Evolution and Driving Mechanism of Tourism Flow Networks in the Yangtze River Delta Urban Agglomeration Based on Social Network Analysis and Geographic Information System: A Double-Network Perspective
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Research Framework for Analyzing the Tourism Flow Networks
2.3. Data Collection and Analysis
2.4. Research Methods
2.4.1. Network Density
2.4.2. Core-Periphery Model Analysis
2.4.3. Node Centrality
2.4.4. QAP Regression Analysis
- (1)
- Policy support (PS): The development of tourism is dominated by the government. The government’s implementation of active policies is conducive to accelerating the expansion of the tourism market, promoting complementary advantages of resources and balancing regional development. Therefore, we argue that the government’s formulation and implementation of policies to promote the development of the tourism industry will increase tourist flows to destinations.
- (2)
- Tourism resource endowment (TRE): Tourism resources can stimulate tourists’ travel motivation and travel activities. Because of the imbalanced distribution of tourism resources in different destinations and their immobility, tourism flow comes into being. Therefore, we argue that unique tourism resources have a strong attraction, which can help tourists effectively overcome the resistance of spatial distance to a certain extent and form tourism flows.
- (3)
- Economic development level (EDL): The level of economic development of tourist destinations plays an important role in the development of tourist resources, the construction of infrastructure and the improvement of the tourist environment. The higher the level of economic development of tourist destinations, the more investment in tourism development, the more perfect the construction of tourist attractions and infrastructure, the more tourists’ needs can be met. Therefore, we argue that the level of economic development of tourist destinations is also an important factor influencing the formation of tourist flows.
- (4)
- Tourism reception service capacity (TRSC): Tourism reception service is the most effective way to show the cultural connotation of a tourism destination, promote local culture and enhance attraction. Tourism reception service plays a very important role in shaping a good market image of a destination and improving comprehensive competitiveness of the tourism industry. Only with a high level of tourism reception service ability can the trust and reputation of tourists be won. Therefore, we argue that the tourism reception service capacity of a destination will affect the formation of tourism flows.
- (5)
- Traffic convenience degree (TCD): Tourism transportation facilities and tools build bridges for tourists to and from the source and destination, and are the basic conditions for improving the accessibility of a destination and promoting the sustainable development of the tourism market. Whether the traffic is convenient or not will directly affect the formation of tourism flow between two cities. Therefore, we argue that the formation of tourism flow is inseparable from the influence of transportation convenience.
3. Results and Discussion
3.1. Evolution Analysis of Network Density
3.2. Evolution Analysis of Network Node Centrality
3.2.1. Degree Centrality Analysis
3.2.2. Closeness Centrality Analysis
3.2.3. Betweenness Centrality
3.2.4. The Core–Periphery Structure Analysis
3.3. Driving Mechanism Analysis of Tourism Flow Network Formation
3.3.1. The Selection of Drivers
3.3.2. Driving Mechanism Analysis
4. Conclusions
4.1. Findings
4.2. Implications, Limitations and Further Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Year | Overall Network Density | Anhui Province | Jiangsu Province | Zhejiang Province |
---|---|---|---|---|
2014 | 0.357 | 0.339 | 0.792 | 0.875 |
2017 | 0.572 | 0.661 | 0.847 | 0.929 |
2020 | 0.497 | 0.696 | 0.861 | 0.821 |
Driving Factors | Variable Names | Meaning and Data Source | Expectations | References |
---|---|---|---|---|
Tourism resources endowment | Number of 5A and 4A scenic spots | Tourists tend to flow to high-grade scenic spots. According to the collected travel notes and text data, the difference matrix is established by the sum of the number of 5A and 4A scenic spots. | + | [44,45] |
Transportation convenience | Minimum travel time by automobile | Tourists are willing to move between easily accessible areas. The shortest time through Baidu map query. | - | [45,46] |
Minimum travel time by high-speed rail/train | Check China’s Railway official website (https://www.12306.cn/) for a high-speed rail/train that takes the shortest running time. If there is no railway between the two places, the value assigned is 0. | - | [42,46] | |
Minimum travel time by air | Inquire through the carrier network (www.ctrip.com) to get the shortest operating time of the aircraft. If there is no route between the two places, the value is 0 | - | [46,47] | |
Economic development level | GDP gross | Tourists tend to flow in areas with high economic levels. According to the statistical bulletins of cities in the Yangtze River Delta, the difference matrix is established by taking the values for 2020. | + | [45,47] |
Total income of tertiary industry | Ditto | + | [47] | |
The proportion of tertiary industry in GDP | Ditto | + | [47] | |
Policy support | Tourism financial expenditure | The greater the government policy support, the more effective it will be to improve the destination supply and tourism attraction. According to the statistical bulletin of each city, the values for 2020 are taken to establish the difference matrix. | + | [48] |
Tourism reception service capacity | Number of travel agencies and star-rated hotels | Tourists tend to flow in areas with strong reception and service capacity. The data were collected from the cultural and tourism departments of Anhui, Zhejiang, Jiangsu, and Shanghai, and the difference matrix was established according to the quantity. | + | [44,47] |
Number of tertiary industry employees | Ditto | + | [44,47] |
Item | Variable Names | QAP Correlation Coefficient | p-Value | QAP Regression Coefficient | Sig. |
---|---|---|---|---|---|
Tourism resources endowment | Number of 5A and 4A scenic spots | 0.298 *** | 0.001 | 0.157 ** | 0.013 |
Transportation convenience | Minimum travel time by automobile | −0.228 *** | 0.000 | −0.195 *** | 0.001 |
Minimum travel time by high-speed rail/train | −0.264 *** | 0.000 | −0.121 ** | 0.003 | |
Minimum travel time by air | 0.237 *** | 0.002 | 0.141 | 0.008 | |
Economic development level | GDP gross | 0.329 *** | 0.001 | 0.081 | 0.011 |
Total income of tertiary industry | 0.275 *** | 0.000 | 0.188 | 0.099 | |
The proportion of tertiary industry in GDP | 0.249 *** | 0.000 | 0.401 | 0.001 | |
Policy support | Tourism financial expenditure | 0.204 *** | 0.358 | 0.006 | 0.107 |
Tourism reception service ability | Number of travel agencies and star-rated hotels | 0.336 *** | 0.000 | 0.449 ** | 0.011 |
Number of tertiary industry employees | 0.296 *** | 0.002 | 0.508 ** | 0.014 |
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Wang, Y.; Xi, M.; Chen, H.; Lu, C. Evolution and Driving Mechanism of Tourism Flow Networks in the Yangtze River Delta Urban Agglomeration Based on Social Network Analysis and Geographic Information System: A Double-Network Perspective. Sustainability 2022, 14, 7656. https://doi.org/10.3390/su14137656
Wang Y, Xi M, Chen H, Lu C. Evolution and Driving Mechanism of Tourism Flow Networks in the Yangtze River Delta Urban Agglomeration Based on Social Network Analysis and Geographic Information System: A Double-Network Perspective. Sustainability. 2022; 14(13):7656. https://doi.org/10.3390/su14137656
Chicago/Turabian StyleWang, Yuewei, Mengmeng Xi, Hang Chen, and Cong Lu. 2022. "Evolution and Driving Mechanism of Tourism Flow Networks in the Yangtze River Delta Urban Agglomeration Based on Social Network Analysis and Geographic Information System: A Double-Network Perspective" Sustainability 14, no. 13: 7656. https://doi.org/10.3390/su14137656