CAJ | 학술논문

本文使用K-means聚类方法对我国农村贫困地区的贫困人口进行聚类,并进一步分析了特殊类型贫困地区集中连片贫困地区的贫困类型结构分析结果表明,贫困类型的分布呈现了扶贫对象在区域间分布的不平衡性,各种贫困类型的不同特点和区域分布上的差异从一个视角揭示了收入差距特别是贫困程度差异化的来源尤其是,少数民族地区的贫困特征和贫困人口比重都要比老区和边境县地区更加突出,而这些地区有着自身独特的特点和性质,尤其需要对少数民族地区贫困背后的形成机制开展更加深入的研究,以便提出针对少数民族地区的因地制宜的扶贫开发措施连片特困地区的主导贫困类型各不相同,意味着片区扶贫开发需要具有片区针对性的扶贫政策尽管聚类分析只是一种探索性分析,但是农村贫困人口的聚类仍然为我们定义各种贫困的类型以及它们在不同区域或特定区域划分之间的内部分布结构提供了非常有价值的信息,并将为进一步的统计推断分析提供基础。
본문사용K-means취류방법대아국농촌빈곤지구적빈곤인구진행취류,병진일보분석료특수류형빈곤지구집중련편빈곤지구적빈곤류형결구분석결과표명,빈곤류형적분포정현료부빈대상재구역간분포적불평형성,각충빈곤류형적불동특점화구역분포상적차이종일개시각게시료수입차거특별시빈곤정도차이화적래원우기시,소수민족지구적빈곤특정화빈곤인구비중도요비로구화변경현지구경가돌출,이저사지구유착자신독특적특점화성질,우기수요대소수민족지구빈곤배후적형성궤제개전경가심입적연구,이편제출침대소수민족지구적인지제의적부빈개발조시련편특곤지구적주도빈곤류형각불상동,의미착편구부빈개발수요구유편구침대성적부빈정책진관취류분석지시일충탐색성분석,단시농촌빈곤인구적취류잉연위아문정의각충빈곤적류형이급타문재불동구역혹특정구역화분지간적내부분포결구제공료비상유개치적신식,병장위진일보적통계추단분석제공기출。
Using the method of K-means clustering, this paper makes the classification poverty population in rural China and thus the analysis of structure of poverty types in areas of special types of poverty and in contiguous poverty areas. The outcomes show that the targeted poor are disproportionately distributed among regions and the features of different types and their regional distribution can be treated as sources of income inequality especially the poverty levels. In particular, poverty characteristics are more notable and the poverty is lager in population in ethnic minority areas than those in old revolutionary base areas and border regions, which implicates that further research is required to explore the hiding mechanism causing poverty in ethnic minority areas so as to put forward poverty alleviation and development measures accommodating to local condition. Also, the leading poverty type is different among contiguous poverty-stricken areas, so that targeted policies are needed. Though clustering is mainly deemed as exploratory analysis, the clustering of rural poverty population still helps to make classifications and definitions of various types of poverty and the internal structure and regional distribution of these poverty types, which can contribute to further statistical inferences and causal analysis.