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Uncovering Tourism Network

This project is to uncover the structure of travel network in Republic of Korea. Mobile big data representing massive flow of international travelers have been analyzed by the spatial network analytics. The project configures the macro and meso network structures of tourism destinations across 250 cities, which provide important insights for regional marketing and destination cooperation strategy.

Collaborator: Korean Tourism Organization

Understanding travel behaviors/patterns of international travelers visiting South Korea: Applications of big data analysis

This project is to characterize and model travel behavior of international tourists to South Korea, which suggest an innovative approach to integrating mobile big data analytics into destination planning. As a result, innovative strategies are advocated to destination planners so as to make the destinations more sustainable and competitive.

Collaborator: Korean Tourism Organization

Travel Pattern Analysis of Foreign Visitors using Mobile Big Data

This project is to discover tourism network community in South Korea by analyzing mobile sensor data. This project suggests the tourism typology of network structures in South Korea, suggesting the configuration of destination collaboration and enhancement of transportation efficiency.

Collaborator: Korean Tourism Organization

Big Data Analysis on Travel Patterns of Visitors to Jeju

The project aims to discover the travel patterns of tourists visiting Jeju island by analyzing massive mobility data, such as smart card and navigation data. The results come up with important insights (hot places/key travel routes and activities in Jeju), which can serve as foundations of developing destination recommendation systems.

Collaborator: Jeju Tourism Organization

Uncover Space-Time Dynamics of Tourist Mobility Patterns from Large-Scale Mobile Positioning Data

Collaborator: The Hong Kong Polytechnic University

Artificial Intelligence in Hospitality and Tourism: User Experiences and Organizational Efficiency

Collaborator: The Hong Kong Polytechnic University

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