基于三种流的中国城市网络空间结构与自组织机制

doi:10.13959/j.issn.1003-2398.2025.03.008

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Abstract With the deepening of research on spatial structure and influence mechanisms within urban networks, the investigation of self-organizing mechanisms has become an important research topic. This paper took 336 China's prefecture-level administrative units as research objects, and utilize Internet big data to establish three kinds of urban networks: enterprise organization, information search, and population migration. Then we analyzed the characteristics of physical space and topological structure across these networks, and finally used the exponential random graph model to empirically test both exogenous influencing factors and endogenous self-organizing mechanisms. The research demonstrates that: 1) Three types of urban networks demonstrate spatial heterogeneity in physical spatial patterns, manifesting a "center-periphery" structure across two hierarchical scales: the national level and the regional level (encompassing urban agglomerations and provincial areas). This spatial configuration leads to two hinterland patterns: large-scale interlaced networks and proximity diffusion zones. 2) Topological analysis identifies characteristic scale-free and smallworld properties, with self-organization manifesting at varying scales. As spatial scope expands, triangle and star configurations emerge as dominant factors, driving the transformation of urban networks toward more complex interactive topologies. 3) Urban network arises from the synergistic integration of exogenous influencing factors and endogenous driving mechanisms. The mechanisms of preferential attachment and network proximity collectively elucidate the self-organization process underlying the intricate urban system.

Key words： urban network spatial structure self-organization space of flow exponential random graph model (ERGM)

Received: 18 March 2024

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F129.9

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	AN Di
	HU Ying-jie
	WAN Yong

Cite this article:

AN Di,HU Ying-jie,WAN Yong. SPATIAL STRUCTURE AND SELF-ORGANIZING MECHANISMS OF URBAN NETWORKS IN CHINA BASED ON THREE TYPES OF FLOWS[J]. HUMAN GEOGRAPHY, 2025, 40(3): 68-78.

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http://rwdl.xisu.edu.cn/EN/10.13959/j.issn.1003-2398.2025.03.008 OR http://rwdl.xisu.edu.cn/EN/Y2025/V40/I3/68

[1]	王士君,廉超,赵梓渝.从中心地到城市网络：中国城镇体系研究的理论转变[J]. 地理研究,2019,38(1):64-74. [Wang Shijun, Lian Chao, Zhao Ziyu. From central place to city network: A theoretical change in China's urban system study[J]. Geographical Research,2019,38(1):64- 74.]
[2]	杨亮洁,杨晓蓉,杨永春.城市内生竞争力与外生竞争力耦合协调研究——以成渝城市群为例[J]. 人文地理,2021,36(6):76-86. [Yang Liangjie, Yang Xiaorong, Yang Yongchun. Coupling coordination study between endogenous and exogenous competitiveness of ChengduChongqing urban agglomeration[J]. Human Geography, 2021,36(6):76- 86.]
[3]	Fang C L, Yu D L. Urban agglomeration: An evolving concept of an emerging phenomenon[J]. Landscape and Urban Planning, 2017, 162:126-136.
[4]	潘峰华,方成,李仙德.中国城市网络研究评述与展望[J]. 地理科学, 2019,39(7):1093-1101. [Pan Fenghua, Fang Cheng, Li Xiande. The progress and prospect of research on Chinese city network[J]. Scientia Geographica Sinica, 2019,39(7):1093-1101.]
[5]	戴靓,曹湛,马海涛,等.中国城市知识合作网络结构演化的影响机制[J]. 地理学报,2023,78(2):334-350. [Dai Liang, Cao Zhan, Ma Haitao, et al. The influencing mechanisms of evolving structures of China's intercity knowledge collaboration networks[J]. Acta Geographica Sinica, 2023,78(2):334-350.]
[6]	郭建科,刘晓扬,韩增林,等.高铁流与信息流在推动环渤海地区城市跨界网络演化中的区别与联系[J]. 人文地理,2024,39(1):130- 141. [Guo Jianke, Liu Xiaoyang, Han Zenglin, et al. The differences and connections between high-speed rail flow and information flow in promoting the evolution of urban cross-border networks in the Bohai rim region[J]. Human Geography, 2024,39(1):130-141.]
[7]	赵群毅.全球化背景下的城市中心性：概念、测量与应用[J]. 城市发展研究,2009,16(4):76-82. [Zhao Qunyi. Debate on urban centrality under the globalization: Concept, measures, and application[J]. Urban Development Studies, 2009,16(4):76-82.]
[8]	胡国建,陈传明,金星星,等. 中国城市体系网络化研究[J]. 地理学报,2019,74(4):681-693. [Hu Guojian, Chen Chuanming, Jing Xingxing, et al. Research progress of networking of urban systems in China[J]. Acta Geographica Sinica, 2019,74(4):681-693.]
[9]	魏石梅, 潘竟虎. 中国地级及以上城市网络结构韧性测度[J]. 地理学报,2021,76(6):1394-1407. [Wei Shimei,Pan Jinghu.Network structure resilience of cities at the prefecture level and above in China[J]. Acta Geographica Sinica, 2021,76(6):1394-1407.]
[10]	安頔,胡映洁,万勇.中国城市网络关联与经济增长溢出效应—— 基于大数据与网络分析方法的研究[J]. 地理研究,2022,41(9): 2465-2481. [An Di, Hu Yingjie, Wan Yong. Urban network association and spillover effects of economic growth in China: A study based on big data and network analysis[J]. Geographical Research, 2022,41(9):2465-2481.]
[11]	Derudder B. Network analysis of 'urban systems': Potential, challenges, and pitfalls[J]. Tijdschrift voor Economische en Sociale Geografie, 2021,112(4):404-420.
[12]	吴康,方创琳,赵渺希.中国城市网络的空间组织及其复杂性结构特征[J]. 地理研究,2015,34(4):711-728. [Wu Kang, Fang Chuanglin, Zhao Miaoxi. The spatial organization and structure complexity of Chinese intercity networks[J]. Geographical Research, 2015, 34(4):711-728.]
[13]	盛科荣,张红霞,赵超越.中国城市网络关联格局的影响因素分析——基于电子信息企业网络的视角[J]. 地理研究,2019,38(5): 1030-1044. [Sheng Kerong, Zhang Hongxia, Zhao Chaoyue. Determinants of the urban spatial network in China: An analysis through the lens of corporate networks within electronic information industry[J]. Geographical Research, 2019,38(5):1030-1044.]
[14]	盛科荣,王云靓,樊杰.中国城市网络空间结构的演化特征及机理研究——基于上市公司500强企业网络视角[J]. 经济地理,2019, 39(11): 84-93. [Sheng Kerong, Wang Yunjing, Fan Jie. Dynamics and mechanisms of the spatial structure of urban network in China: A study based on the corporate networks of top 500 public companies[J]. Economic Geography, 2019,39(11):84-93.]
[15]	戴靓,刘承良,王嵩,等.长三角城市科研合作的邻近性与自组织性[J]. 地理研究,2022,41(9):2499-2515. [Dai Liang, Liu Chengliang, Wang Song, et al. Proximity and self-organizing mechanisms underlying scientific collaboration of cities in the Yangtze River delta[J]. Geographical Research, 2022,41(9):2499-2515.]
[16]	盛科荣,杨雨,张红霞.中国城市网络的凝聚子群及影响因素研究[J]. 地理研究,2019,38(11):2639-2652. [Sheng Kerong, Yang Yu, Zhang Hongxia. Cohesive subgroups and underlying factors in the urban network in China[J]. Geographical Research, 2019, 38(11): 2639-2652.]
[17]	Dai L, Derudder B, Liu X J. Generative network models for simulating urban networks, the case of intercity transport network in Southeast Asia[J]. Cybergeo: European Journal of Geography, 2016. https://doi.org/10.4000/cybergeo.27734.
[18]	周建平,刘程军,徐维祥,等.电子商务背景下快递企业物流网络结构及自组织效应——以中通快递为例[J]. 经济地理,2021,41(2): 103-112. [Zhou Jianping, Liu Chengjun, Xu Weixiang, et al. Logistics network structure of express delivery companies and their selforganization effect under the background of e-commerce: Taking ZTO express as an example[J]. Economic Geography, 2021,41(2): 103-112.]
[19]	古恒宇,沈体雁,刘子亮,等.基于空间滤波方法的中国省际人口迁移驱动因素[J]. 地理学报,2019,74(2):222-237. [Gu hengyu, Shen Tiyan, Liu Ziliang, et al. Driving mechanism of interprovincial population migration flows in China based on spatial filtering[J]. Acta Geographica Sinica, 2019,74(2):222-237.]
[20]	刘清,蒋小荣. 关系依赖如何影响全球化城市网络的生长发育——以苹果手机供应商为例[J]. 干旱区地理,2022,45(1):310-324. [Liu Qing, Jiang Xiaorong. How does relationship dependence affect the spatial growth of globalizing city networks: A case of iPhone's suppliers[J]. Arid Land Geography, 2022,45(1):310-324.]
[21]	Broekel T, Balland P A, Burger M, et al. Modeling knowledge networks in economic geography: A discussion of four empirical strategies[J]. The Annals of Regional Science, 2014,53(2):423-452.
[22]	Block P, Stadtfeld C, Snijders T A B. Forms of dependence: Comparing SAOMs and ERGMs from basic principles[J]. Sociological Methods & Research, 2019,48(1):202-239.
[23]	施响,王士君,王冬艳,等.中国市域间日常人口流动特征及影响因素[J]. 地理科学,2022,42(11):1889-1899. [Shi Xiang, Wang Shijun, Wang Dongyan, et al. Characteristics and influencing factors of daily population flow among cities in China[J]. Scientia Geographica Sinica, 2022,42(11):1889-1899.]
[24]	Liu X J, Derudder B, Liu Y L. Regional geographies of intercity corporate networks: The use of exponential random graph models to assess regional network-formation[J]. Papers in Regional Science, 2015,94(1):109-126.
[25]	戴靓,王瑞霖,曹湛,等.中国城市信息网络的主干结构与影响机制[J]. 人文地理,2024,39(3):113-122. [Dai Liang, Wang Ruilin, Cao Zhan, et al. The influencing mechanism and backbone structure of China's intercity information network[J]. Human Geography, 2024, 39(3):113-122.]
[26]	刘林青,闫小斐,杨理斯,等.国际贸易依赖网络的演化及内生机制研究[J]. 中国工业经济,2021(2):98-116. [Liu Linqing, Yan Xiaofei, Yang Lisi, et al. Research on the evolution and endogenous mechanism of international trade dependence network[J]. China Industrial Economics, 2021(2):98-116.]
[27]	Zhang W Y, Derudder B, Wang J H, et al. An analysis of the determinants of the multiplex urban networks in the Yangtze River delta [J]. Tijdschrift voor Economische en Sociale Geografie, 2020, 111(2):117-133.
[28]	Pan F H, Bi W K, Lenzer J, et al. Mapping urban networks through inter-firm service relationships: The case of China[J]. Urban Studies,2017,54(16):3639-3654.
[29]	Xu Y S, Zhang H P, Li Z H, et al. Integration of migration and attention flow data to reveal association of virtual-real dual intercity network structure[J]. Cities, 2023,143:104614. https://doi.org/10.1016/j.cities.2023.104614.
[30]	戴靓,纪宇凡,邵蕊,等.长三角赛博空间信息流动的方向性与层级性[J]. 长江流域资源与环境,2021,30(9):2070-2078. [Dai Liang, Ji Yufan, Shao Rui, et al. Direction and hierarchy of intercity information flows in cyberspace of Yangtze River delta region[J]. Resources and Environment in the Yangtze Basin, 2021, 30(9): 2070- 2078.]
[31]	侯传璐,覃成林.中国省际贸易网络的特征及影响因素——基于铁路货运流量数据及指数随机图模型的分析[J]. 财贸经济,2019, 40(3): 116-129. [Hou Chuanlu, Qin Chenglin. Features and influence factors of inter-provincial trade network in China[J]. Finance & Trade Economics, 2019,40(3):116-129.]
[32]	Lusher D, Koskinen J, Robins G. Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications[M]. Cambridge: Cambridge University Press, 2013:23-28.
[33]	Schaefer D R, Marcum C S. Modeling network dynamics[M] // Light R, Moody J. The Oxford Handbook of Social Networks. New York: Oxford University Press, 2020:254-287.
[34]	Zhang W L, Chong Z H, Li X J, et al. Spatial patterns and determinant factors of population flow networks in China: Analysis on Tencent location big data[J]. Cities,2020,99:102640. https://doi.org/10.1016/j.cities.2020.102640.
[35]	刘林青,陈紫若.中国优势产业组合的动态演化机制研究——基于 TERGM的实证分析[J]. 科技进步与对策,2020,37(11):70-78. [Liu Linqing, Chen Ziruo. The mechanism research on dynamic evolution of China's advantageous industry portfolio: An empirical analysis based on TERGM[J]. Science & Technology Progress and Policy, 2020,37(11):70-78.]
[36]	孙宇,彭树远.长三角城市创新网络凝聚子群发育机制研究—— 基于多值ERGM[J]. 经济地理,2021,41(9):22-30. [Sun Yu, Peng Shuyuan. Development mechanism of cohesive subgroups' urban innovation networks in the Yangtze River delta: Based on the valued ERGM[J]. Economic Geography, 2021,41(9):22-30.]
[37]	Windzio M. The network of global migration 1990—2013: Using ERGMs to test theories of migration between countries[J]. Social Networks, 2018,53:20-29. http://dx.doi.org/10.1016/j.socnet.2017.08.006.