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Open AccessArticle

Spatial Big Data Analysis of Political Risks along the Belt and Road

by 1,2,†, 3,*,† and 4,5,6
1
School of International Relations and Public Affairs, Fudan University, Shanghai 200433, China
2
Department of Politics and International Studies, University of Cambridge, Cambridge CB2 1TN, UK
3
Department of Land Economy, University of Cambridge, Cambridge CB2 1TN, UK
4
School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
5
Smart Planning and Design Collaborative Innovation Research Center, Huazhong University of Science and Technology, Wuhan 430074, China
6
Hubei New Urbanization Engineering Technology Research Center, Huazhong University of Science and Technology, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this study.
Sustainability 2019, 11(8), 2216; https://doi.org/10.3390/su11082216
Received: 7 January 2019 / Revised: 17 February 2019 / Accepted: 2 April 2019 / Published: 12 April 2019
As many regions along the Belt and Road have long been struggling with terrorist attacks, crimes, wars, and corruption, political risks pose important challenges for infrastructure projects and transnational investment. The objective of the article is to contribute to the identification of different types of political risks along the Silk Road Economic Belt and 21st Century Maritime Silk Road, and the visualization of their micro-level spatial distribution based on the Global Database of Events, Language, and Tone (GDELT) datasets from October 2013 to May 2018. By adopting the bivariate Moran’s I model to compare the distribution of political risks along the Belt and Road and that of the Chinese Belt and Road Initiative (BRI) investment and construction projects based on data from the China Global Investment Tracker (CGIT), the article also generates an overall political risk profile for Chinese BRI projects. Our findings show that a particularly high percentage of Chinese BRI projects are distributed in regions with high political risks. This research has important implications for the discussion and study of the BRI. First, by combining geographic spatial statistical analysis and political science conceptual frameworks, we point out the necessity to query the BRI from interdisciplinary perspectives grounded in empirical research. Second, the research delivers to researchers, academics, practitioners, consultants and policy makers interested in the BRI the latest insights into the risks and challenges along the Belt and Road. Third, it advocates policies and strategies conducive to identifying, assessing and mitigating political risks in investment along the Belt and Road and to strengthening the sustainable development of the BRI. View Full-Text
Keywords: Belt and Road Initiative; political risks; big data; spatial distribution; kernel density estimation; bivariate Moran’s I Belt and Road Initiative; political risks; big data; spatial distribution; kernel density estimation; bivariate Moran’s I
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Zhang, C.; Xiao, C.; Liu, H. Spatial Big Data Analysis of Political Risks along the Belt and Road. Sustainability 2019, 11, 2216.

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