Study on the spatial distribution and influential factors of rural poverty in Xinjiang

doi:10.3969/j.issn.1004-9479.2020.03.2018504

Abstract

Abstract:

Under the background of the country's policy of promoting precise poverty alleviation, it is of great significance to analyze the differences in the causes of poverty in different regions. This paper takes the Wasteland town of Shache County, Xinjiang Uygur Autonomous Region as the research area, and explores the spatial distribution and influencing factors of the poor population and state in the region through geo-detectors and the geographically weighted regression models. The results show that: 1) the rural poverty in the wasteland town The characteristics of agglomeration distribution within a certain range, the incidence of poverty in the central and northern regions is significantly higher than that in the surrounding areas, and the poverty-stricken areas in the central part spread from the center to the periphery, and the incidence of poverty in the north is lower than that in the central part. 2) The main factors leading to poverty include the distance from the main road, the population density and the proportion of the male population. 3) The distance from the main road has a positive correlation with the incidence of poverty. The distance from the town center has a negative correlation with the incidence of poverty. The population density and the incidence of poverty show a significant negative correlation. relationship. 4) According to the dominant factors of poverty formation in different regions, poverty-stricken areas can be divided into road-constrained and population-constrained types, of which road constraints are mainly distributed in the southeastern part of the wasteland town, while population constraints are mainly distributed in the northern region. In view of the rural poverty of different causes, this paper proposes that poverty alleviation measures should be adopted with different strategies.

Key words: poverty incidence, spatial pattern, geographic detector, geographically weighted regression model

摘要：

在国家大力推进精准扶贫的政策背景下，分析不同地区贫困成因的差异性具有重要意义。本文以新疆维吾尔自治区莎车县荒地镇为研究区，通过地理探测器和地理加权回归模型探究该区贫困人口和状态的空间分布及其影响因素，研究结果表明：1)荒地镇农村贫困化呈现一定范围内集聚分布的特征，镇域中部和北部地区的贫困发生率明显高于其周边地区，中部的贫困地区由中心向四周扩散，北部的贫困发生率低于中部。2)导致贫困的主要因素包括距主干道距离、人口密度和男性人口所占比例。3)距离主干道的距离与贫困发生率呈现出明显的正向相关的关系，距离镇中心的距离与贫困发生率呈现出一定的负相关关系，人口密度与贫困发生率呈现出明显的负相关关系。4)依据不同地区贫困形成的主导因素，贫困地区可以划分为道路制约型和人口制约型，其中道路制约性主要分布在荒地镇的东南地区，而人口制约性主要分布在北部地区。针对不同成因的农村贫困，本文提出应该采用不同策略的扶贫措施。

关键词: 贫困发生率, 空间格局, 地理探测器, 地理加权回归模型

Ying MENG, Yue‘e CAO. Study on the spatial distribution and influential factors of rural poverty in Xinjiang[J]. World Regional Studies, 2020, 29(3): 650-658.

孟颖, 曹月娥. 新疆农村贫困化空间分布及影响因素探究[J]. 世界地理研究, 2020, 29(3): 650-658.

Figures/Tables 6

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因素	x₁	x₂	x₃	x₄	x₅
-4	0.253484	0.122671	0.495225	0.286604	0.394040
-3	0.325976	0.596389	0.806350	0.313992	0.316287
-2	0.516576	0.544444	0.869652	0.479241	0.431571
-1	0.176480	0.540275	0.422565	0.381928	0.556950
0	0.376502	0.373952	0.201208	0.416112	0.447511
1	0.306535	0.592762	0.269150	0.715305	0.474602
2	0.315589	0.326469	0.294282	0.423757	0.836066
3	0.815412	0.457110	0.448672	0.499221	0.043750
4	0.919845	0.079031	0.068280	0.487234	0.122671

因素	x₁	x₂	x₃	x₄	x₅
-4	0.253484	0.122671	0.495225	0.286604	0.394040
-3	0.325976	0.596389	0.806350	0.313992	0.316287
-2	0.516576	0.544444	0.869652	0.479241	0.431571
-1	0.176480	0.540275	0.422565	0.381928	0.556950
0	0.376502	0.373952	0.201208	0.416112	0.447511
1	0.306535	0.592762	0.269150	0.715305	0.474602
2	0.315589	0.326469	0.294282	0.423757	0.836066
3	0.815412	0.457110	0.448672	0.499221	0.043750
4	0.919845	0.079031	0.068280	0.487234	0.122671

交互探测因素	交互探测值	交互探测类型
x₁ ∩ x₂	0.918134546	非线性增强
x₁ ∩ x₃	0.89230337	非线性增强
x₁ ∩ x₄	0.897833907	非线性增强
x₁ ∩ x₅	0.991081105	非线性增强
x₂∩ x₃	0.981178821	非线性增强
x₂∩ x₄	0.641942575	非线性增强
x₂ ∩ x₅	0.800203464	非线性增强
x₃ ∩ x=	0.87039336	非线性增强
x₃ ∩ x₅	0.945411295	非线性增强
x₄ ∩ x₅	0.94516175	非线性增强

交互探测因素	交互探测值	交互探测类型
x₁ ∩ x₂	0.918134546	非线性增强
x₁ ∩ x₃	0.89230337	非线性增强
x₁ ∩ x₄	0.897833907	非线性增强
x₁ ∩ x₅	0.991081105	非线性增强
x₂∩ x₃	0.981178821	非线性增强
x₂∩ x₄	0.641942575	非线性增强
x₂ ∩ x₅	0.800203464	非线性增强
x₃ ∩ x=	0.87039336	非线性增强
x₃ ∩ x₅	0.945411295	非线性增强
x₄ ∩ x₅	0.94516175	非线性增强

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