Advancing tourism resilience and data science using multimodal data

被引：0

作者：

Liu, Yi ^{[1
,2
,3
]}

Jiang, Yougen ^{[1
]}

Luo, Qiuju ^{[1
,4
]}

机构：

[1] Sun Yat Sen Univ, Sch Tourism Management, Zhuhai, Peoples R China

[2] Minist Culture & Tourism, Key Lab Intelligent Assessment Technol Sustainable, Zhuhai, Peoples R China

[3] Minist Culture & Tourism China, Key Lab Sustainable Tourism Smart Assessment Techn, Zhuhai, Peoples R China

[4] Sun Yat Sen Univ, Ctr Tourism Planning & Res, Guangzhou, Peoples R China

来源：

JOURNAL OF POLICY RESEARCH IN TOURISM LEISURE AND EVENTS | 2024年

基金：

中国国家自然科学基金;

关键词：

Multimodal data; tourism industry; resilience; selection biases; COVID-19;

D O I：

10.1080/19407963.2024.2435581

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

To address data selection bias in tourism research and objectively assess tourism resilience, this study uses the impact of COVID-19 on China's tourism industry as a case. By using data from POI, UGC, nighttime lights, corporate revenue, and infections, the study develops a multimodal 'city-enterprise-tourist' data approach to analyze tourism resilience comprehensively. The multimodal approach disproves the assumed correlation between city size and tourism resilience during public crises while uncovering resilience differences within the tourism sector. As a result, the multimodal data method effectively avoids data selection bias and offers a detailed understanding of tourism resilience. The study highlights the importance of integrating diverse data sources and targeted resilience improvements in tourism resilience research. It provides a comprehensive framework for assessing the impact of public health crises on tourism, contributing to both data science and tourism resilience research.

引用

页数：11