Characteristics and evolution of knowledge innovation network in the Yangtze River Delta urban agglomeration--A case study of China National Knowledge Infrastructure

被引:6
作者
Gao, Ya [1 ,2 ,3 ]
Ye, Lei [1 ,3 ]
机构
[1] Nantong Univ, Sch Geog Sci, Nantong, Peoples R China
[2] Nantong Univ, Sch Teacher Educ, Nantong, Peoples R China
[3] Nantong Univ, Jiangsu Yangtze River Econ Belt Res Inst, Nantong, Peoples R China
关键词
D O I
10.1371/journal.pone.0283853
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
With the development of economic globalization, urban agglomerations have become growth poles and core areas of economic development. By building knowledge innovation networks in urban agglomerations, we can effectively improve the strength of inter-city knowledge innovation links and better realize the integrated and synergistic development of the region. This study selected core cities in the Yangtze River Delta urban agglomeration as the study area, constructed the knowledge innovation network based on inter-city dissertation cooperation data from 2010 to 2020, and analyzed the characteristics and evolution of its knowledge network by combining social network analysis and geospatial analysis. The research results show that: (1) with changes in policies and investment in scientific research and innovation, intra-regional thesis cooperation in the Yangtze River Delta urban agglomeration has been increasing and the scale of the knowledge innovation cooperation network is growing; (2) in addition to the core cities radiating innovation resources outward to drive the development of other node cities, other cities are continuously improving their own innovation capabilities, taking the initiative to strengthen knowledge innovation cooperation with core cities and enhancing their own position in the network; (3) there are no longer isolated cities within the Yangtze River Delta urban agglomeration, and a multi-core knowledge network structure centered on Shanghai, Nanjing, Hangzhou, and Suzhou has initially formed, but the network is still spatially heterogeneous; (4) there are still problems within the Yangtze River Delta urban agglomeration such as uneven development of knowledge innovation and low participation of peripheral cities, which need to be addressed jointly by all regions. The article concludes with some suggestions for countermeasures to provide a reference for the Yangtze River Delta urban agglomeration to continuously strengthen intra-regional knowledge cooperation in the future, enhance regional competitiveness, and ultimately achieve synergistic development among cities.
引用
收藏
页数:16
相关论文
共 28 条
[1]   Impacts of Urban Green Space on Land Surface Temperature from Urban Block Perspectives [J].
An, Hongmin ;
Cai, Hongyan ;
Xu, Xinliang ;
Qiao, Zhi ;
Han, Dongrui .
REMOTE SENSING, 2022, 14 (18)
[2]  
[Anonymous], 1963, Little science, big science
[3]  
Bagley M J O., 2021, IND INNOV, P1, DOI [10.1080/13662716.2021.2007758, DOI 10.1080/13662716.2021.2007758]
[4]  
Chuan Tang., 2014, INFORM SCIENTIST, V32, P74, DOI [10.13833/j.cnki.is.2014.01.015, DOI 10.13833/J.CNKI.IS.2014.01.015]
[5]  
Darko Dimitrovski., 2022, CURR ISSUES TOUR, V25, DOI [10.1080/13683500.2021.1957788, DOI 10.1080/13683500.2021.1957788]
[6]  
Dongling Zhang., 2008, CHINA SCI TECHNOLOGY, P102, DOI [10.3969/j.issn.1002-6711.2008.09.023, DOI 10.3969/J.ISSN.1002-6711.2008.09.023]
[7]   MODELING SPATIAL AUTOCORRELATION IN SPATIAL INTERACTION DATA: AN APPLICATION TO PATENT CITATION DATA IN THE EUROPEAN UNION [J].
Fischer, Manfred M. ;
Griffith, Daniel A. .
JOURNAL OF REGIONAL SCIENCE, 2008, 48 (05) :969-989
[8]  
[高爽 Gao Shuang], 2019, [热带地理, Tropical Geography], V39, P678
[9]   Understanding seasonal contributions of urban morphology to thermal environment based on boosted regression tree approach [J].
Han, Dongrui ;
An, Hongmin ;
Wang, Fei ;
Xu, Xinliang ;
Qiao, Zhi ;
Wang, Meng ;
Sui, Xueyan ;
Liang, Shouzhen ;
Hou, Xuehui ;
Cai, Hongyan ;
Liu, Yihui .
BUILDING AND ENVIRONMENT, 2022, 226
[10]   Modelling spatial distribution of fine-scale populations based on residential properties [J].
Han, Dongrui ;
Yang, Xiaohuan ;
Cai, Hongyan ;
Xu, Xinliang ;
Qiao, Zhi ;
Cheng, Chuanzhou ;
Dong, Nan ;
Huang, Dong ;
Liu, Andi .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2019, 40 (14) :5287-5300