Destination attraction clustering: segmenting tourist movement patterns with geotagged information

Abstract

This study used geotagged Instagram information to analyze tourist movements in Hong Kong. Data were collected over a four-year period from over 600,000 posts at 202 attractions. Destination-wide analyses of cluster attractions can illustrate relationships between attractions and facilitate comprehensive multi-destination planning. Using geotagged information for tourist movement patterns between attractions, we connected attractions based on tourists’ common motivations and described the corresponding clustering effects by sorting the attractions into four clusters. A new framework was used to reveal the characteristics of these intra-cluster attractions through three dimensions: theme, visit volume, and importance level by attractiveness propagation rank.

摘要

这项研究使用带有地理标签的Instagram信息来分析香港的游客移动。数据采集了四年时间里在202个景点上标记的超过六十万条的帖子。目的地范围内的景点集群分析可以体现景点间的联系, 并更好地促进跨目的地的旅游规划。我们使用地理标签数据获取游客在景点间的移动模式, 并根据游客的共同目的来连接景点, 并透过将景点分成四个群组来描述他们相应的集群效应。最终从景点主题、访问量及由吸引力传播次序排列得出的重要程度这三个维度来揭示各个集群内景点的特点, 并提出新的框架。

Keywords:

• Attraction cluster
• attraction rank
• network analysis
• attractiveness
• tourist movement pattern
• Hong Kong

关键词:

• 景点集群；景点排序；网络分析；吸引力；游客移动模式；香港

Additional information

Notes on contributors

Xiaolin Zhou

Xiaolin Zhou is a PhD candidate in Department of Land Surveying and Geo-informatics at the Hong Kong Polytechnic University, Hong Kong, China. Her research interests include GIScience, location-based social networks and text mining.

Zhaoyu Chen

Zhaoyu Chen is a Lecturer at the Macao Institute for Tourism Studies, Macao, China. She received her PhD in School of Hotel & Tourism Management from the Hong Kong Polytechnic University, Hong Kong, China. Dr. Chen had practical experience in both UNESCO Bangkok and UNESCO Beijing assisting with the heritage related projects. Her research interests are cultural tourism, heritage conservation, festival studies and smart city development.