Tourism Economic Network Structural Characteristics of National Parks in the Central Region of China

Abstract

The social network analysis has been actively applied in various tourist destinations, with a few studies on the tourism economic network structure of national parks. Taking the national parks in the Central Region of China as research objects, this study applies the modified gravity model to measure the strength of tourism economic connection. The social network analysis method (SNA) is used to analyze the network structure of tourism economic connection among national parks from the perspectives of overall network density, network centrality, and cohesive subgroups. The results show that the intensity of the tourism relationships among national parks in Central China is unbalanced in spatial distribution. The structure of tourism economic network presents a multi-core model. Lushan Mountain, Shaoshan, Wudang Mountain, and other national parks play a necessary role in the transmission of regional tourism economic elements. There are cohesive subgroups of connections among national parks, which are closely related to administrative divisions and regional cultural background. The subgroups of interior scenes in the same province are more closely related, and the degree of tourism integration still needs to be improved. The study advances the understanding of tourism economic network relationships among tourist attractions in a destination.

Spatiotemporal Characteristics and Determinants of Tourism Cooperation Network in Beijing–Tianjin–Hebei Region

“…At the same time, the cities of the central region were more developed tourism economies of the cohesive type, the southern region contained more outward-looking cities with high external dependence, while the northern region had mostly the isolated type with little or no effective cooperation. Compared with previous studies that were mostly based on tourism economic linkages to construct tourism cooperation networks [42,43,54], weighted and undirected, and only explore the relationship characteristics [46][47][48], this study argues that tourism cooperation should also take into account the attribute characteristics of destinations themselves and identify the directional variability of cooperative linkages. Areas with better tourism resource endowment are more likely to attract tourists, which in turn will be tourist destinations to a greater extent in the cooperative relationship [52].…”

“…Thereinto, the construction of network structure is mainly based on agreements (formal and informal, global and regional) [37,38], web-based text data [39], interviews, and questionnaires [40]. As well as relying on statistics as the data source, a few scholars also attempt to measure the extent of tourism cooperation by the harmonized cooperation index [41] and gravity model [42,43]. On this basis, ArcGIS is used for visual display to form a spatial hierarchical network based on geographical location, focusing on the connection between network nodes and geographical location, as well as identifying the spatial allocation relationship between nodes and tourism elements, and then summarizing the spatial differentiation law of tourism cooperation network structure [44].…”

COVID-19 Impacts on the Spatial Network of City Tourism in Fujian Province, China

“…They found that the weakening of network connectivity largely leads to the decline of regional tourism eco-efficiency, but this trend can be improved by optimizing the spatial distribution of urban agglomerations [21] (Wang, Xia, Dong, Li, Li, Ba, & Zhang, 2021). Wang et al, (2021) [22] used the modified gravity model to analyze the tourism spatial distribution of a national park. Their findings suggest that national parks play an important role in transmitting regional tourism economic factors.…”

Spatial and temporal evolution of Guangdong tourism economic network structure from the perspective of social networks

“…Additionally, the depth and breadth of urban networks from the perspective of tourism are being explored [ 15 ]; however, these studies have mainly focused on the ranking of cities [ 16 ] and ignored the driving mechanisms of urban tourism economic networks [ 17 , 18 ]. The urban tourism network can be understood in more detail from the perspective of dynamics and evolution [ 19 , 20 ]. Currently, only two studies are related to the network structure of the tourism economy in Guangdong Province.…”