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Poverty and Physical Geographic Factors: An Empirical Analysis of Sichuan Province Using the GWR Model

Author

Listed:
  • Xindong He

    (College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu 610059, China)

  • Xianmin Mai

    (School of Architecture, Southwest Minzu University, Chengdu 610041, China)

  • Guoqiang Shen

    (College of Architecture, Planning and Public Affairs, University of Texas at Arlington, Arlington, TX 76019, USA)

Abstract

Given the complexity of the poverty problem, efforts and policies aiming at reducing poverty should be tailored to local conditions, including cultural, economic, social, and geographic aspects. Taking the Sichuan Province of China as the study area, this paper explores the impact of physical geographic factors on poverty using the Ordinary Least Squares (OLS) Regression and the Geographically Weighted Regression (GWR) models at the county level. In total, 28 factors classified in seven groups were considered as variables, including terrain (relief degree of the land surface, altitude, and slope); vegetation (forest coverage rate); water (drainage network density); climate (temperature, annual average rainfall); and natural disaster (landslide, debris flow, and torrential flood). The 28 variables were then tested using correlations and regressions. A total of six physical variables remained significant for the OLS and GWR models. The results showed that the local GWR model was superior to the OLS regression model and, hence, more suitable for explaining the associations between the poverty rate and physical geographic features in Sichuan.

Suggested Citation

  • Xindong He & Xianmin Mai & Guoqiang Shen, 2020. "Poverty and Physical Geographic Factors: An Empirical Analysis of Sichuan Province Using the GWR Model," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2020:i:1:p:100-:d:467596
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    References listed on IDEAS

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