The Spatial Structure and Influencing Factors of the Tourism Economic Network in the Yangtze River Delta Urban Agglomeration

Abstract

The optimization of a tourism economic network is critical in the promotion of the high-quality development of a regional tourism economy. In order to explore the spatial network structure of the tourism economy of the Yangtze River Delta urban agglomeration and its influencing factors, this study used a modified gravity model and social network analysis methods for evaluation and analysis. The results show the following: (1) the spatial network of the tourism economy in the Yangtze River Delta urban agglomeration in 2016–2021 was characterized by significant non-equilibrium; however, that trend has weakened, and the tourism connections are now stronger in economically developed regions. (2) The density of the network structure was slightly strengthened. The structure of the tourism economy network shows an obvious core–periphery distribution pattern. The external radiation of the center city was enhanced, and the core area of the network expanded. (3) The concept of the “small world” is characterized by a significant evolution from five major associations to four major associations during the study period. (4) The quadratic assignment method (QAP) of regression analysis showed that tourism service reception capacity, tourism information flow, tourism resource endowments and transportation convenience make a significant contribution to the formation of the spatial network of inter-city tourism economic connections. The results of this study can provide a theoretical basis for the optimization of the tourism economic network of urban agglomerations and the scientific decision-making underpinning tourism economic cooperation.

Network Structure Characteristics and Influencing Factors of Urban Agglomerations in China under Impact of COVID-19

“…The QAP analysis method, a non-parametric matrix processing approach, alleviates the need for considering variable independence and effectively addresses multicollinearity [38,39]. Consequently, QAP analysis is widely employed to examine the factors influencing the network structure of UAs [40,41].…”