Analysis of Complex Transportation Network and Its Tourism Utilization Potential: A Case Study of Guizhou Expressways

Sihong, Chen; Xi, Jianchao; Liu, Menghao; Tao, Li

doi:10.1155/2020/1042506

Cited by 9 publications

(8 citation statements)

References 49 publications

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“…As for the locational characteristics of each province in the spatial association network of digital financial inclusion development, we focus on three aspects, namely, degree centrality, betweenness centrality, and closeness centrality [24], which can be used to measure the connectivity, intermediation, and accessibility [25] of different provinces in the network. Degree centrality measures the extent to which a province is in direct contact or is adjacent to many other provinces in the network.…”

Section: Centralitymentioning

confidence: 99%

Spatial Association and Explanation of China’s Digital Financial Inclusion Development Based on the Network Analysis Method

et al. 2021

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As an essential direction for developing inclusive finance, digital financial inclusion breaks through the time and space constraints of inclusive finance development and has extensive connections between different regions. However, no research has modelled the network connections and the role and position of different digital financial inclusion development regions. This study constructed the spatial association network of China’s digital financial inclusion development and used the network analysis method and the quadratic assignment procedure (QAP) method to study the structural and locational properties and the influencing factors of the network. We found that (1) although the network had a relatively low density, its connectivity and stability were excellent, and the network structure is not hierarchical; (2) the centrality of some rapidly developing central and western provinces was greater than that of some developed eastern provinces; (3) developed eastern provinces played a net spillover role, driving the development of digital financial inclusion in central and western provinces; and (4) the spatial association was affected by the development level of the PC Internet and economy, the industrial structure, and the spatial adjacency. This study enriches the research on digital financial inclusion and provides a scientific basis for the formulation and implementation of policies to promote the further development of digital financial inclusion.

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Section: Centralitymentioning

confidence: 99%

Spatial Association and Explanation of China’s Digital Financial Inclusion Development Based on the Network Analysis Method

et al. 2021

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“…The nodes at the center of the social network have a great influence on other individuals. In order to know the status and function of each province in the whole network, it is necessary to analyze the centrality of the individual network, using centrality indicators to measure connectivity, intermediation, and accessibility [56]. The measurement indicators mainly include degree centrality, closeness centrality, and betweenness centrality:…”

Section: Individual Network Analysismentioning

confidence: 99%

Spatial Network Analysis on the Coupling Coordination of Digital Finance and Technological Innovation

Wang

2023

Sustainability

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For sustainable economic development, it is extremely important to understand how digital finance and technological innovation are coupled and how the spatial coupling network is connected. Based on panel data collected from 31 Chinese provinces between 2011 and 2020, this paper calculates the technological innovation index using the entropy method, and adopts the coupling coordination model to measure the coupling coordination degree of digital finance and technological innovation. Furthermore, this paper utilizes the improved gravity model to determine the spatial correlation matrix and uses the social network analysis (SNA) method to investigate the spatial spillover characteristics of the coupling network. The results demonstrate the following: (1) While the index of digital finance and technological innovation rose and digital finance developed rapidly, the level of technological innovation remained low. (2) There was an improvement in the degree of coupling coordination between digital finance and technological innovation, which was higher in the eastern region and lower in the west. (3) The overall network density and the number of associations increased; meanwhile, the network hierarchy and network efficiency declined, indicating that the spatial structure was strengthened. (4) The centrality of some developed eastern regions, such as Beijing, Shanghai, and Zhejiang, was greater than that of some underdeveloped areas in the midwest and northeast regions. (5) The coupling coordination network can be classified into four types: the “main inflow plate” mainly includes underdeveloped regions in the midwest areas; the “main outflow plate” and “bidirectional spillover plate” primarily include the developed eastern areas; and the “agent plate” mainly includes the central provinces. This research provides a foundation for enhancing the cross-regional coupling coordinated development of digital finance and technological innovation.

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“…Gao et al used Panel data of Chinese cities from 2004 to 2015 to evaluate the impact of high-speed rail on tourism growth [ 7 ]. Chen et al applied a complex network theory, social network analysis, kernel density analysis, binary auto-association, and other methods for analyzing complex transportation and tourism potential [ 8 ]. Based on the gravity center and coupling coordination model, Lu et al explored the gravity center coupling dynamics and coordinated development degree of tourism and transportation [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Spatiotemporal Association between Tourism and Transportation Based on CGS Model

Ya-nan

Qin

et al. 2022

Computational Intelligence and Neuroscience

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Tourism and transportation generally have an inseparable association. However, there are still many limitations in the existing research on it. For example, most scholars only adopt one single model method, which fails to consider geospatial elements. Moreover, some researchers simply use socioeconomic data for analysis and research and ignore the solid spatial characteristics between tourism and transportation, which leads to deviations in the results. To solve these problems, this article proposed a spatiotemporal association model by comprehensively using coupling coordination degree, gravity center model, and spatial coincidence degree. Based on the tourism economic and attraction spatial data, and the transportation and its network spatial data, the association between tourism and transportation can be revealed by the proposed model. This study conducted a quantitative analysis on the tourism and transportation industry in Jiangxi Province, China, from 2005 to 2019, and the results show that: (1) the coupling coordination degree of tourism and transportation increases year by year; (2) the change in gravity center of tourism and transportation is subtle. The mean value of spatial overlap is 80.33 km, while the mean value of inter-annual variation consistency is 0.56; (3) the spatial coincidence degree of tourism and transportation in Jiangxi Province indicates a steady upward trend and reaches 0.78 in 2019; and (4) based on the evolution trend in the coupling coordination degree, gravity center coupling model, and spatial coincidence degree of tourism and transportation, it can be seen that the slopes of their trend functions are similar and consistent—the slopes are 0.0239, 0.0253, and 0.0319, respectively—and the standard deviation of the slopes of the three is only 0.000018.

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Analysis of Complex Transportation Network and Its Tourism Utilization Potential: A Case Study of Guizhou Expressways

Cited by 9 publications

References 49 publications

Spatial Association and Explanation of China’s Digital Financial Inclusion Development Based on the Network Analysis Method

Spatial Association and Explanation of China’s Digital Financial Inclusion Development Based on the Network Analysis Method

Spatial Network Analysis on the Coupling Coordination of Digital Finance and Technological Innovation

Research on Spatiotemporal Association between Tourism and Transportation Based on CGS Model

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