem, smart cities focus on sustainable growth, efficiency, productivity and environmentally friendly development. By comparing with the European Union, North America and other countries, smart cities in China are still in the preliminary stage. This study offers a comparative analysis of ten smart cities in China on the basis of an extensive database covering two time periods: 2005–2007 and 2008–2010. The unsupervised computational neural network self-organizing map (SOM) analysis is adopted to map out the various cities based on their performance. The demonstration effect and mutual influences between these ten smart cities are also discussed by using social network analysis. Based on the smart city performance and cluster network, current problems for smart city development in China were pointed out. Future research directions for smart city research are discussed at the end this paper.

Suggested Citation

  • Dong Lu & Ye Tian & Vincent Y. Liu & Yi Zhang, 2015. "The Performance of the Smart Cities in China—A Comparative Study by Means of Self-Organizing Maps and Social Networks Analysis," Sustainability, MDPI, vol. 7(6), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:6:p:7604-7621:d:51066
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    References listed on IDEAS

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    Cited by:

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    2. Meiling Han & Martin De Jong & Zhuqing Cui & Limin Xu & Haiyan Lu & Baiqing Sun, 2018. "City Branding in China’s Northeastern Region: How Do Cities Reposition Themselves When Facing Industrial Decline and Ecological Modernization?," Sustainability, MDPI, vol. 10(1), pages 1-25, January.
    3. Sangsung Park & Seung-Joo Lee & Sunghae Jun, 2015. "A Network Analysis Model for Selecting Sustainable Technology," Sustainability, MDPI, vol. 7(10), pages 1-16, September.
    4. Richard Hu, 2019. "The State of Smart Cities in China: The Case of Shenzhen," Energies, MDPI, vol. 12(22), pages 1-18, November.
    5. José Manuel Naranjo Gómez, 2016. "Impacts on the Social Cohesion of Mainland Spain’s Future Motorway and High-Speed Rail Networks," Sustainability, MDPI, vol. 8(7), pages 1-22, July.
    6. Ivan Serrano & Laura Calvet-Mir & Ramon Ribera-Fumaz & Isabel Díaz & Hug March, 2020. "A Social Network Analysis of the Spanish Network of Smart Cities," Sustainability, MDPI, vol. 12(12), pages 1-13, June.
    7. Wei Tang & Tiancai Zhou & Jian Sun & Yurui Li & Weipeng Li, 2017. "Accelerated Urban Expansion in Lhasa City and the Implications for Sustainable Development in a Plateau City," Sustainability, MDPI, vol. 9(9), pages 1-19, August.
    8. Aiyshwariya Paulvannan Kanmani & Renee Obringer & Benjamin Rachunok & Roshanak Nateghi, 2020. "Assessing Global Environmental Sustainability Via an Unsupervised Clustering Framework," Sustainability, MDPI, vol. 12(2), pages 1-12, January.

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