GEOGRAPHICAL RESEARCH ›› 2019, Vol. 38 ›› Issue (6): 1389-1402.doi: 10.11821/dlyj020180024

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Analyzing spatial distribution of poor villages and their poverty contributing factors: A case study from Wumeng Mountain Area

Chenxia LIANG1,2,3,4(), Yanhui WANG2,3,4(), Haitao XU5, Wenping QI2,3,4, Xu CHENG1, Wenji ZHAO2,3,4   

  1. 1. CETC Big Data Research Institute Co., Ltd., Guiyang 550081, China
    2. Beijing Key Laboratory of Resource Environment and Geographic Information System, Capital Normal University, Beijing 100048, China
    3. Key Laboratory of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China
    4. State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China
    5. College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
  • Received:2018-01-02 Revised:2018-06-21 Online:2019-06-20 Published:2019-06-12


Current research mainly focuses on large scale and ignores individual effect or background effect in the exploration of poverty contributing factors. Based on the perspective of spatial poverty, this paper, taking the Wumeng Mountain Area as an example and poor villages as the research object, uses spatial point pattern method to explore the spatial distribution characteristics of the poor villages and designs multi-level linear regression models to comprehensively and quantitatively analyze the poverty contributing factors at both village and county levels. The results were concluded as follows. (1) The spatial clustering characteristics of the poor villages in the study area were obvious. The overall distribution showed a spatial pattern of both large scatters and small concentrations, and scatter points - polar core - axis-cluster coexisted. (2) The poverty degree of poor villages was significantly affected by multilevel factors. The village-level factors were: population density, road access ratio, labor force ratio, frequency of suffered natural disasters, and safe drinking water ratio. The county-level factors were: per capita GDP, second gross enrollment ratio, and vegetation coverage. (3) The rural poverty in the study area came from the dual role of poor villages and counties. Hence, the government and relevant departments can take targeted measures according to different scales in the poverty alleviation, and allocate funds reasonably for poverty relief.

Key words: poor villages, spatial poverty, spatial distribution, contributing factors, Wumeng Mountain Area, multilevel linear regression model