Big data in tourism research: A literature review
By Jingjing Li, Lizhi Xu, L. Tang, Shouyang Wang, Ling Li. Tourism Management, 2018
874 citations recorded by the source corpus.
Abstract Even at an early stage, diverse big data have been applied to tourism research and made an amazing improvement. This paper might be the first attempt to present a comprehensive literature review on different types of big data in tourism research. By data sources, the tourism-related big data fall into three primary categories: UGC data (generated by users), including online textual data and online photo data; device data (by devices), including GPS data, mobile roaming data, Bluetooth data, etc.; transaction data (by operations), including web search data, webpage visiting data, online booking data, etc. Carrying different information, different data types address different tourism issues. For each type, a systematical analysis is conducted from the perspectives of research focuses, data characteristics, analytic techniques, major challenges and further directions. This survey facilitates a thorough understanding of this sunrise research and offers valuable insights into its future prospects.
Subfields: Sociology and Political Science, Transportation
View Paper: https://www.semanticscholar.org/paper/fa863e822087b6101274cece1e2184fdf943a78c
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Citations in the corpus
- Destination Image Analytics through Traveller-Generated Content (2019, background): “In a similar review of literature from the Web of Science, ScienceDirect, SAGE Journals Online, Emerald Insight, Wiley Online Library and Springer published between 2007 and 2016, the authors examined 144 journal papers and 21 conference papers [41].”
- Exploring spatio-temporal changes of city inbound tourism flow: The case of Shanghai, China (2020, background): “There have been a number of studies applying Flickr data to investigate tourism (Li, Xu, Tang, Wang, & Li, 2018), since the increasing numbers of digital photos with geo-tags in Flickr offers great opportunities to study people's travel experiences, behavior and preferences (Yuan & Medel, 2016;…”