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Visualizing bivariate local spatial autocorrelation between commodity revealed comparative advantage index of China and USA from a new space perspective

Author

Listed:
  • Sijing Ye

    (Beijing Normal University, China)

  • Changxiu Cheng

    (Beijing Normal University, China; National Tibetan Plateau Data Center, China)

  • Changqing Song
  • Shi Shen

Abstract

The development of trade research plays an important role in enhancing the understanding of the trade relationship structure, evolution and relevant driving factors. While there is little research on analyzing and visualizing bilateral trade patterns and the evolution of numerous kinds of commodities. In this paper, we respectively calculate revealed comparative advantage index (RCAI) of each traded commodity of China and USA in 2017. Then the RCAI of thousands of commodities have been mapped to a two-dimensional space and visualized in a grid system by using digital trade feature map method. On that basis, bivariate local spatial auto-correlation features between China – USA have been comprehensively depicted.

Suggested Citation

  • Sijing Ye & Changxiu Cheng & Changqing Song & Shi Shen, 2021. "Visualizing bivariate local spatial autocorrelation between commodity revealed comparative advantage index of China and USA from a new space perspective," Environment and Planning A, , vol. 53(2), pages 223-226, March.
    • Handle: RePEc:sae:envira:v:53:y:2021:i:2:p:223-226
    DOI: 10.1177/0308518X20957336
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    Cited by:

    1. Xiaoyang Han & Sijing Ye & Shuyi Ren & Changqing Song, 2023. "Using the DTFM Method to Analyse the Degradation Process of Bilateral Trade Relations between China and Australia," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    2. Liao, Maolin & Zhang, Ze & Jia, Jin & Xiong, Jiao & Han, Mengyao, 2022. "Mapping China's photovoltaic power geographies: Spatial-temporal evolution, provincial competition and low-carbon transition," Renewable Energy, Elsevier, vol. 191(C), pages 251-260.

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