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

Bibliographic Data

ID7761402
AuthorsSijing Ye (0000-0002-8805-8914, Beijing Normal University), Changxiu Cheng (0000-0003-2988-8915, Beijing Normal University, China; National Tibetan Plateau Data Center, China), Changqing Song (0000-0002-8449-4911, Beijing Normal University, corresponding author), Shi Shen (0000-0001-9126-229X, Beijing Normal University)
Year2021
Volume53
Issue2
Pages223-226
Publication date2021-03-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironment and Planning A Economy and Space (JOURNAL)
Journal identifiersISSN: 0308-518X • E-ISSN: 1472-3409
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/0308518x20957336
OpenAlexW3083679120
LanguageEN
Citations received3
References cited2

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

Autocorrelation · Bivariate analysis · China · Commodity · Comparative advantage · Data mining · Econometrics · Economic geography · Economics · Geography · International trade · Machine learning · Revealed comparative advantage · Spatial analysis · Statistics · Computer Science · Economic and Technological Innovation · Global Trade and Competitiveness · Mathematics · Artificial Intelligence

Tools

Open DOI