基于GIS的多尺度多维贫困识别

发布时间：2018-03-22 22:17

本文选题：多尺度　切入点：多维贫困　出处：《首都师范大学》2014年硕士论文　论文类型：学位论文

【摘要】：2010年,中国农村绝对贫困发生率从2000年的3.5%下降到2008年的1%,2013年,国家统计局调整绝对贫困收入标准为2300元,保守估计贫困人口还有8000万人。此外,中国的贫困人口开始向西部地区集中,山区农村贫困人口占全国农村贫困人口的比重逐年上升。贫困向老少边区的“小集中大分散”趋势越来越明显。目前,国内贫困的识别仅仅停留在依靠收入指标的基础上,贫困的表达也仅仅通过统计图表来进行表达。这样的贫困识别与测量技术并不能有效地分析贫困地区的致贫机制,不能帮助政府制定相关的扶贫政策。随着多维贫困理论逐渐成为国内外学者研究的热点,从多维分析贫困的特征已经成为近年来贫困识别与测量的焦点。此外,由于贫困具有地域性特征,所以,对多维贫困人口所处地域的可持续能力进行评估分析,是贫困地区致贫机制研究不可缺少的部分。根据阿玛蒂亚森的理论,量化贫困就必须对贫困进行识别与测量。识别的目的是为了能够划分贫困与非贫困个体；而测量的目的是通过数值指标表示贫困的程度。本文采用多尺度的研究方法,首先参考以往政府部门以及相关学者的贫困识别与测量指标体系,在衡量了研究区地域条件的情况下构建了贫困人口、村级、县级的多维贫困识别与测量指标体系。其次,在贫困人口尺度,使用“双临界值法”对研究区多维贫困人口进行识别与测量；在村级尺度,使用“参与式评估”法对村级贫困程度进行识别与测量；在县级尺度,使用“PI指数法”对县级贫困程度进行识别与测量。最后,通过维度分解分析各个维度对贫困的贡献情况,以此分析地区的致贫原因；通过GIS的空间分析方法分析地区贫困空间分布状况；基于不同尺度致贫原因的关系研究,分析贫困人口与其所处环境之间的致贫关系。实证方面,本文首先以秦巴山区南阳市四个国家级贫困县为例,在调查数据不足的情况下运用Kriging插值技术实现了对研究区多维贫困人口空间分布的模拟；其次,以武陵山区重庆市黔江区为例,对其多维贫困人口、村级贫困、县级贫困进行了识别与测量,分析研究区的致贫原因以及贫困的空间分布情况,并探讨了贫困人口与其所处环境之间的致贫关系,以此研究为政府提供政策建议。其贫困分析结果如下： 1)通过数据检验,南阳市四县多维贫困测量指标符合插值条件。结果显示：淅川县山区部分的多维贫困人口发生率最高,镇平县MPI最低,但健康对贫困的贡献度最高。 2)村级贫困主要分布在黔江区中部以及中南部,且分布较零散,聚集程度不明显。在与多维贫困人口的关系分析中,发现造成黔江区健康问题严重的原因与黔江区医疗基础设施不全,缺乏有经验的医护人员以及参与医疗保险的农户数量少有关。 3)在县级尺度上,黔江区的综合贫困指数在整个武陵山区片区县的排名较低,整体贫困较其他武陵山区片区县轻。但是,从其贫困致贫机制分析以及内部村级的贫困分布可以看出,黔江区也属于多因素致贫类型。其致贫模式为：由于区域条件差导致的区域封闭,致使信息闭塞,优厚的生态资源不能通过科学技术转换为经济效益；加之,依然以自给自足的农业经济无法满足黔江区的人口需求以及发展需求,另外,黔江区内部分行政村由于频发地质类灾害导致农业经济受损,单一的产业经济无法支持区域的发展,导致区域发展的失衡,区域差距日渐扩大。
[Abstract]:In 2010, Chinese absolute rural poverty rate dropped from 3.5% in 2000 to 1% in 2008, 2013, the National Bureau of statistics to adjust the absolute poverty income standard is 2300 yuan, a conservative estimate of poverty population and 80 million people. In addition, Chinese poverty began to focus on the western region, the poor mountainous rural areas accounted for the impoverished rural population year by year the young and old poverty to rise. "Small concentrated dispersion" trend is more and more obvious.
At present, the identification of poor domestic only in rely on income index, poor expression only by statistical charts to express. This poor recognition and measurement technology can not effectively analyze the mechanism of poverty in poor areas, can help the government formulate related poverty reduction policies. With the Multidimensional Poverty Theory has gradually become the focus of domestic foreign scholars, from the multidimensional analysis of the characters of poverty in recent years has become the focus of poverty identification and measurement. In addition, due to the poor with regional characteristics, therefore, the ability of sustainable development at the domain of Multidimensional Poverty Assessment and analysis, study on poverty in poor areas is the mechanism of the indispensable part.
According to Amartya Sen's theory, we must identify and quantify the poverty measurement of poverty. The purpose is to be able to identify into poor and non poor individuals; and the purpose of measurement is through numerical index to show the extent of poverty. This research method using multi scale, the first reference to poverty identification and measurement index system in government departments and some scholars, in the measure of the geographical conditions in the study area under construction of poverty village, Multidimensional Poverty identification and measurement index system at county level. Secondly, the poverty population scale, the use of "double threshold method" for the recognition and measurement of Multidimensional Poverty Population in the study area; village scale, the use of "participatory evaluation method for identifying and measuring on the village level poverty level; at the county scale, the county level of poverty were identified and measured using" PI index ". Finally, through The dimension of each dimension decomposition analysis of contribution to poverty, to analyze the causes of poverty area; through the GIS spatial analysis method to analyze the distribution of poverty in the region space; Study on the relationship between different scales based on the analysis of the causes of poverty, poverty and environment between poverty. The empirical aspect, firstly in the Qinba Mountain District of Nanyang City, four a national poverty county as an example, in the case of lack of survey data by using Kriging interpolation technique to realize the simulation of the spatial distribution of poverty population in the study area of multidimensional; secondly, in Qianjiang District of Chongqing city in Wuling mountainous areas as an example, the multidimensional poverty village, poverty, poverty county is the identification and measurement, analysis of the study area the causes of poverty and poverty of the spatial distribution, and discusses the poverty population and its environment in order to study the relationship between poverty and provide policy for the government The results of its poverty analysis are as follows:
1) through data inspection, Nanyang city four county of Multidimensional Poverty Measurement Indicators in line with interpolation conditions. The results show that the Multidimensional Poverty County of Xichuan mountainous area had the highest incidence, Zhenping county MPI is the lowest, but the highest contribution to poor health.
2) village poverty mainly distributed in the Qianjiang area of central and south central, and the distribution is scattered, the aggregation degree is not obvious. In the analysis of the relationship between the population and the Multidimensional Poverty, found the cause of serious health problems in Qianjiang district and Qianjiang district medical infrastructure is not complete, lack of experienced staff and participate in the medical insurance of farmers a small number of related.
3) at the county scale, comprehensive poverty index of Qianjiang District in the area of the whole county in mountainous area of Wuling ranked lower overall poverty than other area in the mountainous area of Wuling county. But the light from the poverty, poverty and internal mechanism analysis of village level poverty distribution can be seen, Qianjiang district also belong to multiple types. The model for poverty poverty due to regional conditions due to poor areas closed, resulting in the lack of information, ecological resources can not be paid by the science and technology into economic benefits; in addition, still in the self-sufficient agricultural economy can not meet the demand of Qianjiang district population and development needs, in addition, the Qianjiang district administrative villages due to frequent geological disaster caused agricultural economic damage the single industrial economy cannot support the development of the region, resulting in the imbalance of regional development, regional disparity is increasing.

【学位授予单位】：首都师范大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F323.8;F224

【参考文献】