Tourists’ digital footprint: The spatial patterns of tourist flows in Qingdao, China

Chinese tourists in Nordic countries: An analysis of spatio-temporal behavior using geo-located travel blog data

“…Qunar.com ( https://www.qunar.com/ ), as China's leading travel search engine, is the largest Chinese online travel sharing website. The data from Qunar.com has been applied, for example, to the study of tourists' rating behavior ( Zhang, Zhang, & Yang, 2016 ), tourist movement ( Jin et al, 2018 ; Mou, Zheng, et al, 2020 ) and destination image ( Lian & Yu, 2017 ). The website provides an intelligent editing scheme for travel blogs: when writing blogs on the website, tourists can set up spatio-temporal labels (recorded in the source code of the blog's webpage) of the attractions involved in the blog.…”

“…In view of this, this paper provides a detailed analysis of the spatio-temporal behavior of Chinese tourists in Nordic countries by using geo-located travel blog data. By doing so, the paper is answering to the call voiced by Mou et al (2020) : the implicit information in the text data should be utilized in the geographic analysis of tourist flows to deepen the analysis of tourists' spatio-temporal behavior by uncovering the potential reasons behind seasonal variation of tourist flows and to incorporate textual data into the analysis to enrich it with descriptions on the drivers of tourist flows. The structure of this paper is as follows: the second section reviews the most relevant earlier literature on geo-located travel blogs and the behavior of Chinese tourists.…”

Tourism Flow Between Major Cities During China’s National Day Holiday: A Social Network Analysis Using Weibo Check-in Data

“…Fabien et al [18], collecting geotagged photos published by users on the photo sharing website Flickr from April 2005 to April 2007 and combining mobile phone signaling, conduct a survey of the tourism flows in Florence and Italy in time and space respectively. It is found that tourists are more inclined to visit Florence in July and August in terms of time; while in terms of space, the most tourists visit the Santa Maria in Florence Province, and Rome and Florence are most closely connected; Mou et al [19], obtaining the online travel notes from Qunar.com, analyze the spatial mode of tourism flow in Qingdao by using the gravity center model and constructing the tourism flow network, and find that the tourism resources in coastal and inland areas of Qingdao are obviously different and the coastal tourism core area has been gradually formed;Chung et al [20] analyze the tourism mode of Korean backpackers to Europe from the central indicators, finding that tourists are more apt to travel to London and Paris. Önder et al [21], collecting all Flickr tourism photo data from 2007 to 2011 in Austria, establish polynomial regression prediction model, and predict the number of tourists in different cities in Austria through Flickr data;Su et al [22], using Weibo check-in data, compare the temporal and spatial characteristics of day trippers and general tourists, tourists from Shenzhen and other tourists form Chinese mainland visiting Hong Kong.…”

Role Of Tourist Technology Platforms After The Pandemic

“…https://www.wttc.org/about/media-centre/press-releases/press-releases/2020/latest-research-from-wttc-shows-anincrease-in-jobs-at-risk-in-travel-and-tourism/ (Miles & Shipway, 2020), including reorienting tourists to eco-friendly and green tourism (Zhu & Deng, 2020). The pandemic literally "spurred" the digitalization of the economy, predicting in a special way the future transformation of the entire tourism sector and its opportunities in this direction (Mou et al, 2020).…”