文章基本信息

标题：Poverty and Physical Geographic Factors: An Empirical Analysis of Sichuan Province Using the GWR Model
本地全文：下载
作者：Xindong He ; Xianmin Mai ; Guoqiang Shen 等
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2021
卷号：13
期号：1
页码：100
DOI：10.3390/su13010100
语种：English
出版社：MDPI, Open Access Journal
摘要：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.