Open AccessCombining Conventional Statistics and Big Data to Map Global Tourism Destinations Before COVID-19
Author(s) -
Czesław Adamiak,
Barbara Szyda
Publication year - 2021
Publication title -
journal of travel research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.403
H-Index - 132
eISSN - 1552-6763
pISSN - 0047-2875
DOI - 10.1177/00472875211051418
Subject(s) - tourism , destinations , trips architecture , geography , dominance (genetics) , population , big data , domestic tourism , descriptive statistics , covid-19 , tourism geography , business , economic geography , regional science , marketing , statistics , computer science , infectious disease (medical specialty) , data mining , mathematics , chemistry , archaeology , pathology , sociology , biochemistry , parallel computing , medicine , demography , disease , gene
World Tourism Organization (UNWTO) is the major source of internationally comparable data on tourism. However, UNWTO data has two drawbacks: it focuses on international trips and ignores differences between regions within individual countries. Alternative sources of big data are increasingly used to enhance tourism statistics. In this paper, we combine traditional information sources with gridded population dataset and Airbnb data to address the limitations of UNWTO statistics. We produce a map of world tourism destinations measured by the number of tourism visits and tourism expenditure in 2019, before the COVID-19 pandemic. We then identify hot spots of tourism and compare the level of spatial concentration of tourism to that of global population and economy. The results illustrate how supply and demand shape the global distribution of tourism, highlight the dominance of domestic travels in global tourism mobility and may help planning tourism policy in the face of current global challenges.