地理研究 ›› 2016, Vol. 35 ›› Issue (12): 2298-2308.doi: 10.11821/dlyj201612009

• 研究论文 • 上一篇    下一篇

中国贫困村测度与空间分布特征分析

陈烨烽1,2,3(), 王艳慧1,2,3(), 王小林4   

  1. 1. 首都师范大学资源环境与地理信息系统北京市重点实验室,北京 100048
    2. 首都师范大学三维信息获取与应用教育部重点实验室,北京 100048
    3. 首都师范大学城市环境过程与数字模拟国家重点实验室培育基地,北京 100048
    4. 国务院扶贫办信息中心,北京 100028
  • 收稿日期:2016-06-05 修回日期:2016-09-22 出版日期:2016-12-23 发布日期:2017-01-05
  • 作者简介:

    作者简介:陈烨烽(1991- ),男,浙江绍兴人,硕士,主要从事GIS方法与应用研究。E-mail:13648313352@163.com

  • 基金资助:
    国家自然科学基金项目(40701147);北京市自然科学基金项目(8132018);“十二五”国家科技支撑计划项目(2012BAH33B00,2012BAH33B03,2012BAH33B05)

Measurement and spatial analysis of poverty-strickenvillages in China

Yefeng CHEN1,2,3(), Yanhui WANG1,2,3(), Xiaolin WANG4   

  1. 1. Beijing Key Laboratory of Resource Environment and Geographic Information System, Capital Normal University, Beijing 100048, China
    2. Key Laboratory of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China
    3. State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, CapitalNormal University, Beijing 100048, China
    4. Information Center of the State Council LeadingGroup Office of Poverty Alleviation and Development, Beijing 100028, China
  • Received:2016-06-05 Revised:2016-09-22 Online:2016-12-23 Published:2017-01-05

摘要:

农村贫困问题是中国全面建成小康社会的主要障碍之一。面向当前国家瞄准贫困村和贫困人口的精准脱贫战略需求,基于“十二五”期间全国贫困村“整村推进”项目村数据,构建村级多维贫困综合测度模型,并利用加权核密度模型、空间自相关方法等,从不同尺度、不同视角系统测度并分析了研究区贫困村的相对贫困特征。结果表明:① 贫困程度上,“十二五”期间贫困村呈中间大,两头小的“橄榄型”结构,省级、县级尺度下贫困深度与当地经济、区位、政策、自然环境等因素相关;② 空间分布上,全国贫困村分布呈现出东部和西北部稀疏、中部和西南部密集的“夹层”形空间异质性格局,同时存在多个不同量级、呈“星点”式分布的贫困核心;③ 中国贫困村的多维贫困存在较强的全局空间依赖性,局部呈现为高—高区与低—低区集中式分布、高—低区与低—高区离散夹杂式分布,且整体表现为西高东低的“阶梯状”格局。

关键词: 贫困村, GIS, 多维贫困指数, 空间自相关, 加权核密度估计

Abstract:

Rural poverty is one of the most important obstacles to achieve the goal that builds a moderately prosperous society in an all-around way in China. To address the strategic requirement of targeted poverty reduction for the implementation of "Entire-village Advancement" to rid all Chinese of poverty by 2020, we integrate geographical contributing factors and socioeconomic ones to design a set of village-level evaluation indicator system, consisting of 6 dimensions and 20 indicators. And then based on the above indicator system, we build a comprehensive village-level poverty measurement model, using village-level archived "Entire-village Advancement" dataset that was collected during the "12th Five-year Plan" period (from 2011 to 2015) to assess multidimensional poverty index and relative poverty characteristics of each village. At last, we adopt spatial autocorrelation and weighted kernel density estimation to examine spatial distribution of the poverty-stricken villages at a multiscale of area-province-county. The results show that: (1) During the "12th Five-year Plan" period, the distribution of poverty levels for different villages statistically represents a geometrical olive-shape pattern that is larger in the middle and smaller at both ends; Meanwhile, the poverty depths of poverty-stricken villages at both provincial and county scales are closely related to their local economies, regional locations, local policies and natural environment, etc. (2) As far as the distribution of poverty-stricken villages is concerned, there exists a spatially heterogeneous distribution, presenting a typical sandwich-shaped structure that is sparse in both eastern and northwestern China while dense in both central and southwestern China, where different levels of dotted poverty kernels are scattered, and 3 first-level kernels, 6 secondary kernels and many tertiary kernels of poverty hotspots exist obviously. However, a grey poverty area called "transition zone", located between High-High and Low-Low poverty-stricken areas, is found in central China where the distribution of poverty-stricken villages does not show a significant spatial autocorrelation. (3) The multidimensional characteristics of poverty-stricken villages represent a globally strong spatial dependence with a Moran's I coefficient of 0. 55; however, both High-High areas and Low-Low areas are distributed intensively while both High-Low areas and Low-High areas are distributed discretely, overall showing a stepped distribution pattern that is "west-high vs. east-low". This study could help precisely target the poverty situation of the poverty-stricken villages in rural China, and also may provide a good understanding of the status and regional differences among villages at a village scale. On the other hand, it could serve as a scientific reference regarding decisions-making in both promoting intra-rural anti-poverty harmonious development and in constructing the new countryside of China.

Key words: poverty-stricken villages, GIS, multidimensional poverty index, spatial autocorrelation, Weighted Kernel Density Estimation