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2000—2020年云南省人口收缩的空间特征与成因分析

Analysis on the Spatial Characteristics and Causes of Population Shrinkage in Yunnan Province from 2000 to 2020
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摘要 基于云南省第五、六、七次人口普查数据,本文首次在县市和街镇空间尺度下对云南省人口收缩的空间特征和影响因素展开分析。研究发现:(1)自2000年以来,全省及省内各地区的人口分布的集中化趋势不断加强,人口增长与收缩区域的分化特征日益明显;(2)相比于2000—2010年,2010—2020年省内人口收缩的地区在县市和街镇尺度上不仅表现出显著的扩张趋势,而且收缩程度也在不断加剧;(3)根据2010—2020年街镇尺度下人口收缩地区的空间分布,全省人口变化呈现出明显的“核心增长—边缘收缩”特征,县级城市(镇)是目前省内各地区人口的主要增长点;(4)从回归分析的结果来看,相较于地区的经济发展水平,以教育、医疗为代表的公共服务设施供给水平在人口集聚方面作用更加突出,未来可能会成为影响区域之间人口迁移的关键。 Based on the data of the fifth,sixth and seventh population censuses in Yunnan Province,this paper analyzes the spatial characteristics and influencing factors of population contraction in Yunnan province at the county and town spatial scales for the first time.The results are as follows:(1)Since 2000,the centralization trend of population distribution has been strengthened,and the differentiation of population growth and contraction region has become more and more obvious.(2)Compared with 2000-2010,the areas with the shrinking population in 2010-2020 doesn't only show a significant trend of expansion at county and town scales,but also showed an increasingly severe degree of contraction.(3)According to the spatial distribution of population shrinking regions at the town scale from 2010 to 2020,the population change of the whole province presents an obvious feature of'core growth and marginal contraction'.At present,county-level cities(towns)are the main growth point of population in each region of the province.(4)According to the results of regression analysis,compared with the level of regional economic development,the supply level of public service facilities represented by education and medical care plays a more prominent role in population agglomeration,which may become the key to affecting population migration between regions in the future.
作者 童思永 苏振宇 程联欢 TONG Siyong;SU Zhenyu;CHENG Lianhuan
出处 《小城镇建设》 2023年第5期61-69,共9页 DEVELOPMENT OF SMALL CITIES & TOWNS
关键词 人口收缩 空间特征 影响因素 回归分析 云南省 population shrinkage spatial characteristic influencing factor regression analysis Yunnan Province
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