Spatial evolution and growth mechanism of urban networks in western China: A multi-scale perspective

doi:10.1007/s11442-022-1959-8

摘要/Abstract

Abstract:

Globalization and informatization promote the evolution of urban spatial organization from a hierarchical structure mode to a network structure mode, forming a complex network system. This study considers the coupling of “space of flows” and “spaces of places” as the core and “embeddedness” as the link and a relevant theoretical basis; then we construct a conceptual model of urban networks and explore the internal logic of enterprise networks and city networks. Using the interlocking-affiliate network model and data from China’s top 500 listed companies, this study constructs a directed multi-valued relational matrix between cities in western China from 2005 to 2015. Using social network analysis and the multiple regression of quadratic assignment program model (MRQAP), this study adopts a “top-down” research perspective to analyze the spatio-temporal evolution and growth mechanism of the city network in western China from three nested spatial scales: large regions, intercity agglomerations, and intracity agglomerations. The results show the following: (1) Under the large regional scale, the city network has good symmetry, obvious characteristics of hierarchical diffusion, neighborhood diffusion, and cross-administrative regional connection, presenting the “core-periphery” structural pattern. (2) The network of intercity agglomerations has the characteristics of centralization, stratification, and geographical proximity. (3) The internal network of each urban agglomeration presents a variety of network structure modes, such as dual-core, single-core, and multicore modes. (4) Administrative subordination and economic system proximity have a significant positive impact on the city network in western China. The differences in internet convenience, investment in science and technology, average time distance, and economic development have negative effects on the growth and development of city networks. (5) The preferential attachment is the internal driving force of the city network development.

Key words: interlocking-affiliate network model, urban agglomerations, western China, centrality, multiple regression of quadratic assignment program (MRQAP)

. [J]. 地理学报(英文版), 2022, 32(3): 517-536.

YANG Liangjie, WANG Jing, YANG Yongchun. Spatial evolution and growth mechanism of urban networks in western China: A multi-scale perspective[J]. Journal of Geographical Sciences, 2022, 32(3): 517-536.

图/表 12

参考文献 58

[1]	Akhavan M, Ghiara H, Mariotti I et al., 2020. Logistics global network connectivity and its determinants. A European city network analysis. Journal of Transport Geography, 82: 102624. doi: 10.1016/j.jtrangeo.2019.102624
[2]	Alderson A S, Beckfield J, 2004. Power and position in the world city system. American Journal of Sociology, 109(4): 811-851. doi: 10.1086/378930
[3]	Ashkezari-Toussi S, Kamel M, Sadoghi-Yazdi H, 2019. Emotional maps based on social networks data to analyze cities emotional structure and measure their emotional similarity. Cities, 86: 113-124. doi: 10.1016/j.cities.2018.09.009
[4]	Berry B J L, 1964. Cities as systems within systems of cities. Papers in Regional Science, 13(1): 147-163. doi: 10.1111/j.1435-5597.1964.tb01283.x
[5]	Broekel T, Balland P A, Burger M et al., 2014. Modeling knowledge networks in economic geography: A discussion of four methods. The Annals of Regional Science, 53(2): 423-452. doi: 10.1007/s00168-014-0616-2
[6]	Cao G, Derudder B, Peng Z W, 2018. Comparing the physical, functional and knowledge integration of the Yangtze River Delta city-region through the lens of inter-city networks. Cities, 82: 119-126. doi: 10.1016/j.cities.2018.05.010
[7]	Castells M, 1996. The Rise of the Network Society. Cambridge, MA: Blackwell.
[8]	Castells M, 2007. Centrality in the space of flows. Built Environment, 33(4): 482-485. doi: 10.2148/benv.33.4.482
[9]	Cervero R, Wu K L, 1998. Sub-centring and commuting: Evidence from the San Francisco Bay Area, 1980-90. Urban Studies, 35(7): 1059-1076. doi: 10.1080/0042098984484
[10]	Chong Z, Pan S, 2020. Understanding the structure and determinants of city network through intra-firm service relationships: The case of Guangdong-Hong Kong-Macao Greater Bay Area. Cities, 103: 102738. doi: 10.1016/j.cities.2020.102738
[11]	Coe N M, Hess M, Yeung H W C et al., 2004. 'Globalizing' regional development: A global production networks perspective. Transactions of the Institute of British Geographers, 29(4): 468-484. doi: 10.1111/tran.2004.29.issue-4
[12]	Defever F, 2006. Functional fragmentation and the location of multinational firms in the enlarged Europe. Regional Science and Urban Economics, 36(5): 658-677. doi: 10.1016/j.regsciurbeco.2006.06.007
[13]	Derudder B, 2006. On conceptual confusion in empirical analyses of a transnational urban network. Urban Studies, 43(11): 2027-2046. doi: 10.1080/00420980600897842
[14]	Derudder B, Witlox F, 2005. On the use of inadequate airline data in mappings of a global urban system. Journal of Air Transport Management, 11(4): 231-237. doi: 10.1016/j.jairtraman.2005.01.001
[15]	Duan D Z, Du D B, Chen Y et al., 2018. Spatial-temporal complexity and growth mechanism of city innovation network in China. Scientia Geographica Sinica, 38(11): 1759-1768. (in Chinese)
[16]	Fang C L, 2015. Important progress and future direction of studies on China’s urban agglomerations. Journal of Geographical Sciences, 25(8): 1003-1024. doi: 10.1007/s11442-015-1216-5
[17]	Fang C L, 2019. The basic law of the formation and expansion in urban agglomerations. Journal of Geographical Sciences, 29(10): 1699-1712. doi: 10.1007/s11442-019-1686-y
[18]	Harvey D, 1990. Between space and time: Reflections on the geographical imagination. Annals of the Association of American Geographers, 80(3): 418-434. doi: 10.1111/j.1467-8306.1990.tb00305.x
[19]	Hu X Q, Wang C, Wu J J et al., 2020. Understanding interurban networks from a multiplexity perspective. Cities, 99: 102625. doi: 10.1016/j.cities.2020.102625
[20]	Kang J J, Xu W, Yu L, Ning Y M, 2020. Localization, urbanization and globalization: Dynamic manufacturing specialization in the YRD mega-city conglomeration. Cities, 99: 102641. doi: 10.1016/j.cities.2020.102641
[21]	Kelly P F, 1999. The geographies and politics of globalization. Progress in Human Geography, 23(3): 379-400. doi: 10.1177/030913259902300303
[22]	Leng B R, Yang Y C, Li Y J et al., 2011. Spatial characteristics and complex analysis: A perspective from basic activities of urban networks in China. Acta Geographica Sinica, 66(2): 199-211. (in Chinese)
[23]	Li D D, Wang T, Wei Y H et al., 2015. Spatial and temporal complexity of scientific knowledge network and technological knowledge network on China’s urban scale. Geographical Research, 34(10): 525-540. (in Chinese)
[24]	Li J, Dong S C, Huang Y B et al., 2014. Multi-scale spatial structure analysis of urban network based on local IT enterprise network in China. Geography and Geo-Information Science, 30(1): 110-115. (in Chinese)
[25]	Li X D, 2012. Research of urban network based on firm network. Shanghai: East China Normal University, (in Chinese)
[26]	Li X D, 2014. Spatial structure of the Yangtze River Delta urban network based on the pattern of listed companies network. Progress in Geography, 33(12): 1587-1600. (in Chinese)
[27]	Li X D, 2015. Structure and Evolution of Urban Network. Beijing: Science and Technology Press. (in Chinese)
[28]	Li Y, Monzur T, 2018. The spatial structure of employment in the metropolitan region of Tokyo: A scale-view. Urban Geography, 39(2): 236-262. doi: 10.1080/02723638.2017.1308182
[29]	Li Y C, 2019. A preliminary analysis on urban innovation network of metropolitan region and its characteristics. Planning Studies, 43(6): 27-33, 39. (in Chinese)
[30]	Liu J, 2009. Lecture on Whole Network Approach:A Practical Guide to UCINET. Shanghai: Truth and Wisdom Press. (in Chinese)
[31]	Liu X, Derudder B, 2013. Analyzing urban networks through the lens of corporate networks: A critical review. Cities, 31: 430-437. doi: 10.1016/j.cities.2012.07.009
[32]	Liu X, Derudder B, Wu K, 2016. Measuring polycentric urban development in China: An intercity transportation network perspective. Regional Studies, 50(8): 1302-1315. doi: 10.1080/00343404.2015.1004535
[33]	Ma H T, Fang C L, 2011. Spatial organization of production networks in city-region based on enterprises relationship: A case of apparel production in urban agglomeration of eastern Guangdong Province. Scientia Geographica Sinica, 31(10): 1172-1180. (in Chinese)
[34]	Matthiessen C W, Schwarz A W, Find S, 2010. World cities of scientific knowledge: systems, networks and potential dynamics. an analysis based on bibliometric indicators. Urban Studies, 47(9): 1879-1897. doi: 10.1177/0042098010372683
[35]	Pan F H, Bi W K, Lenzer J, Zhao S, 2017. Mapping urban networks through inter-firm service relationships: The case of China. Urban Studies, 54 (12): 3639-3654. doi: 10.1177/0042098016685511
[36]	Ren Y, Chen X M, 2009. The Ere of Global City-regions. Shanghai, China: Shanghai Fudan University Press. (in Chinese)
[37]	Rozenblat C, 2010. Opening the black box of agglomeration economies for measuring cities’ competitiveness through international firm networks. Urban Studies, 47(13): 2841-2865. doi: 10.1177/0042098010377369
[38]	Sheng K R, Wang Y J, Fan J, 2019. Dynamics and mechanisms of the spatial structure of urban network in China: A study based on the corporate networks of top 500 public companies. Economic Geography, 39(11): 84-93. (in Chinese) doi: 10.2307/142497
[39]	Sheng K R, Zhang H X, Lv D D, 2018. Progress and prospect of urban networks research through the lens of corporate networks. Human Geography, 33(2): 11-18. (in Chinese)
[40]	Sheng K R, Zhang J, Zhang H X, 2020. Network embedding and urban economic growth in China: A study based on the corporate networks of top 500 public companies. Acta Geographica Sinica, 75(10): 1-17. (in Chinese)
[41]	Smith M P, 2000. Transnational Urbanism: Locating Globalization. Malden, MA: Wiley-Blackwell.
[42]	Song Q, Zhao X Z, Li T S et al., 2018. Spatial structures and influencing factors of multiple urban networks based on the perspective of directed-multivalued relation. Progress in Geography, 37(9): 1257-1267. (in Chinese)
[43]	Sun T, Lv Y Q, 2020. Employment centers and polycentric spatial development in Chinese cities: A multi-scale analysis. Cities, 99: 102617.
[44]	Sun Z W, Lu Z, He J L, 2009. Research on spatial pattern and organization mechanism of international internet information flows. Human Geography, 24(4): 43-49. (in Chinese)
[45]	Taylor P J, 1982. A materialist framework for political geography. Transactions of the Institute of British Geographers, 7(1): 15-34. doi: 10.2307/621909
[46]	Taylor P J, 2001. Urban hinterworlds: Geographies of corporate service provision under conditions of contemporary globalisation. Geography, 86(1): 51-60.
[47]	Taylor P J, 2005. Leading world cities: Empirical evaluations of urban nodes in multiple networks. Urban Studies, 42(9): 1593-1608. doi: 10.1080/00420980500185504
[48]	Taylor P J, Catalano G, Walker D R F, 2002. Measurement of the world city network. Urban Studies, 39(13): 2367-2376. doi: 10.1080/00420980220080011
[49]	Wang L, Duan X, 2018. High-speed rail network development and winner and loser cities in megaregions: The case study of Yangtze River Delta, China. Cities, 83: 71-82. doi: 10.1016/j.cities.2018.06.010
[50]	Wei Y, Song W, Xiu C L et al., 2018. The rich-club phenomenon of China’s population flow network during the country’s spring festival. Applied Geography, 96: 77-85. doi: 10.1016/j.apgeog.2018.05.009
[51]	Xu J, Zhang M, Zhang X L et al., 2019. How does city-cluster high-speed rail facilitate regional integration? Evidence from the Shanghai-Nanjing corridor. Cities, 85: 83-97. doi: 10.1016/j.cities.2018.12.003
[52]	Yang L J, Yang Y C, Pan J H, 2019. Network structure evolution and the spatial-temporal pattern of China western cities participating in global and national competition. Economic Geography, 39(10): 43-53. (in Chinese)
[53]	Yeh A G O, Yang F F, Wang J J, 2015. Producer service linkages and city connectivity in the mega-city region of China: A case study of the Pearl River Delta. Urban Studies, 52(13): 2458-2482. doi: 10.1177/0042098014544762
[54]	Yuan H L, Xin N, 2019. Global trade network pattern and influencing factors of advanced manufacturing in China. Economic geography, 39(6): 108-117. (in Chinese)
[55]	Zhao M X, Li Z F, Zhong Y et al., 2016. Polycentric network topology of urban agglomerations in China. Progress in Geography, 35(3): 376-388. (in Chinese)
[56]	Zhao X Z, Li Q P, Rui S et al., 2019. The characteristics of urban network of China: A study based on the Chinese companies in the fortune global 500 list. Acta Geographica Sinica, 74(4): 694-709. (in Chinese)
[57]	Zhen F, Wang B, Chen Y X, 2012. China’s city network characteristics based on social network space: An empirical analysis of Sina Micro-Blog. Acta Geographica Sinica, 67(8): 1031-1043. (in Chinese)
[58]	Zhou Z H, 2006. Logistic relation of globalization, global urban network and global cities. Journal of Social Sciences, (10): 17-26. (in Chinese)

	Calculation formula	Remarks
Degree centrality	${{C}_{a}}=\underset{b=1}{\overset{M}{\mathop \sum }}\,{{C}_{ab}}$ (4) $C=\underset{a=1}{\overset{M}{\mathop \sum }}\,\underset{b=1}{\overset{M}{\mathop \sum }}\,{{C}_{ab}}$ (5) ${{\text{{C}'}}_{\text{a}}}=\frac{{{\text{C}}_{\text{a}}}}{\text{j}-1}$ (6)	${{C}_{a}}$ refers to the degree of node city a, ${{C}_{ab}}$ is the total connection intensity between city a and city b, $C$ is the total connection intensity of the whole network, and $M$is the number of cities. ${{{C}'}_{a}}$ is the relative centrality of node a, and $j-1$ is the maximum possible link.
Out-degree and in-degree	$C_{a}^{out}=\underset{b=1}{\overset{M}{\mathop \sum }}\,~$ (7) $C_{a}^{in}=\underset{b=1}{\overset{M}{\mathop \sum }}\,~$ (8) $C_{a}^{O}=\frac{C_{a}^{out}}{j-1}$ (9) $C_{a}^{I}=\frac{C_{a}^{in}}{j-1}$ (10)	$C_{a}^{out}$ is the point outdegree of city a,$\text{ }\!\!~\!\!\text{ }C_{a}^{in}$ is the point indegree of city a, $C_{a}^{o}$ and $C_{a}^{I}$ are, respectively, the relative point outdegree and relative point indegree of city a.
Betweenness centrality	${{C}_{B}}\left( {{N}_{i}} \right)=\underset{j<k}{\overset{M}{\mathop \sum }}\,~\frac{{{g}_{jk}}\left( {{n}_{i}} \right)}{{{g}_{jk}}}$ (11)	The shortest path through point i that exists between points j and k is expressed in terms of g_jk(n_i). C_B(N_i) is the probability that i is in the shortest path between points j and k.
Node symmetry	$S{{M}_{a}}=\text{C}_{\text{a}}^{\text{o}}-\text{C}_{\text{a}}^{I}$(12) $SM=1-\frac{1}{2}\underset{a}{\mathop \sum }\,\left\| C_{a}^{o}-C_{a}^{I} \right\|$ (13)	SM_a represents node symmetry, SM represents overall network symmetry.

Target layer	Index layer	Index meaning	Theoretical hypothesis
Y: city network	X₁: Industrial structure similarity	Using the industrial structure similarity model to describe the economic industrial relations based on data of secondary and tertiary industries	A negative influence on city networks
	X₂: Economic development gap relationship	Using the per capita GDP difference to construct the economic development gap	A positive influence on city networks
	X₃: Administrative relationship	According to the national standard of city administrative grade classification to divide the grades of cities	A positive influence on city networks
	X₄: Economic system proximity	According to industrial parks above the provincial level to calculate the intercity economic proximity	A positive influence on city networks
	X₅: Average time distance relationship	By the average time distance between highway and railway to construct the distance relation between two cities	A negative influence on city networks
	X₆: Internet gap	Taking the difference in the number of Internet users per 10,000 people between two cities to build the difference in Internet convenience	A negative influence on city networks
	X₇: Science and technology gap	Taking the difference of expenditure on scientific undertakings between two cities to construct the differential relationship of scientific and technological innovation	A negative influence on city networks

	2005		2010		2015
	City	C^o-Cⁱ	City	C^o-Cⁱ	City	C^o-Cⁱ
Dominant city	Chongqing	0.1983	Chongqing	0.1402	Chongqing	0.0563
	Xi’an	0.0776	Aksu	0.0898	Liuzhou	0.0101
	Chengdu	0.0485	Xi’an	0.0481	Chengdu	0.0096
	Wuhai	0.0267	Chengdu	0.0376	Hohhot	0.0071
	Kunming	0.0087	Yibin	0.0213	Deyang	0.0068
	Nanning	0.004	Kunming	0.0136	Xi’an	0.006
	Hohhot	0.0033	Nanning	0.0092	Guilin	0.005
	Bayingolin	0.0007	Urumqi	0.0081	Xianyang	0.0047
	Yinchuan	0.0002	Ordos	0.0036	Mianyang	0.0044
	Gannan	0.0002	Liuzhou	0.0012	Kunming	0.0044
	┋	┋	┋	┋	┋	┋
Equivalent city	Huangnan	0	Huangnan	0	Huangnan	0
	Golog	0	Golog	0	Ngari	0
	Ganzi	0	Yushu	0	Beihai	0
	Changdu	0	Kizilsu	0
	Nyingchi	0	Changdu	0
	Shannan	0	Shannan	0
	Nagqu	0	Nagqu	0
	Ngari	0	Ngari	0
	Bijie	0	Liupanshui	0
			Bijie	0
			┋	┋
Subordinate city	Xianyang	-0.0184	Hechi	-0.018	Weinan	-0.0154
	Tongliao	-0.0181	Guang’an	-0.0171	Urumqi	-0.013
	Urumqi	-0.0132	Hezhou	-0.0116	Zhangye	-0.0096
	Guiyang	-0.0121	Yuxi	-0.0097	Laibin	-0.0065
	Lanzhou	-0.0101	Nanchong	-0.0083	Wuwei	-0.0064
	Nanchong	-0.01	Wuhai	-0.0081	Jiuquan	-0.0062
	Yuxi	-0.0096	Tianshui	-0.0078	Zunyi	-0.0058
	Deyang	-0.009	Suining	-0.0078	Qujing	-0.0049
	Qujing	-0.0071	Guiyang	-0.0075	Xining	-0.0049
	Xining	-0.0071	Qujing	-0.0072	Lanzhou	-0.0044
	┋	┋	┋	┋	┋	┋
Symmetry	0.63		0.63		0.75

Urban agglomerations 1	Urban agglomerations 2	2005	2010	2015
Lanzhou-Xining	Chengdu-Chongqing	33728	27610	210694
Lanzhou-Xining	Guanzhong Plain	8272	8629	67382
Lanzhou-Xining	Beibu Gulf	1937	4271	35036
Chengdu-Chongqing	Guanzhong Plain	79017	59175	428342
Chengdu-Chongqing	Beibu Gulf	28025	27852	212851
Guanzhong Plain	Beibu Gulf	5909	7711	67043

Influencing factor	Regression coefficient
Influencing factor	2010	2015
Industrial structure similarity Economic development gap relationship	-0.0003 -0.0254*	0.0101 -0.0230*
Administrative relation	0.6998***	0.6709***
Economic systems proximity	-0.0850*	0.0908**
Average time distance relationship	-0.0499**	-0.0333*
Internet gap relationship	-0.1394***	-0.1649***
Science and technology gap relationship	-0.0812***	-0.0740***
R²	0.37	0.48