XIONG Chuanlin,YE Changsheng.The Multi-Dimensional Poverty Measure and Spatial Pattern of the Former Central Soviet Area at County Level in South of Jiangxi Province[J].Research of Soil and Water Conservation,2016,23(03):225-232.
赣南等原中央苏区县域多维贫困测度及空间格局
- Title:
- The Multi-Dimensional Poverty Measure and Spatial Pattern of the Former Central Soviet Area at County Level in South of Jiangxi Province
- 关键词:
- 多维贫困; 地理识别; 脆弱性—可持续生计分析框架; 空间关联; 赣南等原中央苏区
- Keywords:
- multi-dimensional poverty; geographical identification; vulnerability and sustainable livelihood analysis framework; spatial correlation; former Central Soviet Area in south of Jiangxi Province
- 分类号:
- F323.8
- 摘要:
- 针对赣南等原中央苏区高度集中的连片贫困现象，以国际上应用广泛的脆弱性—可持续生计分析框架作为理论基础，结合农村多维发展指数（MDI）和空间关联分析方法，系统分析了赣南等原中央苏区县域多维贫困分布格局。结果表明：（1）基于多维度的指标体系和集成式识别方法更贴合多维贫困地理识别结果；（2）根据空间集聚Gi*指数值，多维发展指数存在空间自相关性；（3）研究区37个县市中有23个县级单元被识别为多维度贫困县，其中14个是国家级贫困县，且冷点区、次冷点区与多维贫困识别认定的贫困县重合率达73.91%；（4）识别的多维贫困县被划分为基础建设缺乏型、金融基建兼缺型、人力基建兼缺型、发展条件缺乏型和生存条件缺乏型5种类型。多维贫困地理识别和空间格局分析，对研究区实现精准脱贫、全面建成小康社会的目标有着积极的理论和现实意义。
- Abstract:
- On the theoretical basis of the vulnerability and sustainable livelihood analysis framework proposed by DFID which is widely applied worldwide, Multi-dimensional Development Index (MDI) and spatial correlation analysis method have been used to analyze the distribution pattern of county multidimensional poverty systematically of the former Central Soviet Area in South of Jiangxi Province for highly concentrated poverty-stricken phenomenon. The results show that: (1) it is feasible to develop acomposite Multi-dimensional Development Index (MDI) for the integrated method of geographical identification of multi-dimensional poverty; (2) Getis-Ord Gi* index values remain that spatial autocorrelation exists in MDI of South of Jiangxi Province; (3) the overlap rate of a total of 23 county-level units are identified as multi-dimensional poor counties, which have 14 country’s poor counties, and the coincidencerate of cold spots, subcold spots and the poor counties identified by the multi-dimensional poverty identification is 73.91%; (4) multi-dimensional poor counties are classified into 5 types, lack of infrastructure, lack of both financial capital and infrastructure, lack of both human capital and infrastructure, lack of development condition and lack of living condition. Multi-dimensional poverty identification and spatial correlation analysis have the positive and practical significance to achieve the goal of building a moderately prosperous society in an all-round way.
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备注/Memo
收稿日期:2015-11-07;改回日期:2015-11-22。
基金项目:江西省自然科学基金“南昌市城市街道绿化带滞尘能力研究”（20151BAB213032）
作者简介:熊传麟(1991-),男,江西九江人,硕士研究生,研究方向为区域与城市规划。E-mail:xcl186@126.com