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Machine learning analysis of the effects of COVID-19 on migration patterns
Farzona Mukhamedova1, Ivan Tyukin2
1King's College London, London, WC2 R2LS, UK. farzona.mukhamedova@kcl.ac.uk.
Scientific Reports
|November 30, 2024
Summary
The COVID-19 pandemic shifted European tourism from diverse patterns to global uniformity. Socio-economic factors like GDP and culture influence tourist mobility corridors, highlighting the need for resilient infrastructure and SME support.
Area of Science:
- Socio-economics
- Machine Learning
- Mobility Studies
Background:
- The COVID-19 pandemic significantly disrupted global travel and tourism.
- Understanding shifts in tourist mobility is crucial for economic resilience and regional development.
Purpose of the Study:
- To analyze the impact of the COVID-19 pandemic on European tourist mobility patterns from 2019-2021.
- To model and evaluate the stability of socio-economic corridors under variable conditions.
Main Methods:
- Conceptualizing countries as monomers emitting radiation for pattern analysis.
- Applying perturbed clustering, Principal Component Analysis (PCA), and dendrograms.
- Integrating socio-economic data (GDP, cultural, linguistic similarities) with machine learning.
Main Results:
- Tourist mobility shifted from heterogeneous (bimodal) in 2019 to uniform (unimodal) in 2020-2021 due to pandemic restrictions.
- Tourist preferences correlate with GDP, cultural, and linguistic similarities, explaining corridor cohesion and fragility.
- Emerging corridors (e.g., Red Octopus) showed fragility, while established ones (e.g., Blue Banana) demonstrated resilience.
Conclusions:
- The study provides a robust framework for assessing mobility patterns amidst global crises.
- Targeted policy interventions, including transport infrastructure and SME support, are vital for mitigating disruptions.
- Enhancing economic resilience requires anticipating shifts in tourist behavior and strengthening socio-economic corridors.
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