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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Benedek Nagy & Manuela Rozalia Gabor & Ioan Bogdan Baco? & Moaaz Kabil & Kai Zhu & Lóránt Dénes Dávid, 2023. "Google and apple mobility data as predictors for European tourism during the COVID-19 pandemic: A neural network approach," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 18(2), pages 419-459, June.
    • Handle: RePEc:pes:ierequ:v:18:y:2023:i:2:p:419-459
    DOI: 10.24136/eq.2023.013
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    References listed on IDEAS

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    More about this item

    Keywords

    mobility; tourism; Google mobility data; Apple mobility data; Europe; COVID-19 pandemic;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade

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