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地理研究    2018, Vol. 37 Issue (3): 593-606     DOI: 10.11821/dlyj201803011
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
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
西北大学城市与环境学院,西安 710127
Spatial differentiation and influencing factors analysis of rural poverty at county scale: A case study of Shanyang county in Shaanxi province, China
WU Peng(),LI Tongsheng(),LI Weimin
College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China
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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。

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武鹏
李同昇
李卫民
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 wordsrural poverty    influencing factors    spatial heterogeneity    geographically weighted regression (GWR)    Geodetector    Shanyang county
收稿日期: 2017-11-03      出版日期: 2018-04-25
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
引用本文:   
武鹏, 李同昇, 李卫民 . 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[J]. 地理研究, 2018, 37(3): 593-606.
WU Peng, LI Tongsheng, LI Weimin . Spatial differentiation and influencing factors analysis of rural poverty at county scale: A case study of Shanyang county in Shaanxi province, China[J]. GEOGRAPHICAL RESEARCH, 2018, 37(3): 593-606.
链接本文:  
http://www.dlyj.ac.cn/CN/10.11821/dlyj201803011      或      http://www.dlyj.ac.cn/CN/Y2018/V37/I3/593
Fig. 1  研究区位置及陕西山阳县行政区范围
图示 判据 交互作用
q(X1∩X2) < Min(q(X1), q(X2)) 非线性减弱
Min(q(X1), q(X2))<q(X1∩ X2)<Max(q(X1), q(X2)) 单因子非线性减弱
q(X1∩ X2) > Max(q(X1), q(X2)) 双因子增强
q(X1∩ X2) = q(X1)+q(X2) 独立
q(X1∩ X2) > q(X1)+q(X2) 非线性增强
Tab.1  两个自变量对因变量交互作用的类型
Fig. 2  2015年陕西山阳县贫困发生率
Fig. 3  山阳县贫困发生率热点图
Fig. 4  山阳县贫困发生率聚类和异常值分布图
Fig. 5  山阳县农村贫困化分组分析结果
类别 行政村数量(个) 贫困发生率均值(%) Moran's I Z得分
研究区整体 238 24.6 0.47 12.52
低度贫困区 74 18.0 0.20 4.33
中度贫困区 123 26.2 0.23 4.95
高度贫困区 41 31.6 0.01 0.31
Tab.2  分组分析结果统计
维度 指标 单位 计算方法
自然环境特征 海拔 m 30 m×30 m 山阳县DEM
坡度 ArcGIS坡度分析
地形起伏度 m ArcGIS栅格统计
水网密度 km/km2 ArcGIS密度分析
人均耕地面积 亩/人 耕地面积/行政村总人口
地理区位特征 到县中心的距离 km ArcGIS邻域分析
到最近乡镇中心的距离 km ArcGIS邻域分析
到最近公路的距离 km ArcGIS邻域分析
经济因素 农民人均可支配收入 统计数据
危房比例 % 危房户数/行政村总户数
社会因素 外出务工人数比例 % 外出务工人数/行政村总人数
残疾人比例 % 残疾人人数/行政村总人数
农户入社比例 % 农民专业合作社入社户数/行政村总户数
Tab.3  变量和指标说明
变量 非标准化系数 标准系数 t Sig. VIF
危房比例 0.538 0.611 14.041 0.000 1.443
农民人均可支配收入 -0.001 -0.194 -4.530 0.000 1.392
外出务工人数比例 0.242 0.140 3.815 0.000 1.033
到最近公路的距离 0.723 0.128 3.401 0.001 1.086
水网密度 -2.372 -0.086 -2.326 0.021 1.036
农户入社比例 0.057 0.079 2.084 0.038 1.085
常数项 22.512 8.079 0.000
R2 0.835
校正R2 0.697
F 88.496
Sig. 0.000
Tab.4  逐步回归模型运算结果
分区 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 常数项
低度贫困区 -2.4157 0.0007 0.5478 -0.0011 0.2483 0.0471 22.5960
中度贫困区 -2.3897 0.0008 0.5498 -0.0011 0.2415 0.0654 22.2734
高度贫困区 -3.0607 0.0006 0.5280 -0.0010 0.2495 0.0429 22.7668
Tab.6  影响因素回归系数均值分区统计
因素 均值 最小值 上四分位值 中位值 下四分位值 最大值
水网密度 -2.5134 -3.4385 -2.8068 -2.4314 -2.2079 -1.7251
到最近公路的距离 0.0007 0.0005 0.0006 0.0007 0.0008 0.0009
危房比例 0.5454 0.5089 0.5366 0.5468 0.5537 0.5759
农民人均可支配收入 -0.0011 -0.0011 -0.0011 -0.0011 -0.0010 -0.0009
外出务工人数比例 0.2450 0.2214 0.2404 0.2484 0.2515 0.2559
农户入社比例 0.0559 0.0314 0.0436 0.0540 0.0671 0.0884
常数项 22.4587 21.5827 22.2273 22.5621 22.7192 22.9751
带宽 47465.7089
AICc 1446.8583
R2 0.7092
校正R2 0.6931
Tab.5  GWR模型运算结果
Fig. 6  GWR模型影响因素回归系数的空间分布
因素 研究区整体 低度贫困区 中度贫困区 高度贫困区
水网密度∩到最近公路的距离 NE NE NE NE
水网密度∩农民人均可支配收入 NE NE NE NE
水网密度∩危房比例 NE NE NE NE
水网密度∩外出务工人数比例 NE NE NE NE
水网密度∩农户入社比例 NE NE NE NE
到最近公路的距离∩农民人均可支配收入 BE NE BE NE
到最近公路的距离∩危房比例 BE NE NE NE
到最近公路的距离∩外出务工人数比例 NE NE NE NE
到最近公路的距离∩农户入社比例 NE NE NE NE
农民人均可支配收入∩危房比例 BE NE BE NE
农民人均可支配收入∩外出务工人数比例 NE NE NE NE
农民人均可支配收入∩农户入社比例 NE NE BE NE
危房比例∩外出务工人数比例 NE NE NE NE
危房比例∩农户入社比例 NE NE BE NE
外出务工人数比例∩农户入社比例 NE NE NE NE
Tab.7  交互作用探测结果
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