Exploring Park Visitors’ Activities in Hong Kong using Geotagged Photos
Understanding tourist activities could help attraction managers for appropriate planning and decision making. For a metropolitan city with limited land as Hong Kong, insight into what tourists have done in the urban area is vitally important. Tourists’ travel photos, tagged with geographical information, can assist attraction managers in identifying tourism hot spots and the activities that the visitors are interested in at certain spots. This study examined major visitor’s activities in the urban parks in Hong Kong by utilizing the geotagged photos posted on the social media sites. The results indicated that visitors had different interests in different parks. Moreover, the focuses of park visitors are different between local residents and international tourists. By spotting the photo locations, attraction managers can identify the tourists’ concentration so as to arrange better management on crowd control and visitors’ safety.
KeywordsTourist activities Geotagged photos Urban parks Hong Kong Attractions management
- Ding, X., Liu, B., & Zhang, L. (2009). Entity discovery and assignment for opinion mining applications. In Proceedings of the 15th ACM SIG KDD Conference on Knowledge Discovery and Data Mining (pp. 1125–1134), Paris, France.Google Scholar
- HKTB. (2014). Visitor profile report 2014. Retrieved August 21, 2015, from http://securepartnernet.hktb.com/filemanager/intranet/dept_info/private_20/paper/Visitor-Pro/Profile2014/Visitor_Profile_2014_0.pdf
- Kisilevich, S., Mansmann, F. & Keim, D. (2010). P-DBSCAN: A density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos. In Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application Article. Article no. 38, Bethesda, MD.Google Scholar
- Schmallegger, D., Carson, D., & Jacobsen, D. (2009). The use of photographs on consumer generated content websites: Practical Implications for destination image analysis. In S. Nalin (Ed.), Tourism informatics: Visual travel recommender systems, social communities, and user interface design (pp. 243–260). Hershey, PA: Hershey.Google Scholar
- Ye, Q., Zhang, Z., & Law, R. (2009) Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems with Applications, 36 (3 Part 2), 6527–6535.Google Scholar