Research on the tourist market of Beijing 5A scenic spot based on social media data
Bibliographic Data
| ID | 21699685 |
|---|---|
| Authors | Qihao Weng (0000-0002-2498-0934, Henan Normal University), Qian-Wen Weng (Henan Normal University), Jin-Wei Yan (0000-0002-3777-6916, University of Copenhagen), Xiaoling Lu (0000-0001-5664-4401, Henan Normal University), Xiao-Tong Lu (Henan Normal University), Hao Zhang (0000-0003-0232-5565, Henan Normal University), Hao Zhanga (Henan Normal University), Qiang Fu (0009-0000-9606-6001, Henan Normal University, corresponding author) |
| Year | 2025 |
| Pages | 1-19 |
| Publication date | 2025-07-16 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Current Issues in Tourism (JOURNAL) |
| Journal identifiers | ISSN: 1368-3500 • E-ISSN: 1747-7603 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/13683500.2025.2531451 |
| OpenAlex | W4412481697 |
| Language | EN |
| References cited | 43 |
Studying the tourist market structure is essential for understanding regional differences in travel preferences and mobility patterns. This study examines the spatiotemporal distribution and driving factors of domestic tourist flows to Beijing’s 5A scenic spots from 2019 to 2023, based on multi-source data including over one million records from Sina Weibo, Point of Interest (POI) datasets, and official statistical yearbooks. Grounded in time geography, core – periphery theory, and spatial heterogeneity, the research employs spatial–temporal techniques such as LISA path analysis and the Geographically and Temporally Weighted Regression (GTWR) model. The results reveal pronounced spatial disparities in tourist market structure, with economically developed provinces contributing the majority of visitors. The market follows an ‘olive-type’ hierarchy, where secondary-tier regions dominate. LISA path analysis identifies directional shifts in market centres over time, while GTWR confirms that economic, transportation, and service-related factors exert regionally differentiated impacts on tourist flows. This study advances theoretical understanding by integrating dynamic spatial models with a multi-scalar theoretical framework, offering valuable insights for region-specific tourism policy and destination management strategies
Advertising · Beijing · Business · China · Geography · Social media · Tourism · World Wide Web · Computer Science · Digital Marketing and Social Media · Diverse Aspects of Tourism Research · Sport and Mega-Event Impacts · Marketing
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| Citation velocity | historical |
|---|---|
| Highly cited | No |