基于人口流的中国省域城镇化空间关联特征及形成机制

doi:10.11821/dlxb201904003

地理学报 ›› 2019, Vol. 74 ›› Issue (4): 648-663.doi: 10.11821/dlxb201904003

基于人口流的中国省域城镇化空间关联特征及形成机制

刘帅宾^1,^2,^3,⁴(), 杨山^1,^2,^3,⁴(), 王钊^1,^2,^3,⁴

1. 南京师范大学地理科学学院,南京 210023
2. 江苏省地理信息资源开发与利用协同创新中心,南京 210023
3. 虚拟地理环境教育部重点实验室（南京师范大学）,南京 210023
4. 江苏省地理环境演化国家重点实验室培育建设点,南京 210023

收稿日期:2017-12-27 修回日期:2019-03-11 出版日期:2019-04-25 发布日期:2019-04-23
作者简介:
作者简介：刘帅宾(1988-), 男, 河南汝州人, 博士生, 研究方向为城乡发展与区域规划。E-mail: liushuaibin312@163.com
基金资助:
国家自然科学基金项目(41471135, 41771171);江苏高校优势学科建设工程资助项目

Characteristics and formation mechanism of China's provincial urbanization spatial correlation based on population flow

Shuaibin LIU^1,^2,^3,⁴(), Shan YANG^1,^2,^3,⁴(), Zhao WANG^1,^2,^3,⁴

1. School of Geography Science, Nanjing Normal University, Nanjing 210023, China
2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
3. Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China
4. State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China

Received:2017-12-27 Revised:2019-03-11 Published:2019-04-25 Online:2019-04-23
Supported by:
National Natural Science Foundation of China, No.41471135, No.41771171;A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions

摘要/Abstract

摘要：

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。

关键词: 省域城镇化, 省际人口流动, 城镇化空间关联, 空间关联强度, 关联网络中心度

Abstract:

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 high-level 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.

Key words: provincial urbanization, inter-provincial population flow, urbanization spatial correlation, spatial correlation intensity, spatial correlation network centrality

刘帅宾, 杨山, 王钊. 基于人口流的中国省域城镇化空间关联特征及形成机制[J]. 地理学报, 2019, 74(4): 648-663.

Shuaibin LIU, Shan YANG, Zhao WANG. Characteristics and formation mechanism of China's provincial urbanization spatial correlation based on population flow[J]. Acta Geographica Sinica, 2019, 74(4): 648-663.

图/表 12

图1

表1

表2

城镇化空间关联网络分析的主要指标

指标	公式	变量含义	指标含义
网络密度	$D = ∑ i = 1 n ∑ j = 1 n - i P ij [n (n - 1)]; (i ≠ j)$ (2)	$D$ 为网络密度; $P ij$ 为省域i与省域j之间的关联路径,即实际联系的路径,n为省域数量; $F in α$ 和 $F out α$ 分别为子群的内向和外向关联密度, $α$ 和 $β$ 分别为子群内部与外部的节点数量; $C i$ 为省域i的网络中心度, $P i → j$ 和 $P j → i$ 分别代表省域i的流出路径和流入路径。	结果越大,关联网络的整体联通性越好
关联密度 (内向和外向)	$F in α = ∑ i = 1 α ∑ j = 1 α - 1 P ij [α (α - 1)] F out α = ∑ i = 1 α ∑ j = 1 β P j → i (αβ)$ (3)		结果越大,区域或子群的联通性越好
网络中心度	$C i = ∑ j = 1 n - 1 P i → j + ∑ j = 1 n - 1 P j → i$ (4)		结果越大,节点在网络中的地位越高

表2

图2

图3

表3

图4

图5

图6

图7

表4

表5

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强度值(PC1)	强度趋势(PC2)	强度类型	含义
高等	高速	I-H类	关联强度值较大,上升趋势较快
高等	中速	I-M类	关联强度值较大,上升趋势中等
高等	低速	I-L类	关联强度值较大,上升趋势较慢
中等	高速	II-H类	关联强度值中等,上升趋势较快
中等	中速	II-M类	关联强度值中等,上升趋势中等
中等	低速	II-L类	关联强度值中等,上升趋势较慢
低等	高速	III-H类	关联强度值较小,上升趋势较快
低等	中速	III-M类	关联强度值较小,上升趋势中等
低等	低速	III-L类	关联强度值较小,上升趋势较慢

	1990	1995	2000	2005	2010	2015
网络密度	0.064	0.047	0.105	0.122	0.159	0.230
关联路径	52	41	98	113	148	214
路径变异系数	1.158	1.625	1.271	1.296	1.077	0.912

分位数	常数项	Y2	X2	X3	X4	X5
0.1	-8.11^***	0.898^***	0.692	2.352^***	0.165	-0.346
0.2	-7.907^**	0.994^***	0.762	2.087^***	0.177	-0.286
0.3	-7.514^***	0.942^***	0.992^*	1.807^***	-0.029	-0.199^*
0.4	-9.071^***	0.917^***	1.197^***	2.137^***	-0.100	-0.188^*
0.5	-6.692^***	0.922^***	0.903^*	1.669^***	-0.048	-0.143^*
0.6	-6.834^***	0.96^***	0.658^*	2.051^***	0.062	-0.214^**
0.7	-7.523^***	0.991^***	0.623^*	2.169^***	0.073	-0.168^*
0.8	-8.44^***	0.754^***	0.936^**	2.259^***	0.194	-0.155
0.9	-9.451^***	0.741^***	1.185^***	2.332^***	0.209^*	-0.160
pls	-8.087^***	0.863^***	0.838^***	2.060^***	0.139	-0.165^*

分位数	常数项	Y1	X2	X3	X4	X5
0.1	5.442^*	0.804^***	-0.001	-1.907^*	0.180	-0.017
0.2	8.478^***	0.739^***	-0.470	-2.583^***	0.397	0.110
0.3	6.767^***	0.783^***	-0.616^**	-1.881^***	0.149	0.168^*
0.4	6.539^***	0.697^***	-0.552^**	-2.007^***	0.243^**	0.245^***
0.5	6.303^***	0.662^***	-0.444	-2.020^***	0.100	0.307^***
0.6	5.020^**	0.587^***	-0.348	-1.592^***	0.140	0.269^***
0.7	3.108	0.506^***	-0.119	-0.904^*	0.025	0.201^***
0.8	1.899	0.433^***	0.021	-0.537	-0.013	0.185^***
0.9	1.327	0.340^***	-0.019	-0.211	0.057	0.170^***
pls	7.005^***	0.711^***	-0.516^*	-2.062^***	0.301^**	0.178^**

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

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