| 贫困村空间分布及影响因素分析——以乌蒙山连片特困区为例 |
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| 引用本文： | 梁晨霞,王艳慧,徐海涛,齐文平,程序,赵文吉.贫困村空间分布及影响因素分析——以乌蒙山连片特困区为例[J].地理研究,2019,38(6):1389-1402. |
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| 作者姓名： | 梁晨霞 王艳慧 徐海涛 齐文平 程序 赵文吉 |
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| 作者单位： | 1. 中电科大数据研究院有限公司,贵阳 5500812. 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 1000483. 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 1000484. 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 1000485. 成都理工大学地球科学学院,成都 610059 |
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| 基金项目： | 国家自然科学基金项目（41771157）;国家重点研发计划项目（2018YFB0505400）;北京市长城学者资助项目（CIT&TCD20190328）;全国统计科学研究重点项目（2018LZ27）;北京市教委科研计划一般项目（KM201810028014）;首都师范大学青年燕京学者项目;首都师范大学科技创新服务能力建设-基本科研业务费（科研类）（19530050178）;1.落实国家资助政策;2.加大贫困生的资助力度;3.增建学校 |
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| 摘 要： | 针对目前贫困分布研究以大尺度为主而缺乏对小尺度的关注和致贫因素分析忽略个体效应或背景效应的不足,基于空间贫困视角,以乌蒙山片区为研究区域、贫困村为研究对象,运用空间点模式分析方法探究贫困村空间分布特征,并设计多层线性回归模型从贫困村和县域两个层面综合定量剖析贫困影响因素。研究发现：① 贫困村的空间聚集特征较为明显,总体分布呈现大分散小集中、散点-极核-轴带-团块并存的空间格局。② 贫困村的贫困程度受多层因素的显著影响。其中,村级影响因素为：人口密度、通路率、劳动力比例、遭受自然灾害频次、安全饮用水比例;县级影响因素为：人均地方生产总值、高中阶段毛入学率、植被覆盖率。③ 农村贫困来源于贫困村与县域的双重作用。因此在精准扶贫工作中,政府及相关部门可针对不同尺度对象有针对性地施策,合理配置扶贫资金。
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| 关 键 词： | 贫困村 空间贫困 空间分布 影响因素 乌蒙山片区 多层线性回归模型（HLM） |
| 收稿时间： | 2018-01-02 |
| 修稿时间： | 2018-06-21 |
Analyzing spatial distribution of poor villages and their poverty contributing factors: A case study from Wumeng Mountain Area |
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| Chenxia LIANG,Yanhui WANG,Haitao XU,Wenping QI,Xu CHENG,Wenji ZHAO.Analyzing spatial distribution of poor villages and their poverty contributing factors: A case study from Wumeng Mountain Area[J].Geographical Research,2019,38(6):1389-1402. |
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| Authors: | Chenxia LIANG Yanhui WANG Haitao XU Wenping QI Xu CHENG Wenji ZHAO |
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| Abstract: | 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. |
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| Keywords: | poor villages spatial poverty spatial distribution contributing factors Wumeng Mountain Area multilevel linear regression model |
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