Tourism Flow Between Major Cities During China’s National Day Holiday: A Social Network Analysis Using Weibo Check-in Data
Holiday tourism flow is a significant indicator to evaluate the development of tourism. The exploration of the rule of tourism flow between cities can not only provide reasonable suggestions for stimulating demand, promoting consumption and economic development, but also make crucial significance fo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9293277/ |
| _version_ | 1811274458519830528 |
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| author | Anjun Li Naixia Mou Lingxian Zhang Tengfei Yang Wenbao Liu Feng Liu |
| author_facet | Anjun Li Naixia Mou Lingxian Zhang Tengfei Yang Wenbao Liu Feng Liu |
| author_sort | Anjun Li |
| collection | DOAJ |
| description | Holiday tourism flow is a significant indicator to evaluate the development of tourism. The exploration of the rule of tourism flow between cities can not only provide reasonable suggestions for stimulating demand, promoting consumption and economic development, but also make crucial significance for the management of tourism destinations and the optimization of spatial structure of tourism flow. Based on Weibo check-in data, this paper, by using social network analysis, studies the spatial distribution and network structure characteristics of tourism flow in 50 major cities in China during the National Day holiday in 2018. The results show that: 1) the tourism flow connection of the main cities in China represents a diamond shaped spatial structure with “Beijing-Shanghai-Guangzhou-Chengdu” as the core. There exists spatial heterogeneity in different levels of tourism flow intensity and proximity and selectivity in tourism links between cities; 2) the intensity of tourism connection between cities in China is clearly divided into different levels. On the basis of index of degree of centrality, Beijing and Shanghai are far higher than other cities;3) there are obvious differences between core nodes and edge nodes, with the core nodes often composed of cities with high economic development or rich tourism resources. Although the number is small, it plays a significant role in driving the edge cities; 4) the urban tourism flow network is relatively stable, but most cities have relatively weak tourism links and more small-scale tourism flows. In the division of cohesive subgroups, the fifth and sixth subgroups are not only the main tourist sources but also the main destinations. Whether it is the internal connection of subgroups or the connection with other subgroups, tourism flow has a very high density of connection. |
| first_indexed | 2024-04-12T23:19:30Z |
| format | Article |
| id | doaj.art-50ae0cbe1ab244299c05d8a8251d43fe |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T23:19:30Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-50ae0cbe1ab244299c05d8a8251d43fe2022-12-22T03:12:34ZengIEEEIEEE Access2169-35362020-01-01822567522569110.1109/ACCESS.2020.30446139293277Tourism Flow Between Major Cities During China’s National Day Holiday: A Social Network Analysis Using Weibo Check-in DataAnjun Li0https://orcid.org/0000-0003-4691-6674Naixia Mou1https://orcid.org/0000-0003-1700-5943Lingxian Zhang2https://orcid.org/0000-0001-5060-5751Tengfei Yang3https://orcid.org/0000-0001-7405-6697Wenbao Liu4https://orcid.org/0000-0001-9236-5127Feng Liu5https://orcid.org/0000-0002-2034-4711College of Geomatics, Shandong University of Science and Technology, Qingdao, ChinaCollege of Geomatics, Shandong University of Science and Technology, Qingdao, ChinaCollege of Geomatics, Shandong University of Science and Technology, Qingdao, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaCollege of Geomatics, Shandong University of Science and Technology, Qingdao, ChinaCollege of Geomatics, Shandong University of Science and Technology, Qingdao, ChinaHoliday tourism flow is a significant indicator to evaluate the development of tourism. The exploration of the rule of tourism flow between cities can not only provide reasonable suggestions for stimulating demand, promoting consumption and economic development, but also make crucial significance for the management of tourism destinations and the optimization of spatial structure of tourism flow. Based on Weibo check-in data, this paper, by using social network analysis, studies the spatial distribution and network structure characteristics of tourism flow in 50 major cities in China during the National Day holiday in 2018. The results show that: 1) the tourism flow connection of the main cities in China represents a diamond shaped spatial structure with “Beijing-Shanghai-Guangzhou-Chengdu” as the core. There exists spatial heterogeneity in different levels of tourism flow intensity and proximity and selectivity in tourism links between cities; 2) the intensity of tourism connection between cities in China is clearly divided into different levels. On the basis of index of degree of centrality, Beijing and Shanghai are far higher than other cities;3) there are obvious differences between core nodes and edge nodes, with the core nodes often composed of cities with high economic development or rich tourism resources. Although the number is small, it plays a significant role in driving the edge cities; 4) the urban tourism flow network is relatively stable, but most cities have relatively weak tourism links and more small-scale tourism flows. In the division of cohesive subgroups, the fifth and sixth subgroups are not only the main tourist sources but also the main destinations. Whether it is the internal connection of subgroups or the connection with other subgroups, tourism flow has a very high density of connection.https://ieeexplore.ieee.org/document/9293277/Tourism flownetwork structuresocial network analysiscityWeibo check-in |
| spellingShingle | Anjun Li Naixia Mou Lingxian Zhang Tengfei Yang Wenbao Liu Feng Liu Tourism Flow Between Major Cities During China’s National Day Holiday: A Social Network Analysis Using Weibo Check-in Data IEEE Access Tourism flow network structure social network analysis city Weibo check-in |
| title | Tourism Flow Between Major Cities During China’s National Day Holiday: A Social Network Analysis Using Weibo Check-in Data |
| title_full | Tourism Flow Between Major Cities During China’s National Day Holiday: A Social Network Analysis Using Weibo Check-in Data |
| title_fullStr | Tourism Flow Between Major Cities During China’s National Day Holiday: A Social Network Analysis Using Weibo Check-in Data |
| title_full_unstemmed | Tourism Flow Between Major Cities During China’s National Day Holiday: A Social Network Analysis Using Weibo Check-in Data |
| title_short | Tourism Flow Between Major Cities During China’s National Day Holiday: A Social Network Analysis Using Weibo Check-in Data |
| title_sort | tourism flow between major cities during china x2019 s national day holiday a social network analysis using weibo check in data |
| topic | Tourism flow network structure social network analysis city Weibo check-in |
| url | https://ieeexplore.ieee.org/document/9293277/ |
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