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Research on the Structural Characteristics and Evolutionary Process of China’s Tourism Investment Spatial Correlation Network
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 sp...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9736402/ https://www.ncbi.nlm.nih.gov/pubmed/36497736 http://dx.doi.org/10.3390/ijerph192315661 |
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author | Li, Haijian Xie, Wujie |
author_facet | Li, Haijian Xie, Wujie |
author_sort | Li, Haijian |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9736402 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97364022022-12-11 Research on the Structural Characteristics and Evolutionary Process of China’s Tourism Investment Spatial Correlation Network Li, Haijian Xie, Wujie Int J Environ Res Public Health Article 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. MDPI 2022-11-25 /pmc/articles/PMC9736402/ /pubmed/36497736 http://dx.doi.org/10.3390/ijerph192315661 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Haijian Xie, Wujie Research on the Structural Characteristics and Evolutionary Process of China’s Tourism Investment Spatial Correlation Network |
title | Research on the Structural Characteristics and Evolutionary Process of China’s Tourism Investment Spatial Correlation Network |
title_full | Research on the Structural Characteristics and Evolutionary Process of China’s Tourism Investment Spatial Correlation Network |
title_fullStr | Research on the Structural Characteristics and Evolutionary Process of China’s Tourism Investment Spatial Correlation Network |
title_full_unstemmed | Research on the Structural Characteristics and Evolutionary Process of China’s Tourism Investment Spatial Correlation Network |
title_short | Research on the Structural Characteristics and Evolutionary Process of China’s Tourism Investment Spatial Correlation Network |
title_sort | research on the structural characteristics and evolutionary process of china’s tourism investment spatial correlation network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9736402/ https://www.ncbi.nlm.nih.gov/pubmed/36497736 http://dx.doi.org/10.3390/ijerph192315661 |
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