Unsupervised Hierarchical Clustering Approach for Tourism Market Segmentation Based on Crowdsourced Mobile Phone Data

Area of Science:

• Tourism Studies
• Data Science
• Transportation Planning

Background:

• Effective tourism management requires understanding traveler behavior and mobility patterns.
• Traditional survey methods for data collection are often time-consuming and costly.
• Emerging sensor technologies, like smartphones, offer efficient data collection for tourism research.

Purpose of the Study:

• To develop and implement a hierarchical clustering approach using smartphone geo-localized data.
• To identify meaningful tourism market segments and analyze their characteristics and mobility behavior.
• To demonstrate the approach's applicability in a real-world tourism destination.

Main Methods:

• Utilized hierarchical clustering on geo-localized smartphone data.
• Collected data from 1505 users over five months via the Zeeland app.
• Interpreted spatio-temporal and visiting patterns to define market segments.

Main Results:

• Identified two major tourist clusters and four sub-clusters.
• Characterized segments based on detailed insights into their mobility behavior.
• Demonstrated the practical application of the clustering approach in Zeeland, Netherlands.

Conclusions:

• Smartphone data analysis effectively segments tourists based on mobility patterns.
• The identified segments provide valuable insights for personalized tourism services.
• This data-driven approach enhances transportation system planning and tourism management strategies.