全球城市知识流动网络的结构特征与影响因素

doi:10.11821/dlyj020200443

地理研究 ›› 2021, Vol. 40 ›› Issue (5): 1320-1337.doi: 10.11821/dlyj020200443

全球城市知识流动网络的结构特征与影响因素

桂钦昌¹^,²(), 杜德斌¹^,², 刘承良¹^,², 徐伟²^,³(), 侯纯光¹^,², 焦美琪¹^,², 翟晨阳¹^,², 卢函¹^,²

1.华东师范大学全球创新与发展研究院,上海200062
2.华东师范大学城市与区域科学学院,上海 200241
3.莱斯布里奇大学地理系,加拿大阿尔伯塔T1K3M4

收稿日期:2020-05-20 接受日期:2020-09-28 出版日期:2021-05-10 发布日期:2021-07-10
通讯作者: 徐伟
作者简介:桂钦昌（1991-）,男,四川宣汉县人,博士研究生,研究方向为科技创新与城市发展。E-mail:plmok753951@163.com
基金资助:
中国科学院战略性先导科技专项A类(XDA20100311);国家社会科学基金重大项目(19ZDA087);国家社会科学基金重大项目(21ZDA011);华东师范大学未来科学家培育计划(WLKXJ2019-002);华东师范大学研究生出国(境)访学项目(20190620013)

Structural characteristics and influencing factors of the global inter-city knowledge flows network

GUI Qinchang¹^,²(), DU Debin¹^,², LIU Chengliang¹^,², XU Wei²^,³(), HOU Chunguang¹^,², JIAO Meiqi¹^,², ZHAI Chenyang¹^,², LU Han¹^,²

1. Institute for Global Innovation and Development, East China Normal University, Shanghai 200062, China
2. School of Urban and Regional Science, East China Normal University, Shanghai 200241, China
3. Department of Geography, University of Lethbridge, Lethbridge T1K3M4, Alberta, Canada

Received:2020-05-20 Accepted:2020-09-28 Online:2021-05-10 Published:2021-07-10
Contact: XU Wei

摘要/Abstract

摘要：

在科技全球化时代,城市之间的知识流动日益频繁,成为当代知识生产的重要特征。然而,鲜有从知识流动的视角开展全球城市体系的研究。基于2017年的高被引论文合作数据,采用社会网络分析方法和空间计量模型系统地刻画了全球城际科研合作网络的拓扑结构和空间格局及其影响因素。研究发现：① 拓扑结构呈现出以北美、欧洲和亚太城市的三极格局,轴辐式和分布式结构特征并存。② 世界城市科研合作网络形成以北美、西欧、东亚和澳大利亚为顶点的四边形格局。③ 多核心-边缘结构显现,纽约、伦敦和北京等8个城市为全球核心,芝加哥等41个城市为区域核心。④ 全球城市科研合作网络的等级层次特征显著,纽约、北京和吉达分别是三大子网的主导型城市。⑤ 空间相互作用模型表明地理距离阻碍了城市间的科研合作,城市的科研规模、人口规模、世界一流大学数量、制度邻近性和社会邻近性促进了城际联系。

关键词: 全球城市, 城际科研合作, 科技创新中心, 社会网络分析, 加权随机区块模型

Abstract:

In the age of globalizing science and technology, urban economic development increasingly rests on knowledge production and knowledge flows. Inter-city scientific collaborations, as the most potent aspect of modern knowledge production, are more and more frequent, which produces some of the highest quality science. However, there is a paucity of analysis of the world city system from the knowledge flows perspective. Using highly cited papers data from the Web of Science database in 2017, this study applies social network analysis, a Bayesian-inference weighted stochastic block model (WSBM), the dominant flow analysis and spatial interaction model to explore the topological structure, spatial pattern and influencing factors of global inter-city scientific collaboration. Results show that the science hotspots are highly concentrated in three regions: North America, East Asia and Western Europe, and the whole network is dominated by a tri-polar world. The two seemingly paradoxical trends, star-shaped and triangulated structure, coexist in the global inter-city knowledge flows network. The spatial pattern of inter-city collaboration network forms a quadrilateral graph with four vertexes in Western Europe, North America, East Asia and Australia, particularly on the trans-Atlantic axis between North America and Western Europe. The network has a distinctive multicore-periphery structure, which can be divided into five categories: global core, macro-region core, strong semi-periphery, semi-periphery, and periphery, and identifies New York, London, Boston, San Francisco-San Jose, Washington, Los Angeles, Paris and Beijing as eight global core cities and forty-one macro-region cores. The network is characterized as hierarchical “hub-and-spoke” structures, and the hierarchy of the network is obvious, New York, Beijing and Jeddah are dominant nodes in the three subnetworks. In addition, the gravity model indicates the spatial distance impedes inter-city scientific collaboration, while the amount of science output and the number of urban residents, the number of world-class universities, institutional proximity and social proximity have positive and significant effect on inter-city scientific collaboration. In order to further our understanding of world city network, this paper calls for more attention to inter-city knowledge flows.

Key words: global city, scientific collaboration network, science and technology innovation center, social network analysis, weighted stochastic block model

桂钦昌, 杜德斌, 刘承良, 徐伟, 侯纯光, 焦美琪, 翟晨阳, 卢函. 全球城市知识流动网络的结构特征与影响因素[J]. 地理研究, 2021, 40(5): 1320-1337.

GUI Qinchang, DU Debin, LIU Chengliang, XU Wei, HOU Chunguang, JIAO Meiqi, ZHAI Chenyang, LU Han. Structural characteristics and influencing factors of the global inter-city knowledge flows network[J]. GEOGRAPHICAL RESEARCH, 2021, 40(5): 1320-1337.

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网络规模			稠密程度			集聚程度				小世界性
节点数量	边数		密度	平均度		度中心势	接近度中心势	介数中心势		平均集聚系数	平均路径长度
923	109937		0.26	238.22		0.52	0.51	0.03		0.67	1.84

	模型1	模型2	模型3	模型4
城市i发文量	0.32750***	0.31521***	0.32182***	0.23175***
	(0.01140)	(0.01043)	(0.01017)	(0.00830)
城市j发文量	0.33523***	0.32794***	0.33285***	0.20712***
	(0.01205)	(0.01091)	(0.01088)	(0.00907)
城市i人口数	0.01690***	0.02252***	0.04210***	0.02666***
	(0.00451)	(0.00425)	(0.00435)	(0.00365)
城市j人口数	0.00740*	0.01069***	0.02956***	0.01398***
	(0.00437)	(0.00413)	(0.00422)	(0.00345)
城市i一流大学	0.00005	0.00006	0.00005	0.00046***
	(0.00006)	(0.00006)	(0.00005)	(0.00004)
城市j一流大学	0.00004	0.00004	0.00003	0.00057***
	(0.00006)	(0.00006)	(0.00005)	(0.00004)
制度邻近性		0.30082***	0.16768***	0.30144***
		(0.01472)	(0.01620)	(0.01330)
地理邻近性			-0.08666***	-0.06924***
			(0.00484)	(0.00394)
社会邻近性				1.14789***
				(0.02571)
常数	-0.72820***	-0.81986***	-0.72664***	0.11746
	(0.13271)	(0.12082)	(0.12156)	(0.09622)
样本量	5312	5312	5312	5312
Alpha	0.09514	0.08036	0.07227	0.03873
Log likelihood	-19390.632	-19072.32	-18886.488	-17881.985

全球城市知识流动网络的结构特征与影响因素

Structural characteristics and influencing factors of the global inter-city knowledge flows network

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