Spatial network structure of the tourism economy in urban agglomeration: A social network analysis

Gan, Chang; Voda, Mihai; Wang, Kai; Chen, Lijun; Jun, Ye

doi:10.1016/j.jhtm.2021.03.009

Cited by 104 publications

(74 citation statements)

References 49 publications

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“…e rapid development of information technology allows cities to connect across physical distances, and the flow affects all aspects of residents' lives in various ways [7]. From the standpoint of geography, "flow space" is a new spatial logic established against the backdrop of the information age, supported by transportation infrastructure, and characterized by the continuous movement of people flow, logistics, traffic flow, information flow, technology flow, and capital flow [8]. e main components of "flow space" are various entity flows and virtual flows, and "flow's" high-speed characteristics make the circulation of various elements in space more convenient and efficient.…”

Section: Introductionmentioning

confidence: 99%

“…"Flow" not only shortens the distance between cities but also serves as a carrier for residents' activities, cities, and regions while also promoting the development of urban spatial networks and reshaping geographical space. Because all kinds of elements flow at breakneck speed in today's information society, "flow space" is critical to the formation and development of urban and regional networks [8,9]. "Flow space" data can now be accurately measured thanks to the rapid advancement of information technology and the efficient application of information data.…”

Section: Introductionmentioning

confidence: 99%

“…A network is a carrier made up of abstract nodes and connecting node edges that can describe the complex and intertwined objective world [2]. Network or networked research perspectives have been introduced into geography research with the development of research theories and methods [8]. In urban geography, it is assumed that cities are not isolated in regional space but rather have complex interactions with one another, forming the urban network.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Intercity Travel Network Structure during Daily Time and Holiday in China

2021

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Intercity travel by residents promotes the regathering and dissemination of social and economic factors. Based on big data from Tencent’s location-based service, 346 cities above the prefecture level in China were chosen as study objects, with 2018 as the study time node. To construct the intercity residents’ travel network, complex network analysis and GIS spatial analysis methods were used. Furthermore, when analyzing the structural characteristics and spatial differences of Chinese residents’ intercity travel from different time perspectives (the whole year, daily, Spring Festival travel rush, and special holidays), Gephi network analysis tools and ArcGIS spatial analysis software were used. The following are the major findings: daily and the whole year intercity travel by Chinese residents, as well as intercity travel during special holidays and the Spring Festival, all exhibit the “diamond” structure, with Beijing, Shanghai, Guangzhou-Shenzhen, and Chengdu-Chongqing at the core. The distribution of lines in and around the “diamond” is large and concentrated from the perspective of the hierarchical nature of the residents’ intercity travel network. Significant increases in high-intensity population flow lines within the “diamond” can be seen during Spring Festival travel and holidays. The number of cities involved in the inflow line is significantly greater than that involved in the outflow line, as demonstrated by the number of residents in the first point of travel, indicating that there is a difference between the central cities flowing into and out of the network. The first flow of the central city is the most visible during the Spring Festival travel period. Most cities in the resident intercity travel network have relatively low degrees of centrality, closeness centrality, and betweenness centrality, and the number of cities with large values of the three is small, and they are concentrated in the apex and interior of the “diamond” structure.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Intercity Travel Network Structure during Daily Time and Holiday in China

2021

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“…However, scholars have seldom analyzed the tourism economic cooperation between tourist attractions from the microscopic point of view [8]. The epistemology of Sustainability 2021, 13, 4805 2 of 19 tourist destination cooperation has been the subject of ongoing discussion and debate [9,10]. Voda et al [11] emphasized the innovation diffusion importance for destination integration in transformative networks.…”

Section: Introductionmentioning

confidence: 99%

“…The dispersion or cooperation of tourist attractions is a basic feature of the tourism industry [8,12]. Scholars pay more attention to embodying the cooperation from the tourist flow [13] and information flow [14] between scenic areas, to analyze and grasp the flow scale, flow direction, and flow mode of tourists in the inner region. However, scholars have failed to concentrate on tourism economic flows between tourist attractions, which can more objectively and comprehensively reflect the relationship and cooperation between tourist attractions.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Gan

et al. 2021

Sustainability

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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.

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Impact of COVID-19 on Tourists’ Travel Intentions and Behaviors: The Case Study of Hong Kong, China

Peng

Claramunt

et al. 2022

Web and Wireless Geographical Information Systems

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Spatial network structure of the tourism economy in urban agglomeration: A social network analysis

Cited by 104 publications

References 49 publications

Comparison of Intercity Travel Network Structure during Daily Time and Holiday in China

Comparison of Intercity Travel Network Structure during Daily Time and Holiday in China

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

Impact of COVID-19 on Tourists’ Travel Intentions and Behaviors: The Case Study of Hong Kong, China

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