Evidence map›Paper›PMID 38912436›Full record

ArticleHeliyon2024

A study on the spatial differences between the tourism network attention and tourism flow in Shanghai, China.

Yuxin Feng, Xiaoyu Lv, Yunxia Tian, Zhuo Li, Jiayu Xue, Yulan Chen

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Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yuxin FengNorthwest Normal University, Tourism College, Lanzhou, 730070, China.
Xiaoyu LvNorthwest Normal University, Tourism College, Lanzhou, 730070, China.
Yunxia TianNorthwest Normal University, Tourism College, Lanzhou, 730070, China.
Zhuo LiNorthwest Normal University, Tourism College, Lanzhou, 730070, China.
Jiayu XueNorthwest Normal University, Tourism College, Lanzhou, 730070, China.
Yulan ChenNorthwest Normal University, Tourism College, Lanzhou, 730070, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The tourism network attention as a reflection of tourism demand is closely related to the tourism flow, the differences between the two has become an important criterion for judging the efficiency of destination tourism demand conversion, as well as a manifestation of the balance and coordination of destination tourism industry. Against the background of insufficient release of tourism demand in China, research on the development differences between tourism network attention and tourism flow can provide a basis for demand-side management and high-quality development. Based on the theory of spatial mismatch, this research analyzes the spatial development difference between the tourism network attention and the tourism flow in Shanghai from 2012 to 2021 using methods such as center of gravity model, spatial mismatch index, and two-dimensional combination matrix. The results show: (1) According to the analysis of the center of gravity model, there was a shift of the center of gravity of tourism network attention with the direction of "south-north", while the tourism flow shifted "west-east"; the center of gravity between tourism network attention and tourism flow began to diverge from 2012 to 2016, gradually converged from 2016 to 2019, and then gradually deviated again after 2020. (2) According to the spatial mismatch index, the spatial mismatch types between tourism network attention and tourism flow in various Districts of Shanghai are mainly negative and low mismatch, with high mismatch areas mainly distributed in the eastern and southwestern parts of Shanghai. (3) Combining the two-dimensional combination matrix, it can be observed that the spatial development difference between tourism network attention and tourism flow in Shanghai show a characteristic of "enlarging-shrinking-enlarging". From 2012 to 2016, the spatial development difference between tourism network attention and tourism flow in Shanghai continuously expanded; from 2017 to 2019, the spatial development difference continuously shrank; and from 2020 to 2021, the spatial differences expanded again. (4) The analysis results of the panel data model show that the development of tourism resources and the level of tourism services have a positive promoting effect on the evolution of spatial mismatch, while the social basic development environment has a negative effect. The research results not only meet the needs of evaluating the high-quality development of the tourism industry in the current economic restructuring, providing direction for the high-quality development of the regional tourism industry, but also enrich the research content of network attention as a tourism element participating in the evaluation of tourism industry development quality, and deepen the relationship research between network attention and tourism flow.

Indexed as

Network attentionSpatial mismatchTourism industry

Identifiers

PMID38912436
PMCPMC11190535

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.