MA Haitao, FANG Chuanglin, PANG Bo, WANG Shaojian. Structure of Chinese City Network as Driven by Technological Knowledge Flows[J]. Chinese Geographical Science, 2015, 25(4): 498-510. doi: 10.1007/s11769-014-0731-0
Citation: MA Haitao, FANG Chuanglin, PANG Bo, WANG Shaojian. Structure of Chinese City Network as Driven by Technological Knowledge Flows[J]. Chinese Geographical Science, 2015, 25(4): 498-510. doi: 10.1007/s11769-014-0731-0

Structure of Chinese City Network as Driven by Technological Knowledge Flows

doi: 10.1007/s11769-014-0731-0
Funds:  Under the auspices of Major Project of National Social Science Foundation of China (No. 13&ZD027), National Natural Science Foundation of China (No. 41201128, 71433008)
More Information
  • Corresponding author: FANG Chuanglin. E-mail: fangcl@igsnrr.ac.cn
  • Received Date: 2014-04-28
  • Rev Recd Date: 2014-08-11
  • Publish Date: 2015-04-27
  • Based on patent cooperation data, this study used a range of city network analysis approaches in order to explore the structure of the Chinese city network which is driven by technological knowledge flows. The results revealed the spatial structure, composition structure, hierarchical structure, group structure, and control structure of Chinese city network, as well as its dynamic factors. The major findings are: 1) the spatial pattern presents a diamond structure, in which Wuhan is the central city; 2) although the invention patent knowledge network is the main part of the broader inter-city innovative cooperation network, it is weaker than the utility model patent; 3) as the senior level cities, Beijing, Shanghai and the cities in the Zhujiang (Pearl) River Delta Region show a strong capability of both spreading and controlling technological knowledge; 4) whilst a national technology alliance has preliminarily formed, regional alliances have not been adequately established; 5) even though the cooperation level amongst weak connection cities is not high, such cities still play an important role in the network as a result of their location within 'structural holes' in the network; and 6) the major driving forces facilitating inter-city technological cooperation are geographical proximity, hierarchical proximity and technological proximity.
  • [1] Barber M J, Scherngell T, 2013. Is the European R&D network homogeneous? Distinguishing relevant network communities using graph theoretic and spatial interaction modelling approaches. Regional Studies, 47(8): 1283-1298. doi: 10.1080/ 00343404.2011.622745.
    [2] Ben Letaifa S, Rabeau Y, 2013. Too close to collaborate? How geographic proximity could impede entrepreneurship and innovation. Journal of Business Research, 66(10): 2071-2078. doi: 10.1016/j.jbusres.2013.02.033.
    [3] Boschma R A, Frenken K, 2010. The spatial evolution of innovation networks: a proximity perspective. In: Boschma R A et al. (eds.). Handbook of Evolutionary Economic Geography. Cheltenham: Edward Elgar, 120-135.
    [4] Bourne L S, 1975. Urban Systems: Strategies for Regulation. Oxford: Oxford University Press, 26-50.
    [5] Burt R S, 1992. Structural Holes: the Social Structure of Competition. Cambridge, Mass: Harvard Business School Press, 3-40.
    [6] Camagni R P, Capello R, 2004. The city network paradigm: Theory and empirical evidence. In: Capello R et al. (eds.). Urban Dynamics and Growth. Netherlands: Elsevier B.V., 1-10.
    [7] Castells M, 1989. The Information City. Oxford: Basil Blackwell, 3-8.
    [8] Choi J H, Barnett G A, Chon B S, 2006. Comparing world city networks: A network analysis of Internet backbone and air transport intercity linkages. Global Networks-a Journal of Transnational Affairs, 6(1): 81-99. doi: 10.1111/j.1471-0374. 2006.00134.x.
    [9] Dai Teqi, Jin Fengjun, 2008. Spatial interaction and network structure evolvement of cities in terms of China's rail passenger flows. Chinese Geographical Science, 18(3): 206-213. doi:  10.1007/s11769-008-0206-2.
    [10] Derudder B,Witlox F, 2005. An appraisal of the use of airline data in assessing the world city network: a research note on data. Urban Studies, 42(13): 2371-2388. doi: 10.1080/004209805 00379503.
    [11] Eslami H, Ebadi A, Schiffauerova A, 2013. Effect of collaboration network structure on knowledge creation and technological performance: the case of biotechnology in Canada. Scientometrics, 97(1): 99-119. doi:  10.1007/s11192-013-1069-6.
    [12] Esparza A X, Krmenec A J, 2000. Large city interaction in the US urban system. Urban Studies, 37(4): 691-709. doi:  10.1080/00420980050003973.
    [13] Fischer M M, Scherngell T, Jansenberger E, 2006. The geography of knowledge spillovers between high-technology firms in Europe: Evidence from a spatial interaction modeling perspective. Geographical Analysis, 38(3): 288-309. doi: 10. 1111/j.1538-4632.2006.00687.x
    [14] Hou H, Kretschmer H, Liu Z, 2008. The structure of scientific collaboration networks in Scientometrics. Scientometrics, 75(2): 189-202. doi:  10.1007/s11192-007-1771-3.
    [15] Jacobs W, Ducruet C, De Langen P, 2010. Integrating world cities into production networks: the case of port cities. Global Networks-a Journal of Transnational Affairs, 10(1): 92-113. doi:  10.1111/j.1471-0374.2010.00276.x
    [16] Lei X P, Zhao Z Y, Zhang X et al., 2013. Technological collaboration patterns in solar cell industry based on patent inventors and assignees analysis. Scientometrics, 96(2): 427-441. doi:  10.1007/s11192-012-0944-x
    [17] Leng Bingrong, Yang Yongchun, Li Yingjie et al., 2011. Spatial characteristics and complex analysis: a perspective from basic activities of urban networks in China. Acta Geographica Sinica, 2(66): 199-211. (in Chinese).
    [18] Leydesdorff L, Persson O, 2010. Mapping the Geography of science: distribution patterns and networks of relations among cities and institutes. Journal of the American Society for Information Science and Technology, 61(8): 1622-1634. doi:  10.1002/Asi.21347
    [19] Liefner I, Hennemann S, 2011. Structural holes and new dimensions of distance: The spatial configuration of the scientific knowledge network of China¢s optical technology sector. Environment and Planning A, 43(4): 810-829.
    [20] Lu Luchang, Huang Ru, 2012. Urban hierarchy of innovation capability and inter-city linkages of knowledge in post-reform China. Chinese Geographical Science, 22(5): 602-616. doi:  10.1007/s11769-012-0555-8
    [21] Matthiessen C W, Schwarz A W, Find S 2002. The ups and downs of global research centers. Science, 297(5586): 1476-1477. doi:  10.1097/CCM.0b013e3181d16b00.
    [22] 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.
    [23] Mo Huihui, Wang Jiaoe, Jin Fengjun, 2009. Identifying centrality in the air transportation network of China. Transportation and Geography, 2: 493-502. doi:  10.1371/journal.pone.0066732.
    [24] Nascimbeni F, 2013. Collaborative knowledge creation in development networks: lessons learnt from a transnational. The Journal of Community Informatics, 9(3): 1-8.
    [25] Neal Z, 2010. Refining the Air Traffic Approach to City Networks. Urban Studies, 47(10): 2195-2215. doi: 10.1177/ 0042098009357352
    [26] Storper M, 1997. The Regional World: territorial Development in a Global Economy. New York: Guilford press. 3-25.
    [27] Tang L, Hu G Y, 2013. Tracing the footprint of knowledge spillover: Evidence from U.S.: China collaboration in Nanotechnology. Journal of the American Society for Information Science and Technology, 64(9): 1791-1801. doi:  10.1002/Asi.22873
    [28] Taylor P J, 2001. Specification of the world city network. Geographical Analysis, 33(2): 181-194.
    [29] Taylor P J, Catalano G, Walker D R F, 2002. Measurement of the world city network. Urban Studies, 39(13): 2367-2376.
    [30] Thompson P, Fox-Kean M, 2005. Patent citations and the geography of knowledge spillovers: a reassessment. American Economic Review, 95(1): 450-460. doi: 10.1257/00028280 53828509.
    [31] Vinciguerra S, Frenken K,Valente M, 2010. The Geography of internet infrastructure: an evolutionary simulation approach based on preferential attachment. Urban Studies, 47(9): 1969-1984.
    [32] Wallace R, Wallace D, Ullmann J E et al., 1999. Deindustriali­zation, inner-city decay, and the hierarchical diffusion of AIDS in the USA: how neoliberal and cold war policies magnified the ecological niche for emerging infections and created a national security crisis. Environment and Planning A, 31(1): 113-139. doi:  10.1068/A310113
    [33] Wang J E, Mo H H, Wang F H et al., 2011. Exploring the network structure and nodal centrality of China's air transport network: A complex network approach. Journal of Transport Geography, 19(4): 712-721. doi: 10.1016/j.jtrangeo.2010. 08.012.
    [34] Wasserman S, Faust K, 1994. Social Network Analysis: Methods and Applications. New York and Cambridge, ENG: Cambridge University Press.
    [35] Zhen Feng, Wang Bo, Chen Yingxue, 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).
    [36] Zhen Feng, Wang Xia, Yin Jun et al., 2013. An empirical study on Chinese city network pattern based on producer services. Chinese Geographical Science, 23(3): 274-285. doi: 10.1007/ s11769-013-0595-8.
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Article Metrics

Article views(395) PDF downloads(894) Cited by()

Proportional views
Related

Structure of Chinese City Network as Driven by Technological Knowledge Flows

doi: 10.1007/s11769-014-0731-0
Funds:  Under the auspices of Major Project of National Social Science Foundation of China (No. 13&ZD027), National Natural Science Foundation of China (No. 41201128, 71433008)
    Corresponding author: FANG Chuanglin. E-mail: fangcl@igsnrr.ac.cn

Abstract: Based on patent cooperation data, this study used a range of city network analysis approaches in order to explore the structure of the Chinese city network which is driven by technological knowledge flows. The results revealed the spatial structure, composition structure, hierarchical structure, group structure, and control structure of Chinese city network, as well as its dynamic factors. The major findings are: 1) the spatial pattern presents a diamond structure, in which Wuhan is the central city; 2) although the invention patent knowledge network is the main part of the broader inter-city innovative cooperation network, it is weaker than the utility model patent; 3) as the senior level cities, Beijing, Shanghai and the cities in the Zhujiang (Pearl) River Delta Region show a strong capability of both spreading and controlling technological knowledge; 4) whilst a national technology alliance has preliminarily formed, regional alliances have not been adequately established; 5) even though the cooperation level amongst weak connection cities is not high, such cities still play an important role in the network as a result of their location within 'structural holes' in the network; and 6) the major driving forces facilitating inter-city technological cooperation are geographical proximity, hierarchical proximity and technological proximity.

MA Haitao, FANG Chuanglin, PANG Bo, WANG Shaojian. Structure of Chinese City Network as Driven by Technological Knowledge Flows[J]. Chinese Geographical Science, 2015, 25(4): 498-510. doi: 10.1007/s11769-014-0731-0
Citation: MA Haitao, FANG Chuanglin, PANG Bo, WANG Shaojian. Structure of Chinese City Network as Driven by Technological Knowledge Flows[J]. Chinese Geographical Science, 2015, 25(4): 498-510. doi: 10.1007/s11769-014-0731-0
Reference (36)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return