Dataset for: Analysis of International Tourism Flows Using a Gravity Model and Explainable Machine Learning

This dataset supports the research article “Analysis of International Tourism Flows: A Gravity Model and an Explainable Machine Learning Approach.”

The dataset contains panel data on international tourism flows to Mongolia from 27 origin countries covering the period 2000–2024. The data were compiled and processed to analyze the determinants of international tourism demand using a combination of gravity model estimation and machine learning techniques.

Data sources include:

• World Bank (macroeconomic indicators such as GDP)

• National Statistics Office of Mongolia (tourist arrivals and tourism expenditure)

• CEPII Distance Database (bilateral geographic distance)

• World Economic Forum (tourism competitiveness indicators)

Main variables include:

• Tourist arrivals to Mongolia

• GDP of origin countries

• GDP of destination country

• Bilateral geographical distance

• Purchasing power parity (PPP)

• Average tourist expenditure

• Tourism infrastructure indicators

The dataset was prepared for empirical analysis using econometric gravity models and machine learning algorithms including Random Forest, LightGBM, and XGBoost. The processed dataset is provided to facilitate transparency and reproducibility of the research results.