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

Abstract

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

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.

Figures/Tables 12

Fig. 1

Tab. 1

Tab. 2

The main indicators of urbanization spatial correlation network analysis

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

Tab. 2

Fig. 2

Fig. 3

Tab. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Tab. 4

Tab. 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^**

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

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