基于人口流的中国省域城镇化空间关联特征及形成机制
Characteristics and formation mechanism of China's provincial urbanization spatial correlation based on population flow
查看参考文献41篇
刘帅宾
1,2,3,4
杨山
1,2,3,4
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王钊
1,2,3,4
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文摘
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空间关联是区域相互作用和影响的纽带,以人口流动为载体的城镇化空间关联影响并不断调整着中国区域经济社会发展格局。根据省际人口流动数据,构建城镇化空间关联模型,通过分析1990-2015年中国省域城镇化空间关联强度与关联网络结构变化,揭示城镇化空间关联特征及形成机制。结果发现:①中国省域城镇化空间关联强度演变过程可以解析出7种类型,其中主导类型与全国的吻合程度最高,并决定着全国城镇化空间关联强度演变过程;非主导类型的城镇化空间关联强度演变特征以2005年为界,之前增长缓慢甚至为负,之后与主导类型共同推动全国城镇化空间关联强度持续增长。②对城镇化空间关联进行不同尺度的结构分析,全国城镇化空间关联网络结构日趋复杂,关联路径由集中转向分散,不同属性的地域子群间关联密度差异开始缩小,高等级节点由东部向中西部扩散。③采用面板向量自回归和面板分位数回归检验城镇化空间关联强度与关联网络中心度的互动机制,并解释了居民收入水平和区域产业结构对城镇化空间关联特征的影响方式。城镇化空间关联不仅深化了城镇化的研究,也为中国人口城镇化发展和产业空间转移调控提供理论依据。 |
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其他语种文摘
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Spatial correlation is the link of regional interaction and influence; the urbanization spatial correlation is spatial relationship of urbanization based on population flow, which influences and continuously adjusts the regional economic and social development pattern in China. By analyzing the change of the spatial correlation intensity and the spatial correlation network of provincial urbanization in China during 1990- 2015, this paper reveals the spatial correlation characteristics, formation process and influencing factors of China's provincial urbanization since China's reform and opening up. The results show that: (1) 7 types of spatial correlation intensity are identified in the evolution of provincial urbanization of China, among which the dominant type has the highest degree of coincidence with the whole country, and determines the evolution process of the spatial correlation intensity of urbanization in China. The non-dominant types of urbanization spatial correlation intensity evolution feature 2005 as the critical year, and their growth was slow or even negative before the year; then together with the dominant type, they have promoted a continuous increase in the urbanization spatial correlation intensity across the country. (2) Urbanization spatial correlation network structure characteristics are analyzed from national, regional and provincial scales. The structure of the spatial correlation network of urbanization is becoming more and more complicated, and the correlation paths have shifted from centralized to scattered. The differences in the correlation density between regional subgroups with different attributes have begun to shrink, and the highlevel nodes have spread from the eastern to the central and western regions. (3) Further, the panel- vector auto- regression and panel quantile regression are used to test the interaction mechanism between urbanization spatial correlation intensity and correlation network centrality, which explains the effect of residents' income level and regional industrial structure on urbanization spatial correlation characteristics. The specialization of urbanization spatial correlation not only deepens the research on urbanization theory and methodology, but also provides a theoretical basis for the development of population urbanization and the adjustment of industrial space in China. |
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来源
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地理学报
,2019,74(4):648-663 【核心库】
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DOI
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10.11821/dlxb201904003
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关键词
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省域城镇化
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省际人口流动
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城镇化空间关联
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空间关联强度
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关联网络中心度
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地址
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1.
南京师范大学地理科学学院, 南京, 210023
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江苏省地理信息资源开发与利用协同创新中心, 江苏省地理信息资源开发与利用协同创新中心, 南京, 210023
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南京师范大学, 虚拟地理环境教育部重点实验室, 南京, 210023
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江苏省地理环境演化国家重点实验室培育建设点, 江苏省地理环境演化国家重点实验室培育建设点, 南京, 210023
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0375-5444 |
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学科
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社会科学总论 |
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基金
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国家自然科学基金项目
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江苏高校优势学科建设工程
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文献收藏号
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CSCD:6479064
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参考文献 共
41
共3页
|
|
1.
Tobler W A. Computer movie simulating urban growth in the Detroit region.
Economic Geography,1970,46(2):234-240
|
被引
590
次
|
|
|
|
|
2.
李小文. 地理学第-定律与时空邻近度的提出.
自然杂志,2006,29(2):69-71
|
被引
4
次
|
|
|
|
|
3.
魏冶. 春运人口流动透视的转型期中国城市网络结构.
地理科学,2016,36(11):1654-1660
|
被引
47
次
|
|
|
|
|
4.
蒋小荣. 基于上市公司数据的中国城市网络空间结构.
城市规划,2017,41(6):18-26
|
被引
15
次
|
|
|
|
|
5.
Dai L. The evolving structure of the Southeast Asian air transport network through the lens of complex networks, 1979-2012.
Journal of Transport Geography,2018,68:67-77
|
被引
11
次
|
|
|
|
|
6.
Matthiessen C W. World cities of scientific knowledge: Systems, networks and potential dynamics. An analysis based on bibliometric indicators.
Urban Studies,2010,47(9):1879-1897
|
被引
51
次
|
|
|
|
|
7.
刘望保. 基于ICT的中国城市间人口日常流动空间格局——以百度迁徙为例.
地理学报,2016,71(10):1667-1679
|
被引
60
次
|
|
|
|
|
8.
杨传开. 中国省际人口迁移格局演变及其对城镇化发展的影响.
地理研究,2015,34(8):1492-1506
|
被引
48
次
|
|
|
|
|
9.
徐素. 基于人口流动的区域城镇化特征及差异化策略探讨:苏浙皖地区的讨论.
城市发展研究,2015,22(8):46-54
|
被引
2
次
|
|
|
|
|
10.
王亚力. 基于流动人口特征的环洞庭湖区县域城镇化的水平和性质分析.
长江流域资源与环境,2014,23(11):1519-1525
|
被引
4
次
|
|
|
|
|
11.
Castells M.
The Rise of the Network Society,1996
|
被引
156
次
|
|
|
|
|
12.
Taylor P.
World City Network: A Global Urban Analysis,2004
|
被引
5
次
|
|
|
|
|
13.
Sanderson M R. Are world cities also world immigrant cities? An international, crosscity analysis of global centrality and immigration.
International Journal of Comparative Sociology,2015,56:173-197
|
被引
2
次
|
|
|
|
|
14.
Jacobs W. The location and global network structure of maritime advanced producer services.
Urban Studies,2011,48(13):2749-2769
|
被引
19
次
|
|
|
|
|
15.
Xiulian M. Identifying China's leading world city: A network approach.
GeoJournal,2008,71(1):19-35
|
被引
1
次
|
|
|
|
|
16.
Neal Z. Differentiating centrality and power in theWorld City Network.
Urban Studies,2011,48(13):2733-2748
|
被引
36
次
|
|
|
|
|
17.
Boyd J P. Measuring centrality and power recursively in the World City Network: A reply to Neal.
Urban Studies,2013,50(8):1641-1647
|
被引
7
次
|
|
|
|
|
18.
Meijers E J. Borrowing size in networks of cities: City size, network connectivity and metropolitan functions in Europe.
Papers in Regional Science,2016,95(1):181-198
|
被引
38
次
|
|
|
|
|
19.
Ducruet C. Maritime networks as systems of cities: The long-term interdependencies between global shipping flows and urban development (1890-2010).
Journal of Transport Geography,2018,66:340-355
|
被引
8
次
|
|
|
|
|
20.
Kleibert J. On the global city map, but not in command? Probing Manila's position in the World City Network.
Environment and Planning A: Economy and Space,2016,49(12):2897-2915
|
被引
1
次
|
|
|
|
|
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