Analysis of Spatiotemporal Differentiation and Driving Factors of Knowledge Innovation Network Structure Resilience in the Yangtze River Delta Urban Agglomeration

被引：0
|
作者：
Wang Y. [1 ]
Liu D. [2 ,3 ]
Wang G. [4 ]
Sun S. [1 ,5 ]
机构：
[1] The Institute for Sustainable Development, Macau University of Science and Technology
[2] Zhaoqing College, Zhaoqing
[3] School of Education, City University of Macau
[4] Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing
[5] School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou
来源：
Journal of Geo-Information Science | 2024年 / 26卷 / 04期
基金：
中国国家自然科学基金;
关键词：
evolutionary resilience; GTWR; innovation network; knowledge innovation; MRQAP; network structure resilience; spatiotemporal differentiation; Yangtze River Delta urban agglomeration;
D O I：
10.12082/dqxxkx.2024.230539
中图分类号：
学科分类号：
摘要：
Improving the structural resilience of the knowledge innovation network of urban agglomerations is conducive to building a safe regional innovation system and ensuring the knowledge creation function of cities. The knowledge innovation network of urban agglomeration is constructed based on the data of papers published on WOS website for 41 cities in the Yangtze River Delta from 2011 to 2021. Based on evolutionary toughness theory and complex network theory, a four-dimensional evaluation index system of network structure toughness is constructed, which is "vulnerability- invulnerability- resilience- evolution". Social network analysis and GIS spatial analysis are used to describe the temporal evolution and spatial pattern of network structure toughness. GTWR and MRQAP models are used to identify the driving factors. The results show that: (1) From 2011 to 2021, the structural resilience of the knowledge innovation network of the Yangtze River Delta urban agglomeration shows an upward trend, with weakened three-dimensional characteristics, optimized transmission environment, weakened heterogeneous connections, and enhanced integration attributes. There are obvious regional differences in the resilience of the network structure, showing a layout trend of "high in the middle and low in the north, high in the east and low in the west"; (2) The leading nodes of resilience are core cities such as Shanghai, Nanjing, Hangzhou, Hefei, and Suzhou. Ensuring the stability of the dominant node is the key to ensuring the structural resilience of the network. The vulnerable nodes are mostly concentrated in the northwest and southwest of the Yangtze River Delta. Although they can avoid large-scale network collapse through the regional "lock-in" effect, there may also be connection instability caused by poor network linkage and poor resource transmission; (3) Science and education support and industrial structure have significant positive driving forces on the resilience of urban nodes. The explanatory power of economic development, opening up, human capital, and knowledge base has distinct spatial heterogeneity. There is a significant correlation between the driving factors, indicating that the interaction of two factors is an effective way to improve the resilience of urban nodes and promote the evolution of the overall resilience of the network; (4) The MRQAP results show that network aggregation effect, matching effect, siphon effect, and multi-dimensional proximity all drive the resilience evolution of network structure. Network topology, similar industries and similar human resources, good educational environment, and three-dimensional proximity of institutions, society, and organizations are all conducive to the formation of strong knowledge cooperation between cities. © 2024 Science Press. All rights reserved.
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页码：1093 / 1109
页数：16
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