地理研究 ›› 2018, Vol. 37 ›› Issue (3): 593-606.doi: 10.11821/dlyj201803011

• 研究论文 • 上一篇    下一篇

县域农村贫困化空间分异及其影响因素——以陕西山阳县为例

武鹏(), 李同昇(), 李卫民   

  1. 西北大学城市与环境学院,西安 710127
  • 收稿日期:2017-11-03 修回日期:2018-01-26 出版日期:2018-03-15 发布日期:2018-04-25
  • 作者简介:

    作者简介:武鹏(1993- ),男,山西介休人,硕士,主要从事农村贫困与区域发展研究。E-mail:wupengeo@163.com

  • 基金资助:
    国家自然科学基金项目(41771129);2016年度陕西高校智库建设项目

Spatial differentiation and influencing factors analysis of rural poverty at county scale: A case study of Shanyang county in Shaanxi province, China

Peng WU(), Tongsheng LI(), Weimin LI   

  1. College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China
  • Received:2017-11-03 Revised:2018-01-26 Online:2018-03-15 Published:2018-04-25
  • About author:

    Author: Shi Zhenqin (1988-), PhD, specialized in regional development and land space management in mountain areas. E-mail: kevinszq@163.com

    *Corresponding author: Deng Wei (1957-), Professor, specialized in mountain environment and regional development.

    E-mail: dengwei@imde.ac.cn

摘要:

以国家扶贫开发重点县山阳县为研究区,通过空间自相关分析和分组分析方法探究山阳县农村贫困化的空间格局和类型;利用逐步回归、地理加权回归和地理探测器模型对山阳县农村贫困化影响因素进行分析,讨论影响因素效应水平的空间异质性及其交互作用。研究表明:① 山阳县农村贫困发生率具有较强的空间集聚性,形成6个热点集聚区和4个冷点集聚区;综合考虑农村贫困程度和空间连接性,将山阳县划分为低度贫困区、中度贫困区和高度贫困区。② 水网密度、到最近公路的距离、危房比例、农民人均可支配收入、外出务工人数比例、农户入社比例6个因素是山阳县农村贫困化的主要影响因素,各因素的影响效应具有空间异质性。③ 两因素交互作用要比单因素作用于贫困发生率时影响力更显著,各主要影响因素的交互作用类型有双因子增强型和非线性增强型两种。

关键词: 农村贫困化, 影响因素, 空间异质性, 地理加权回归, 地理探测器, 陕西山阳县

Abstract:

Poverty is still remaining as the most prominent "short-board" in the Chinese economic development. The hardest and heaviest part of building well-off society in an all-around way lies in the rural construction, especially in the poverty-stricken area. The poverty alleviation and development in China is at the most critical stage, which requires more accurate recognition for the spatial differentiation of the rural poverty and its influencing factors, to make sure the exact targeting of the poverty alleviation policies and measures. This paper picked Shanyang, a key county in the poverty alleviation and development project of China, to explore the spatial pattern and type of the rural poverty of this county through the spatial autocorrelation analysis and grouping analysis. The stepwise regression, geographically weighted regression and Geodetector models were used to analyze the influencing factors of the rural poverty in this county, followed with the discussion on the spatial heterogeneity and interaction of the influencing effects. The following findings were concluded from the research: (1) The incidence of rural poverty in Shanyang noticeably clustered in space, forming 6 hot spots and 4 cold spots. In terms of rural poverty degree and spatial connectivity, the county was divided into low poverty area, mid poverty area and high poverty area. The space distribution was based on the regional poverty degree to facilitate a proper implementation of the poverty alleviation policies. (2) The major influencing factors responsible for the rural poverty in Shanyang included water network density, the distance to the nearest highway, proportion of dilapidated buildings, disposable income per rural capita, proportion of migrant workers and the proportion of rural households participating in the agricultural cooperatives. The influencing effects of all factors featured the spatial heterogeneity. Water network density and rural disposable income per capita were negatively correlated with the incidence of poverty while the rest factors showed a positive correlation. (3) The influence of the interaction between two factors appeared to be larger than that of the single factor. The interaction modes between major factors included bi-factor enhancement and nonlinear enhancement. Due to the interaction enhancement effects between poverty factors, the poverty alleviation policies shall be comprehensively matched to realize the expected target, along with a powerful poverty alleviation security system to ensure the full implementation.

Key words: rural poverty, influencing factors, spatial heterogeneity, geographically weighted regression (GWR), Geodetector, Shanyang county