Prerpints.org logo
Preprint
Article

COVID-19 Impacts on the Sustainable Development of Spatial Network of UrbanTourism—a Case Study of Fujian, China

Altmetrics

Downloads

122

Views

44

Comments

0

A peer-reviewed article of this preprint also exists.

This version is not peer-reviewed

Preprints on COVID-19 and SARS-CoV-2

Submitted:

21 February 2024

Posted:

24 February 2024

You are already at the latest version

Alerts
Abstract
This study examined the tourism spatial distribution of nine cities in the Fujian province and assessed the impacts of COVID-19. The modified gravity model found that it was widely dispersed, with uneven and relatively independent tourism development in different cities. The social network analysis showed that tourism connections across cities were significantly reduced after the pandemic. The impacts of brand awareness and transport accessibility on spatial networks were positive in pre-pandemic but became negative during the pandemic. In contrast, tourist volume had negative impacts on spatial networks in pre-pandemic but had positive ones during the pandemic. Tourism resources and market performance had significantly positive impacts in the post-pandemic era. These findings provide advice on tourism recovery and destination management in coping with future critical events. It also contributes to the theoretical gravity framework in tourism and the research scope of the social networks analysis at the city level.
Keywords: 
Subject: Social Sciences  -   Tourism, Leisure, Sport and Hospitality

1. Introduction

The spatially balanced distribution of tourism development is an integral part of the coordinated regional economy, profoundly influencing government policymaking and tourism market behavior (Wang & Ye, 2022). Tourism development enhances not only the scale of economies within a region but also regional externalities such as the flow of resources (e.g., people, money, materials, information, and technology). Thus, spatially balanced tourism development eventually helps the mutual adoption of industries and stimulates the multiplier effect of industrial development (Pascariu & Ibanescu, 2018). However, a previous unidentified coronavirus emerged from Wuhan, China in December 2019 and it was later named Coronavirus Disease-2019 (COVID-19) by the World Health Organization in February 2020. The outbreak of the COVID-19 pandemic in early 2020 has had significant impacts on the tourism industry in China, and travel restrictions forced people to take short-distance regional trips instead of national/international travel (Wen, Kozak, Yang, & Liu, 2021; Zhong, Sun, Law, & Li, 2021). Thus, the urban spatial network has been changed, while regional incoherence may have come to the fore (Utkarsh & Sigala, 2021).
Previous research mainly focused on a specific dimension of the spatial distribution of the tourism economy (e.g., commuter networks, railway networks, or social networks), rarely has attention been paid to assess tourism interactions across cities as reflected in spatial distribution (Green, 2007). For instance, the existing literature has focused on the tourism spatial distribution simply in terms of tourism economic revenues and tourist volume, neglecting the interactions (i.e., cooperation and competition) between cities as well as the level of economic development and the local as a whole, which leaves the tourism spatial distribution fragmented (Wang & Ye, 2022). Considering that the tourism industry is historically known as vulnerable to crisis, the macro-level institutional travel restrictions and the micro-level behavioral change of tourists due to the Covid-19 would have impacted the urban spatial network substantially (Gössling, Scott, & Hall, 2021). Because of its highly contagious, many countries adopted precautious measures to control possible outbreaks, such as entry bans and quarantine requirements, as well as regular testing to reduce the rapid spread of COVID-19 (Vaishar & Šťastná, 2022). Notably, these travel restrictions caused a great financial loss to international tourism (Bhuiyan, Crovella, Paiano, & Alves, 2021). When the travel bans were gradually eased in the post-pandemic era, tourism in China started to experience a fast recovery, especially for some neighboring cities within the same region (Li, Nguyen, & Coca-Stefaniak, 2021). Thus, studies empirically assessing these impacts are urgently needed for tourism recovery (Ritchie & Jiang, 2019).
Considering the scarcity of literature and data limitation, this study will use the Fujian province in China to do a case study. Before the pandemic, cities in Fujian province were closely connected in 2019, however, travel restrictions weakened the links between the cities during the pandemic in 2020. At this moment, the tourism economy has started to recover in the post-pandemic period. In 2021, there were 651,100 international tourists and 406,805,100 domestic tourists, with $67482.59 million in combined tourism revenue (Fujian Provincial Bureau of Statistics, 2022). As a result of this dynamic context, this study used the Fujian province as a case study and adopted the modified gravity model to explore the tourism spatial distribution and its influencing factors. Building on the modified gravity model, the social network analysis and Quadratic Assignment Procedure (QAP) method were applied to determine the influencing factors and their impacts on the tourism spatial distribution.

2. Literature Review

2.1. Modified Gravity Model

The gravity model, which originates from Newton’s law of university gravitation, is a theoretical model that describes the connection of two objects (Anderson, 2011). It was first applied in tourism literature in the 1960s to assess the determinants of tourism flows between two places (i.e., nodes). The gravity model can capture the transport patterns, which are directly proportional to the nodes’ size and inversely proportional to the distance between them (Nadal & Gallego, 2022; Wang, Qin, Zhao, Zhao & Han, 2023). For example, Goh, Li, and Li (2014) applied the gravity model to compare the spatial distribution of the international tourism economy with the domestic one in China and found that the spatial development of the domestic tourism economy was uneven.
The modified gravity model extended the node to a regional aggregation such as the population and economic activity of a city/province, and distance can include both geographical distance and socioeconomic factors like connectivity and travel costs. Sun and Hou (2021) explored the spatial distribution and evolution of tourism eco-efficiency based on a modified gravity model. 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(Wang, Xia, Dong, Li, Li, Ba & Zhang, 2021). Wang et al. (2021) 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 the transmission of regional tourism economic factors. In particular, the associated subgroups formed are closely related to administrative divisions, and the regional subgroups formed are more related to the administrative division and cultural context. Nadal and Gallego (2022) suggested using the modified gravity model to measure the strength of tourism linkages between different regions and to assess the spatial distribution of the tourism economy.
Despite the popularity of the model in tourism literature, previous studies have not taken consideration of the impacts of the pandemic on tourism spatial distribution. This is deemed a significant research gap that needs to be addressed, because the pandemic has exerted great impacts on tourism and empirical studies assessing these impacts are of great value for coping with future critical events. To address this research gap, this paper aims to identify the influential factors and provide advice on tourism recovery in the post-pandemic.

2.2. Social Network Analysis

Social network analysis uses a network model and the study of graphs to examine the stakeholders and their relationships within the network (Otte & Rousseau, 2002). Since the 1930s, it has been widely applied to examine the complicated interactions between cities ranging from place attributes to intercity relationships (Butts, 2008). It uses the sociogram, where nodes represent individuals, groups, or organizations; and lines investigate the relationships or flows between the nodes (Vance-Borland & Holley, 2011).
The indicators of network characteristics usually include network density, degree centrality, network correlation, and network hierarchy (Wang, Zhang, & Jin, 2021). Network density is a measure of the connectedness of the nodes from fully disconnected to fully connected in the spatial distribution, with values ranging between 0 and 1 (Kohler, Behrman, & Watkins, 2001). It represents the closeness of the influence among cities in the correlation network in this study. The degree centrality refers to the overall centralization of the spatial distribution and is equal to the number of connections that a stakeholder has with other stakeholders (dos Muchangos, Tokai, & Hanashima, 2017). Degree centrality indicates whether a city is centrally located in the spatial network, and the higher the value of degree centrality, the more closely connected it is to other cities. Network correlation represents the robustness of the spatial distribution itself, with a higher network correlation indicating a deeper degree of cooperation between cities. Network hierarchy is a measure of the asymmetric accessibility of the relationship among nodes in the network (Gilbert, Simonetto, Zaidi, Jourdan, & Bourqui, 2011), reflecting the asymmetric accessibility and rigid network of cities. Higher network hierarchy indicates that the hierarchical structure among cities in the network is relatively unequal.
However, these indicators have not been fully applied to examine the tourism spatial network across cities in tourism literature (Wang, Liu, Xu, & Fujiki, 2020). Furthermore, the impact of the COVID-19 pandemic on the spatial network of city tourism has become increasingly prominent in the post-pandemic era. Therefore, it is of great value to compare the spatial network characteristics of city tourism along the pre-, present, and post-pandemic to improve the market performance of urban tourism in coping with future critical events.
Based on the literature review above, Table 1 summarizes the variables related to the tourism spatial distribution including tourist volume, transport accessibility, city brand market performance, city tourism brand awareness, infrastructure levels, regional industrial structures, tourism resources, and geospatial proximity.

3. Research Design

3.1. Samples

The Fujian province is located on the southeastern coast of China with a population of 41.5 million, ranking 15th in population among Chinese provinces. The Fujian province is one of the most culturally and linguistically diverse provinces in China. It has an abundance of tourism resources including 10 national 5A scenic spots, ranking first in the country with a forest coverage rate of 66.80% (Fujian Provincial Bureau of Statistics, 2022). Figure 1 shows the nine prefecture-level cities in the Fujian province including Fuzhou, Xiamen, Quanzhou, Zhangzhou, Putian, Longyan, Sanming, Nanping, and Ningde.
According to the statistical yearbooks of Fujian province and tourism economic-related data, all the A-class scenic spots in the nine cities in the Fujian province were selected for analysis (Fujian Provincial Bureau of Statistics, 2022). This study focused on a three-phase period: pre-pandemic (2019), pandemic (2020), and post-pandemic (2021). Table 2 shows the main variables below.

3.2. Models

Referring to the studies of Nogle (1994) and Seok, Barnett, and Nam (2021), the level of urban economic development (measured by the total GDP of the city), urban population, tourist volume, tourism revenue, and the number of tourism employees were included in the modified gravity models:
G = K i j P i R i T i P j R j T j 3 D i j 2
K i j = g i g i + g j = G i V i G i V i + G j V j
where G is the tourism gravity of city i to city j; Pi and Pj are the tourism volume of city i and city j; Ri and Rj are the tourism revenue of city i and city j; Ti and Tj are the numbers of tourism employees of city i and city j; Kij is the correction factor; gi and gj are the GDP per capita of city i and city j; Gi and Gj are the GDP of city i and city j; and Vi and Vj are the population of city i and city j.
The study uses this modified gravity model to measure the strength of tourism linkages among cities in Fujian Province based on the tourist volume, tourism revenue, the number of tourism employees, GDP, the distance between two cities and the urban population, and to measure the degree of association between cities. Based on the results of the modified gravity model, social network analysis is used to carve out the spatial distribution characteristics among cities in Fujian Province. After that, tourist volume, transport accessibility, city brand market performance, city tourism brand awareness, infrastructure levels, regional industrial structures, tourism resources, and geospatial proximity are selected and Quadratic Assignment Procedure (QAP) is used to explore the influencing factors of the spatial distribution of Fujian Province.

4. Findings

4.1. The Results of the Modified Gravity Model

The modified gravity model was used to calculate the maximum gravitational value of each city in the Fujian province to other cities. Then, the gravitational values of the cities were summed up to form the tourism economic correlation matrix with the tourism economic correlation between the cities. Table 3 shows the sum of the gravitational values exerted by each city on other cities in the Fujian province, the city with the largest exerted gravitational value, and the corresponding gravitational value.
Before the pandemic, Zhangzhou had the largest gravitational value (15160.711) on other cities and exerted the greatest gravitational value (11462.229) on Xiamen in 2019. It is worth noting that the maximum gravitational value of cities declined dramatically during the pandemic in 2020 compared with 2019, particularly in Zhangzhou, where the maximum gravitational value decreased substantially (3054.102). In 2020, it turns out that Xiamen had the largest gravitational value (7059.328) among other cities, with the biggest exerted gravitational value (3461.616) in Zhangzhou. Since the post-pandemic in 2021, Xiamen’s gravitational value (9314.218) returned to the pre-pandemic level, with the highest exerted gravitational value (5097.084) on Zhangzhou. In 2021, inter-city gravitational values have increased, but are still below pre-epidemic levels

4.2. Overall Network Characteristics

Based on the gravitational values, Ucinet 6 was used to calculate the network density, degree centrality, network correlation, and network hierarchy. The spatial structure of tourism linkages in the Fujian province was relatively dispersed in terms of network density values (0.125) (see Table 4). From pre-pandemic to post-pandemic, the value of degree centrality in the Fujian province was 0.278 (in 2019), 0.255 (in 2020), and 0.267 (in 2021), respectively, indicating that cities in the Fujian province were relatively independent. Predictably, the overall centrality of the tourism spatial distribution in 2020 decreased to 0.255, reflecting the decline of tourism flow due to the COVID-19 pandemic. Though lower than its pre-pandemic levels, in 2021, the centrality of the tourism spatial distribution partially recovered to 0.267. Across all periods, the network correlation in the Fujian province was all 1, indicating that the network correlation was robust, without substantial change in the degree of cooperation among cities. Furthermore, the network hierarchy degree was 0, which indicates the low-ranking degree of the tourism spatial distribution in the Fujian province and the relatively independent tourism development of each city.

4.3. Individual Network Characteristics

Table 5 shows the value of degree centrality in the Fujian province. During 2019-2021, the value of indegree for all cities in Fujian was 1, indicating that all nine cities were not closely connected and that no one city was in a relatively central position before, during, or after the pandemic. Fuzhou, Xiamen, and Quanzhou had a value of degree centrality above the indegree, indicating that these cities did not benefit from city networks. In contrast, Sanming, Putian, Ningde, and Nanping each had a value of degree centrality below the indegree, indicating that they benefited from city networks. Interestingly, the degree centrality in Zhangzhou has been steadily increasing from 0.385 in 2019, 0.962 in 2020, to 1.123 in 2021, indicating that Zhangzhou is experiencing rapid tourism development, despite the interruptions associated with the pandemic, and may not need to rely on city networks in the post-pandemic period.
Overall, the tourism spatial distribution in the Fujian province developed unevenly across cities, a pattern that remained before, during, and after the pandemic. Moreover, the tourism interactions between cities in the Fujian province during 2019-2021 were generally low. One explanation is that none of the cities were in a core position, thus, tourism in each city developed independently.

4.4. Clustering Structure Characteristics

Taking the analysis a step further, this research utilized the cohesive subgroups method to analyze the clustering structure characteristics. The members of the cohesive subgroups were very close to each other and interact more closely than groups outside the subgroup (Butts, 2008). In this study, cohesive subgroups were the subsets of cities in the tourism spatial distribution, revealing the actual or potential relationships of each city. Specifically, cities with close tourism spatial distribution were combined to form a subgroup.
The cohesive subgroups were calculated using the iterative correlation convergence method, setting the maximum cut to 2 and the convergence criterion to 0.2, to obtain the density matrix. The density matrix was set to 1 for densities greater than the overall network density and 0 for densities less than the overall network density to form the like matrix. Table 6 presents the results of the density matrix and the like matrix from 2019 to 2021.
To better interpret the clustering structure characteristics, Figure 2 shows that a total of three subgroups were formed in 2019 and 2021: Fuzhou and Ningde joined subgroup1; Putian, Nanping, and Sanming formed subgroup 2; and Quanzhou, Longyan, and Zhangzhou joined subgroup 3. Notably, Xiamen is relatively independent, thus it is not part of any subgroup. Interestingly, subgroup 1 and subgroup 2 were closely linked with similar patterns, while subgroup 3 was more connected with Xiamen.
Figure 3 shows the subgroup results in 2020: Ningde, Putian, and Fuzhou formed subgroup 1; Nanping and Sanming created subgroup 2; and Longyan, Quanzhou, and Zhangzhou formed subgroup 3. It also shows that Xiamen had similar patterns with subgroup 3, while the first two subgroups were more connected.
The results of the cohesive subgroup analysis highlight the different compositions of cities during the pandemic, compared to the pre-pandemic and post-pandemic periods. One plausible explanation is that COVID-19 induced temporary changes in tourism links between cities. For example, in 2020, Putian joined subgroup 1 with Ningde and Fuzhou instead of remaining in the original subgroup 2 with Nanping and Sanming. Notably, Longyan, Zhangzhou, and Quanzhou formed a stable subgroup 3 from 2019 through 2021, which kept a close relationship with Xiamen in the three-phase period under analysis.

4.5. Comparison Analyses: Quadratic Assignment Procedure (QAP)

The quadratic assignment procedure (QAP), which is widely used in social network analysis, is a resampling-based method that calculates the correct standard errors similar to the bootstrap method (Burkard, 1984). This study employed the QAP method to explore the influencing factors of tourism spatial distribution in the Fujian province. Based on the R2 value, the data can explain 39.0%, 39.1%, and 37.8% of the tourism spatial distribution in the Fujian province during the three-phase period (see Table 7).
In 2019, geospatial proximity characteristics, city tourism brand awareness, and transport accessibility had statistically significant positive impacts on the tourism spatial distribution, while the impacts of infrastructure levels and tourist volume were negative and statistically significant (p < 0.1). As the most influential factor, the coefficient on geospatial proximity is significant at the 1% level. For every one-unit increase, there is a 48.2% increase in the degree of city connection. In contrast, the coefficient of tourist volume is significant at the 5% level (β=-0.168), indicating that the more tourists in a city (for a one-unit increase), the weaker the degree of tourism linkages between cities (a 16.8% decrease).
During the pandemic, geospatial proximity characteristics had an even more influential impact (β=0.513) on the tourism spatial distribution compared to the value in the pre-pandemic period (β=0.482). Similarly, for each one-unit increase in tourists, tourism economic linkages between cities increased by 33.3% (p<0.01). However, regional industrial structure, infrastructure level, tourism brand awareness, and transport accessibility had negative and statistically significant impacts on the tourism spatial distribution in the Fujian province. It is plausible that pandemic-related restrictions dominated society such that travel-related materials and information barely existed (Chen, Feng, & Gu, 2022), leading to weaker tourism economic ties between cities.
In 2021, geospatial proximity characteristics still play a significant role in the tourism spatial distribution (β=0.498). Interestingly, both tourism resources (β=0.001) and tourism brand market performance (β=0.002) had significant positive impacts on the tourism spatial distribution in the Fujian province, indicating that tourism resources and marketing activities increase city networks after the pandemic. In contrast, a one-unit increase in a single-city tourism brand awareness decreases the city networks by 13.3%.

5. Discussion

Geographic proximity characteristics, as the core influential factor, had a substantial effect on the tourism spatial distribution in the Fujian province before, during, and after the pandemic. Consistent with the friction of distance, interactions, and tourism linkages strengthen with the decrease in geographic distance (Gao, Yang, Li, & Qu, 2021).
Tourist volume had significant positive impacts on the tourism spatial distribution during the pandemic but had negative impacts in both pre-pandemic and post-pandemic periods. As long-distance travel was severely restricted due to the outbreak in 2020, tourists were more likely to travel to neighboring cities (Jeon & Yang, 2021). Thus, the spillover effect of tourist volume was positive and statistically significant during the pandemic, but this effect was negative in the and pre-and post-pandemic periods from the perspective of city networks. It also indicates that the tourism spatial distribution in the Fujian province was relatively independent, making the regional industrial structure have a statistically significant and negative effect during the pandemic, but not in pre- or post-pandemic periods. Similarly, city tourism brand awareness significantly contributed to the city networks in pre-pandemic, but its impact was statistically significant and negative during and after the pandemic due to travel restrictions (Borkowski, Jażdżewska-Gutta, & Szmelter-Jarosz, 2021). It appears to contradict the positive impact of tourism resources and city brand market performance in the post-pandemic period, suggesting the authorities consider tourism resource development as a whole instead of independent city brand promotions and marketing activities.
Notably, the coefficients on transport accessibility were positive in 2019 and 2021 but negative in 2020. These findings are in line with previous research, which found that transport accessibility had a positive effect on economic interactions and city networks in the and pre-and post-pandemic periods (Wang et al., 2020). However, greater transport accessibility is associated with weaker COVID-19 prevention/controls and restrictions (Yang et al., 2021). This in turn has been shown to weaken tourism links between cities because people are more likely to take long-distance travel. The coefficient of transport accessibility was therefore negative during the pandemic in 2020, and its impact on tourism spatial distribution was oppositely compared to the and pre-and post-pandemic periods.

6. Implications

6.1. Theoretical Implications

This research utilizes the modified gravity model to explore the social network of tourism across cities in the Fujian Province, China. It found that tourism spatial distribution was widely dispersed, with uneven and relatively independent tourism development in different cities. Findings also showed that tourism connection across cities was significantly reduced after the pandemic. This study contributes to the existing literature on the modified gravity model and social network analysis. It also sheds some light on the scope and structure of the spatial network of city tourism before, during, and after the COVID-19 pandemic. Specifically, this paper is one of the first attempts to examine the impacts of the COVID-19 pandemic on the structure of the tourism spatial network from the perspective of a three-phase period: pre-pandemic, present-pandemic, and post-pandemic. This study extends the modified gravity model to handle the pandemic impacts from 2019 to 2021, which are also of great value to enrich the gravity framework in tourism.
This study focuses on social network analysis at the city level, also enriching the literature on tourism spatial networks across cities. The spatial network structure of city tourism proved both complex and dynamic: the results of the cohesive subgroup analysis highlight the different compositions of cities during the pandemic, compared to the pre-pandemic and post-pandemic periods. One plausible explanation is that COVID-19 induced temporary changes in tourism links between cities. These findings expand the research scope of the tourism spatial networks and enrich the literature on destination marketing and management with the pandemic impacts.

6.2. Practical Implications

This research found that the tourism spatial distribution in the Fujian province was relatively dispersed, and tourism development in each city was independent, occurring with few (or little) interactions across cities. These findings have industrial implications for the balanced tourism development of the destinations. Specifically, previous research found that the weak links between cities indicate a need to reduce administrative boundaries, break down barriers, expand the tourism economic exchange, form close city networks, promote the tourist flow between cities, and encourage cooperation across cities (Gao, Peng, Lu, & Claramunt, 2022). Thus, related policies should be introduced to help cities work together to build sustainable networks of tourism development (Rastegar & Ruhanen, 2022).
The impacts of brand awareness and transport accessibility were positive in pre-pandemic but negative during the pandemic. In contrast, tourist volume was negative in pre-pandemic but was positive during the pandemic. Possible explanations include the friction of distance, the spillover effect of tourist volume, and the pandemic-related travel restrictions. Furthermore, tourism resources and market performance had significantly positive impacts on tourism spatial distribution in the post-pandemic. Destinations need interactions between cities such as sharing tourist volume, tourism resources, and infrastructure to achieve sustainable tourism development (Wang, Huang, Gong, & Cao, 2020). In this way, the tourism links across cities could be strengthened, and balanced tourism spatial networks could be gradually formed (Xiao, 2022). To promote sustainable and high-quality tourism development, cities in different positions should collaborate and differentiate tourism marketing promotions according to their tourism resources and specific characteristics (Wang & Ye, 2022).
The findings in this study help to promote the balanced and high-quality development of tourism (Wang & Ye, 2022). In the spatial distribution network of the tourism economy in Fujian Province, different cities have different positions and roles, and different development strategies should be introduced according to the characteristics of different cities, and more policies and policies should be introduced to help the tourism development of disadvantaged cities while strengthening city ties and to play the role of advantageous cities to drive the tourism development of disadvantaged cities (Rastegar & Ruhanen, 2022). Furthermore, fully explore the quality tourism resources of each city, integrate resources as well as complementary advantages, promote the sharing of tourism resources between cities, form a resource linkage situation, strengthen the tourism economic ties between cities, and jointly contribute to the development of urban tourism (Xie, Meng, Cenci, & Zhang, 2022). In addition, resources are the influencing elements of the spatial network of the tourism economy after the epidemic, so it is necessary to pay attention to the development and utilization of urban tourism resources and make full use of the tourism resources to promote the development of the tourism economy.

6.3. Limitations and Future Research Directions

The maximum gravitational value of the city tourism economy in the Fujian province from 2019 to 2021 was measured using the modified gravity model. The tourism spatial distribution, the network characteristics, and the clustering structure characteristics were examined using Ucinet 6. Further, the QAP regression analysis was conducted to assess the influencing factors of the tourism spatial distribution and compare their impacts in the three-phase period: pre-pandemic, present pandemic, and post-pandemic. This research found that geospatial proximity was the most influential factor of the tourism spatial distribution in the three-phase period before, during, and after the pandemic. Yet, the impacts of tourism resources and city brand market performance on the tourism spatial distribution fluctuated due to the pandemic. Therefore, attention needs to be paid to the utilization of tourism resources and city brand market performance in promoting tourism development across cities.
Although this paper employed the social network analysis to compare the differences in the three-phase period, more comprehensive analyses regarding the impacts of the COVID-19 pandemic could be conducted in the future when detailed micro-data become available. Additionally, city tourism brand awareness also needs to be fully examined to jointly promote coordinated tourism development across cities (Xie, Meng, Cenci, & Zhang, 2022). Since regions, countries, and areas have various natural and cultural characteristics and features, future studies may consider developing general theories regarding tourism spatial distribution to inform the industry practices in tourism destination management and marketing.

About the Authors

Huimin Song, Ph.D., is an Associate Professor in the College of Tourism at Huaqiao University, China. Dr. Song’s research interests include sport event and tourism management.
Wei Zeng is a graduate student in the College of Tourism at Huaqiao University, China. Her research interests include event management and tourism economic impacts.
Jamie M. Chen, Ph.D., is an Assistant Professor of Practice in the College of Business at North Dakota State University, USA. Dr. Chen focuses on consumer behavior and marketing.
Emily Ma, Ph.D., is a Professor in hospitality and events at the University of Surrey. Dr. Ma’s research focuses on hospitality, event, and tourism management.

Acknowledgments

This study was funded by Huaqiao University High-level Scholars Foundation (Grant No. 21SKBS003). We thank the funder for the financial support of the data collection.

Disclosure statement

The authors report there are no competing interests to declare.

References

  1. Anderson, J. E. (2011). The gravity model. Annual Review of Economics, 3(1), 133-160. [CrossRef]
  2. Bhuiyan, M. A., Crovella, T., Paiano, A., & Alves, H. (2021). A Review of Research on Tourism Industry, Economic Crisis and Mitigation Process of the Loss: Analysis on Pre, During and Post Pandemic Situation. Sustainability, 13(18), 10314. [CrossRef]
  3. Bian, J., Chen, W., & Zeng, J. (2022). Spatial distribution characteristics and influencing factors of traditional villages in China. International Journal of Environmental Research and Public Health, 19(8). [CrossRef]
  4. Borkowski, P., Jażdżewska-Gutta, M., & Szmelter-Jarosz, A. (2021). Lockdowned: Everyday mobility changes in response to COVID-19. Journal of Transport Geography, 90, 102906. [CrossRef]
  5. Burkard, R. E. (1984). Quadratic assignment problems. European Journal of Operational Research, 15(3), 283-289. [CrossRef]
  6. Butts, C. T. (2008). Social network analysis: A methodological introduction. Asian Journal of Social Psychology, 11(1), 13-41. [CrossRef]
  7. Chen, C., Feng, T., & Gu, X. (2022). Role of latent factors and public policies in travel decisions under COVID-19 pandemic: Findings of a hybrid choice model. Sustainable Cities and Society, 78, 103601. [CrossRef]
  8. dos Muchangos, L. S., Tokai, A., & Hanashima, A. (2017). Stakeholder analysis and social network analysis to evaluate the stakeholders of a MSWM system–A pilot study of Maputo City. Environmental Development, 24, 124-135. [CrossRef]
  9. Fujian Provincial Bureau of Statistics (2022). https://tjj.fujian.gov.cn/.
  10. Gao, J., Peng, P., Lu, F., & Claramunt, C. (2022). A multi-scale comparison of tourism attraction networks across China. Tourism Management, 90, 104489. [CrossRef]
  11. Gao, J., Shao, C., & Chen, S. (2022). Evolution and driving factors of the spatiotemporal pattern of tourism efficiency at the provincial level in China based on SBM-DEA model. International Journal of Environmental Research and Public Health, 19(16), 10118. [CrossRef]
  12. Gao, K., Yang, Y., Li, A., & Qu, X. (2021). Spatial heterogeneity in distance decay of using bike sharing: An empirical large-scale analysis in Shanghai. Transportation Research Part D: Transport and Environment, 94, 102814. [CrossRef]
  13. Gilbert, F., Simonetto, P., Zaidi, F., Jourdan, F., & Bourqui, R. (2011). Communities and hierarchical structures in dynamic social networks: Analysis and visualization. Social Network Analysis and Mining, 1(2), 83-95. [CrossRef]
  14. Goh, C., Li, H., & Li, M. (2014). A comparative analysis of domestic and international tourism spatial distribution: Trends and impacts. Journal of China Tourism Research, 10(4), 388-413. [CrossRef]
  15. Gomez, M., Fernandez, A. C., Molina, A., & Aranda, E. (2018). City branding in European capitals: An analysis from the visitor perspective. Journal of Destination Marketing & Management, 7, 190-201. [CrossRef]
  16. Gössling, S., Scott, D., & Hall, C. M. (2021). Pandemics, tourism and global change: A rapid assessment of COVID-19. Journal of Sustainable Tourism, 29(1), 1-20. [CrossRef]
  17. Green, N. (2007). Functional polycentricity: A formal definition in terms of social network.
  18. analysis. Urban Studies, 44(11), 2077-2103.
  19. Jeon, C.-Y., & Yang, H.-W. (2021). The structural changes of a local tourism network: Comparison of before and after COVID-19. Current Issues in Tourism, 24(23), 3324-3338. [CrossRef]
  20. Khan, A. (2018). The spatial distribution and relationship of tourist flow in Turkey. European Journal of Tourism Research, 19, 40-55.
  21. Kladou, S., & Kehagias, J. (2014). Assessing destination brand equity: An integrated approach. Journal of Destination Marketing & Management, 3(1), 2-10. [CrossRef]
  22. Kohler, H.-P., Behrman, J. R., & Watkins, S. C. (2001). The density of social networks and fertility decisions: Evidence from South Nyanza district, Kenya. Demography, 38(1), 43-58. [CrossRef]
  23. Li, J., Nguyen, T. H. H., & Coca-Stefaniak, J. A. (2021). Understanding post-pandemic travel behaviours–China's Golden Week. Journal of Hospitality and Tourism Management, 49, 84-88. [CrossRef]
  24. Li, S., Ren, T., Jia, B., & Zhong, Y. (2022). The spatial pattern and spillover effect of the eco-efficiency of regional tourism from the perspective of green development: An empirical study in China. Forests, 13(8). [CrossRef]
  25. Nadal, J. R., & Gallego, M. S. (2022). Gravity models for tourism demand modeling: Empirical review and outlook. Journal of Economic Surveys, 36(5), 1358-1409. [CrossRef]
  26. Nogle, J. M. (1994). The systems approach to international migration: An application of network analysis methods. International Migration, 32(2), 329-342. [CrossRef]
  27. Park, C., Kim, Y.-R., & Yeon, J. (2022). Stronger together: International tourists “spillover” into close countries. Tourism Economics, 13548166221098320. [CrossRef]
  28. Pascariu, G. C., & Ibănescu, B. C. (2018). Determinants and implications of the tourism multiplier effect in EU economies. Towards a core-periphery pattern?. Amfiteatru Economic, 20, 982-997. [CrossRef]
  29. Qi, J., Lu, Y., Han, F., Ma, X., & Yang, Z. (2022). Spatial distribution characteristics of the rural tourism villages in the Qinghai-Tibetan plateau and its influencing factors. International Journal of Environmental Research and Public Health, 19(15). [CrossRef]
  30. Otte, E., & Rousseau, R. (2002). Social network analysis: A powerful strategy, also for the information sciences. Journal of Information Science, 28(6), 441-453. [CrossRef]
  31. Rastegar, R., & Ruhanen, L. (2022). The injustices of rapid tourism growth: From recognition to restoration. Annals of Tourism Research, 97, 103504. [CrossRef]
  32. Ritchie, B. W., & Jiang, Y. (2019). A review of research on tourism risk, crisis and disaster management: Launching the annals of tourism research curated collection on tourism risk, crisis and disaster management. Annals of Tourism Research, 79, 102812. [CrossRef]
  33. Seok, H., Barnett, G. A., & Nam, Y. (2021). A social network analysis of international tourism flow. Quality & Quantity, 55(2), 419-439. [CrossRef]
  34. Sun, Y. Y., & Hou, G. L. (2021). Analysis on the spatial-temporal evolution characteristics and spatial network structure of tourism eco-efficiency in the Yangtze river delta urban agglomeration. International Journal of Environmental Research and Public Health, 18(5). [CrossRef]
  35. Utkarsh, & Sigala, M. (2021). A bibliometric review of research on COVID-19 and tourism: Reflections for moving forward. Tourism Management Perspectives, 40, 100912. [CrossRef]
  36. Vaishar, A., & Šťastná, M. (2022). Impact of the COVID-19 pandemic on rural tourism in Czechia Preliminary considerations. Current Issues in Tourism, 25(2), 187-191. [CrossRef]
  37. Vance-Borland, K., & Holley, J. (2011). Conservation stakeholder network mapping, analysis, and weaving. Conservation Letters, 4(4), 278-288. [CrossRef]
  38. Wang, G., & Ye, L. (2022). Spatial-temporal pattern of mismatch degree of high-quality tourism development and its formation mechanism in Taihu lake basin, China. Sustainability, 14(8), 4812. [CrossRef]
  39. Wang, J., Huang, X., Gong, Z., & Cao, K. (2020). Dynamic assessment of tourism carrying capacity and its impacts on tourism economic growth in urban tourism destinations in China. Journal of Destination Marketing & Management, 15. [CrossRef]
  40. Wang, R., Xia, B., Dong, S., Li, Y., Li, Z., Ba, D., Zhang, W. (2021). Research on the Spatial Differentiation and Driving Forces of Eco-Efficiency of Regional Tourism in China. Sustainability, 13(1):280. [CrossRef]
  41. Wang, Z., Liu, Q., Xu, J., & Fujiki, Y. (2020). Evolution characteristics of the spatial network structure of tourism efficiency in China: A province-level analysis. Journal of Destination Marketing & Management, 18, 100509. [CrossRef]
  42. Wang, K., Wang, M., Gan, C., Chen, Q., & Voda, M. (2021). Tourism economic network structural characteristics of national parks in the central region of China. Sustainability, 13(9). [CrossRef]
  43. Wang, S., Qin, C., Zhao, Y., Zhao, J., Han, Y. (2023). The Evolutionary Path of the Center of Gravity for Water Use, the Population, and the Economy, and Their Decomposed Contributions in China from 1965 to 2019. Sustainability, 15, 9275. [CrossRef]
  44. Wang, Z., Zhang, Z., & Jin, X. (2021). A study on the spatial network characteristics and effects of CDW generation in China. Waste Management, 128, 179-188. [CrossRef]
  45. Wen, J., Kozak, M., Yang, S., & Liu, F. (2021). COVID-19: Potential effects on Chinese citizens’ lifestyle and travel. Tourism Review, 76(1), 74-87. [CrossRef]
  46. Xiao, Y. (2022). Integrated development of the Yangtze river delta region: Status, problems and optimization. Studies in Social Science & Humanities, 1(2), 26-30. [CrossRef]
  47. Xie, Y., Meng, X., Cenci, J., & Zhang, J. (2022). Spatial pattern and formation mechanism of rural tourism resources in China: Evidence from 1470 national leisure villages. ISPRS International Journal of Geo-Information, 11(8). [CrossRef]
  48. Yang, Y., Cao, M., Cheng, L., Zhai, K., Zhao, X., & De Vos, J. (2021). Exploring the relationship between the COVID-19 pandemic and changes in travel behaviour: A qualitative study. Transportation Research Interdisciplinary Perspectives, 11, 100450. [CrossRef]
  49. Zhong, L., Sun, S., Law, R., & Li, X. (2021). Tourism crisis management: Evidence from COVID-19. Current Issues in Tourism, 24(19), 2671-2682. [CrossRef]
Preprints 99512 g001
Figure 1. Nine prefecture-level cities in the Fujian province.
Figure 1. Nine prefecture-level cities in the Fujian province.
Preprints 99512 g001
Preprints 99512 g002
Figure 2. Cohesive subgroups in 2019 and 2021.
Figure 2. Cohesive subgroups in 2019 and 2021.
Preprints 99512 g002
Preprints 99512 g003
Figure 3. 2020 cohesive subgroups .
Figure 3. 2020 cohesive subgroups .
Preprints 99512 g003
Table 1. Measurements of observed variables.
Table 1. Measurements of observed variables.
Variable Interpretation Index
Tourist volume (Khan, 2018) The number of tourist visits received by the city in a year. Difference matrix of total tourism recipients in a year.
Tourism resources (Qi, Lu, Han, Ma, & Yang, 2022) The variety and quality of tourism resources affect destination attractiveness, which in turn affects the traveler’s decision-making. The number of scenic spots above 4A in a city accounts for a matrix of differences in the number of scenic spots above 4A in the province in a year.
Transport accessibility (Bian, Chen, & Zeng, 2022) Accessibility influences tourists’ choice of destinations and travel decisions and also affects cross-regional cooperation between cities. Difference matrix of the ratio of highway miles to the urban area in a year. (square kilometers)
City brand market performance (Kladou & Kehagias, 2014) The market value of the city’s tourism brand and its potential for future development. Difference matrix of the ratio of city tourist volume to provincial one in a year.
City tourism brand awareness (Gomez, Fernandez, Molina, & Aranda, 2018). The higher the city’s tourism brand awareness, the more likely it is that the city and its attractions are chosen by tourists. Baidu index of cities and 5A scenic spots in a year
Infrastructure levels (Gao, Shao, & Chen, 2022) The level of infrastructure determines the city’s tourism carrying capacity. Difference matrix of the number of urban accommodation businesses in a year.
Regional industrial structures (Li, Ren, Jia, & Zhong, 2022) The differences in regional industrial structure characterize the complementarity of tourism development between regions. The greater the differences in regional industrial structure, the greater the complementarity of the tourism industry between cities, and the greater the likelihood of generating tourism linkages. Difference matrix of the ratio of the service sector value added to GDP in a year.
Geospatial proximity (Park, Kim, & Yeon, 2022) Geographically closer regions are more likely to have strong linkages, so geospatial proximity is an important factor influencing the spatial distribution of the inter-regional tourism economy. Takes a value of 1 if the cities are geospatially contiguous, and 0 otherwise.
Table 2. Summary of main variables.
Table 2. Summary of main variables.
Index Max Min Ave No. of Obs.
Tourist volume 10000.000 55.470 5071.779 9*3
Transport accessibility 5535.320 126.600 806.049 9*3
City brand market performance 0.527 0.027 0.111 9*3
City tourism brand awareness 11145.000 1492.000 3989.519 9*3
Infrastructure levels 290.000 49.000 135.852 9*3
Regional industrial structures 0.601 0.332 0.448 9*3
Tourism resources 0.174 0.035 0.111 9*3
Geospatial proximity 1.000 0.000 0.481 72*3
Notes: all the data were collected from the Statistical yearbook and Fujian Provincial Bureau of Statistics. The index data were calculated based on the instructions in Table 1, and the gravitational value was calculated via the modified gravity model below.
Table 3. Maximum gravitational value of a city to other cities.
Table 3. Maximum gravitational value of a city to other cities.
City 2019 2020 2021
Sum-g Max-city Max-g Sum-g Max-city Max-g Sum-g Max-city Max-g
Fuzhou 4190.546 Ningde 1373.721 3265.251 Putian 769.655 3169.855 Ningde 911.439
Xiamen 9154.398 Quanzhou 4065.512 7059.328 Zhangzhou 3461.616 9314.218 Zhangzhou 5097.084
Putian 543.605 Fuzhou 241.616 1127.990 Fuzhou 452.521 909.462 Fuzhou 358.152
Sanming 644.887 Longyan 160.212 511.885 Nanping 199.148 931.301 Nanping 323.723
Quanzhou 5215.985 Xiamen 3310.902 3395.316 Xiamen 2022.583 3983.433 Xiamen 2477.352
Zhangzhou 15160.711 Xiamen 11462.229 3054.102 Xiamen 2523.225 4562.779 Xiamen 3792.476
Nanping 712.361 Fuzhou 254.450 963.762 Fuzhou 310.554 980.060 Fuzhou 329.364
Longyan 2741.379 Xiamen 1338.962 794.973 Xiamen 324.535 1087.574 Xiamen 446.074
Ningde 1264.865 Fuzhou 961.160 703.643 Fuzhou 446.998 1043.898 Fuzhou 677.907
Notes: Sum-g is the sum of the gravitational values exerted by the city on other cities; Max-city is the one whose city exerts the largest gravitational value on other cities; Max-g is the gravitational value between the city and the max-city.
Table 4. Overall network characteristics.
Table 4. Overall network characteristics.
Index 2019 2020 2021
Network density 0.125 0.125 0.125
Degree centrality 0.278 0.255 0.267
Network correlation 1 1 1
Network hierarchy 0 0 0
Table 5. Degree centrality in the Fujian province.
Table 5. Degree centrality in the Fujian province.
City 2019 2020 2021
Degree centrality Benefit Degree centrality Benefit Degree centrality Benefit
Xiamen 2.504 No 2.497 No 2.581 No
Fuzhou 2.152 No 1.775 No 1.790 No
Quanzhou 1.431 No 1.113 No 1.095 No
Longyan 1.000 No 0.312 Yes 0.361 Yes
Ningde 0.561 Yes 0.444 Yes 0.547 Yes
Nanping 0.484 Yes 0.990 Yes 0.925 Yes
Zhangzhou 0.385 Yes 0.962 Yes 1.123 No
Sanming 0.296 Yes 0.290 Yes 0.290 Yes
Putian 0.186 Yes 0.617 Yes 0.433 Yes
Indegree 1 1 1
Table 6. Density matrix and Like matrix.
Table 6. Density matrix and Like matrix.
2019 Density matrix Like matrix
1 2 3 4 1 2 3 4
1 0.544 0.201 0.054 0.047 1 1 1 0 0
2 0.053 0.064 0.024 0.016 2 0 0 0 0
3 0.064 0.110 0.097 0.286 3 0 0 0 1
4 0.103 0.139 0.626 4 0 1 1
R2= 0.744
2020 Density matrix Like matrix
1 2 3 4 1 2 3 4
1 0.263 0.125 0.045 0.034 1 1 1 0 0
2 0.081 0.266 0.036 0.024 2 0 1 0 0
3 0.065 0.067 0.092 0.283 3 0 0 0 1
4 0.117 0.158 0.610 4 0 1 1
R2= 0.629
2021 Density matrix Like matrix
1 2 3 4 1 2 3 4
1 0.468 0.186 0.038 0.029 1 1 1 0 0
2 0.096 0.095 0.033 0.020 2 0 0 0 0
3 0.047 0.089 0.103 0.294 3 0 0 0 1
4 0.103 0.171 0.621 4 0 1 1
R2= 0.682
Note: Network density=0.125.
Table 7. Results of the QAP.
Table 7. Results of the QAP.
Variables 2019 2020 2021
β p Β p β p
Geospatial proximity characteristics 0.482*** 0.002 0.513*** 0.001 0.498*** 0.000
Regional industrial structures -0.142 0.201 -0.173** 0.021 0.004 0.464
Infrastructure levels -0.209* 0.051 -0.361*** 0.001 0.058 0.341
Tourism resources 0.057 0.485 0.014 0.497 0.001*** 0.000
City tourism brand awareness 0.073* 0.091 -0.224** 0.014 -0.133* 0.058
City brand market performance 0.027 0.716 -0.003 1.000 0.002*** 0.000
Tourist volume -0.168** 0.022 0.333*** 0.000 -0.301 0.346
Transport accessibility 0.016** 0.015 -0.109** 0.011 0.069 0.176
R2 0.390 0.391 0.378
Notes: * p < 0.1, ** p < 0.05, *** p < 0.01.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

© 2024 MDPI (Basel, Switzerland) unless otherwise stated