Study on the Evolution of Multiple Network Resilience of Urban Agglomerations in the Yellow River Basin

Abstract

To promote the healthy development of urban agglomerations in the Yellow River Basin, we construct a multi-city network-resilience evolution model based on social network theory, combined with QAP regression analysis and analyze the evolution of multiple-city network resilience in the Yellow River Basin in 2014 and 2021 by screening and drawing on indicators in social networks. The results show that (i) only the financial linkage network and the information exchange network are resilient networks, and the magnitude of the evolution of resilience level in the Yellow River basin is finance > information > innovation > transportation. (ii) Except for the increase in the hierarchy of transportation networks, other networks show the trend of flattening. (iii) The matching of the information exchange network shows a shift between heterogeneity and homogeneity, and the transmission and aggregation of the network fluctuate. Based on the study’s findings, a path to improve the resilience level of the Yellow River Basin urban agglomeration by consolidating the status of core cities, optimizing the structure of multiple city networks, and optimizing the flow of factors is proposed.

Exploration of Urban Network Spatial Structure Based on Traffic Flow, Migration Flow and Information Flow: A Case Study of Shanxi Province, China

“…(2) This study explores the spatial patterns of information, population, traffic, and comprehensiveness connection networks among provincial cities using multi-dimensional element flow data with 11 prefecture-level cities in Shanxi Province as research objects. Previous studies have mostly explored the spatial structure of 19 urban agglomerations in China, including the central Shanxi urban agglomeration [32,33] and the Yellow River Basin urban agglomeration [34,35], without focusing on the urban spatial structure of Shanxi Province from the perspective of multi-dimensional element flow. This closely relates to that of Cao [25] on the coordinated development of urban agglomerations in central Shanxi.…”

Research on the Structural Features and Influence Mechanism of the Low-Carbon Technology Cooperation Network Based on Temporal Exponential Random Graph Model

“…The patent data for this study were obtained from the Himmpat database [37]. First, we download the green patent data for the period 2011-2020 based on the classification provided by the International Patent Classification Green Inventory (IPC Green Inventory) developed by the World Intellectual Property Office (WIPO) and the corresponding patent classification number of each category.…”