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Journals / Acta Infologica / 2025 / Volume: 9 Issue: 2

Comparison of European Union Countries in Travel and Tourism Development Using Principal Component Analysis

Mehmet Hakan Özdemir(TURKISH - GERMAN UNIVERSITY)0000-0002-7174-9807
Pages
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Publication Date
2025-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The analysis indicated that three principal components accounted for the majority of variance in the initial dataset, comprising seventeen pillars, and similar countries in terms of travel and tourism development were revealed on the plot constructed accordingly.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Tourism is undoubtedly one of the most significant factors contributing to the economy and development of countries.
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Key Findings & Data:This index, which has undergone multiple updates over the years, comprises five dimensions and 17 pillars.
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Conclusion & Impact:The analysis indicated that three principal components accounted for the majority of variance in the initial dataset, comprising seventeen pillars, and similar countries in terms of travel and tourism development were revealed on the plot constructed accordingly.
🔬Design:Modeling & Simulation
📍Scope:2024 Verileri
⚙️Variables:Travel and Tourism Development Index · European Union · Principal Components Analysis

Abstract

Tourism is undoubtedly one of the most significant factors contributing to the economy and development of countries. Consequently, numerous countries intend to attract more tourists annually by investing in the tourism sector. Countries compete in this domain and seek to enhance their development levels. The Travel and Tourism Development Index (TTDI), featured in the Insight Report by the World Economic Forum (WEF), makes it possible to compare countries in terms of travel and tourism development. This index, which has undergone multiple updates over the years, comprises five dimensions and 17 pillars. This study aims to visualize the travel and tourism development of 27 European Union (EU) countries using principal component analysis (PCA), one of the most well-known dimensionality reduction and unsupervised machine learning methods, by using the 2024 TTDI data of these countries to compare them with each other and identify similar countries. The analysis indicated that three principal components accounted for the majority of variance in the initial dataset, comprising seventeen pillars, and similar countries in terms of travel and tourism development were revealed on the plot constructed accordingly.

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Cite This Article

Özdemir, M. H. (2025). Comparison of European Union Countries in Travel and Tourism Development Using Principal Component Analysis. Acta Infologica, Volume: 9 Issue: 2. https://doi.org/10.26650/acin.1645953

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