Research on the Structural Characteristics and Evolutionary Process of China’s Tourism Investment Spatial Correlation Network
Bibliographic Data
| ID | 15513679 |
|---|---|
| Authors | Haijian Li (0000-0003-4912-9529, Jiangsu Normal University), Wujie Xie (0000-0002-3490-0918, Jiangsu Normal University, corresponding author) |
| Year | 2022 |
| Volume | 19 |
| Issue | 23 |
| Pages | 15661-15661 |
| Publication date | 2022-11-25 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph192315661 |
| PMID | 36497736 |
| OpenAlex | W4310038690 |
| Language | EN |
| Citations received | 1 |
| References cited | 16 |
The paper uses the revised gravity model to measure the intensity of tourism investment spatial correlation, constructs the spatial correlation matrix of tourism investment, and uses the social network method to analyze the structural characteristics and evolutionary process of tourism investment spatial correlation network based on 31 provinces in China from 2000 to 2016. The findings revealed: (1) The spatial correlation quantity of interprovincial tourism investment continues to grow, with Beijing, Jiangsu, Zhejiang, Shanghai, Shandong, and Guangdong at the top of the list. (2) Overall network density and correlation are rising, and the spatial correlation of interprovincial tourism investment is increasingly close. Network hierarchy and network efficiency are decreasing, and network stability has been enhanced. (3) Degree centrality and closeness centrality of each province have shown a significant increase; Beijing, Shandong, Guangdong, Jiangsu, Zhejiang, and Shanghai are the top six and in the center of the network. Most provinces have improved betweenness centrality, Beijing, Guangdong, Shandong, Liaoning, Shaanxi, and Hunan have a strong betweenness centrality with strong intermediary capacity. (4) The core area mainly includes eastern and central provinces, and the periphery areas mainly include western and northeastern provinces. The network connection density of the core and periphery areas shows an increasing trend, while the network linkage density between the core and periphery areas shows a downward trend
Beijing · Betweenness centrality · Business · Centrality · China · Closeness · Economic geography · Geography · Investment (military · Political science · Regional science · Statistics · Tourism · Diverse Aspects of Tourism Research · Mathematics · Sport and Mega-Event Impacts · Wine Industry and Tourism
A social network analysis of international tourism flow
Network Analysis and Tourism
Identification of tourist hot spots based on social networks
Foreign direct investment in tourism—flows and volumes
Evaluating spatial centrality for integrated tourism management in rural areas using GIS and network analysis
Estimating value in Baltimore, Maryland
The evolution and transformation of a tourism destination network
Network effects on cooperation in destination website development
Tourism and regional income inequality
Destination Networks
Multicity trip patterns
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |