Google and apple mobility data as predictors for European tourism during the COVID-19 pandemic: A neural network approach

Benedek Nagy, Manuela Rozalia Gabor, Ioan Bogdan Baco?, Moaaz Kabil, Kai Zhu and Lóránt Dénes Dávid
Additional contact information
Benedek Nagy: Sapientia Hungarian University of Transylvania, Romania
Manuela Rozalia Gabor: George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, Romania
Ioan Bogdan Baco?: George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, Romania
Moaaz Kabil: Hungarian University of Agriculture and Life Sciences, Hungary; Cairo University, Egypt
Kai Zhu: Hubei University, China
Lóránt Dénes Dávid: John von Neumann University, Hungary; Hungarian University of Agriculture and Life Sciences, Hungary

Equilibrium. Quarterly Journal of Economics and Economic Policy, 2023, vol. 18, issue 2, 419-459

Abstract: Research background: The COVID-19 pandemic has caused unprecedented disruptions to the global tourism industry, resulting in significant impacts on both human and economic activities. Travel restrictions, border closures, and quarantine measures have led to a sharp decline in tourism demand, causing businesses to shut down, jobs to be lost, and economies to suffer. Purpose of the article: This study aims to examine the correlation and causal relationship between real-time mobility data and statistical data on tourism, specifically tourism overnights, across eleven European countries during the first 14 months of the pandemic. We analyzed the short longitudinal connections between two dimensions of tourism and related activities. Methods: Our method is to use Google and Apple's observational data to link with tourism statistical data, enabling the development of early predictive models and econometric models for tourism overnights (or other tourism indices). This approach leverages the more timely and more reliable mobility data from Google and Apple, which is published with less delay than tourism statistical data. Findings & value added: Our findings indicate statistically significant correlations between specific mobility dimensions, such as recreation and retail, parks, and tourism statistical data, but poor or insignificant relations with workplace and transit dimensions. We have identified that leisure and recreation have a much stronger influence on tourism than the domestic and routine-named dimensions. Additionally, our neural network analysis revealed that Google Mobility Parks and Google Mobility Retail & Recreation are the best predictors for tourism, while Apple Driving and Apple Walking also show significant correlations with tourism data. The main added value of our research is that it combines observational data with statistical data, demonstrates that Google and Apple location data can be used to model tourism phenomena, and identifies specific methods to determine the extent, direction, and intensity of the relationship between mobility and tourism flows.

Keywords: mobility; tourism; Google mobility data; Apple mobility data; Europe; COVID-19 pandemic (search for similar items in EconPapers)
JEL-codes: C45 F14 R12 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.24136/eq.2023.013 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pes:ierequ:v:18:y:2023:i:2:p:419-459

Access Statistics for this article

Equilibrium. Quarterly Journal of Economics and Economic Policy is currently edited by Adam P. Balcerzak

More articles in Equilibrium. Quarterly Journal of Economics and Economic Policy from Institute of Economic Research Contact information at EDIRC.
Bibliographic data for series maintained by Adam P. Balcerzak ( this e-mail address is bad, please contact ).