Journal cover Journal topic
Abstracts of the ICA
Journal topic
Volume 1
Abstr. Int. Cartogr. Assoc., 1, 209, 2019
https://doi.org/10.5194/ica-abs-1-209-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Abstr. Int. Cartogr. Assoc., 1, 209, 2019
https://doi.org/10.5194/ica-abs-1-209-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Jul 2019

15 Jul 2019

Analysis of Travel Patterns of Seoul Tourists by Trajectory Data Mining

Juyoon Lee, Youngok Kang, Nayeon Kim, Dongeun Kim, and Yearim Park Juyoon Lee et al.
  • Ewha Womans University, South Korea

Keywords: Social Network Service, Flickr, Trajectory Data Mining, Marcov Chain, Apriori

Abstract. It is necessary to identify the preferences and characteristics of tourists for vitalizing the tourism industry, as the tourism industry is of the fast-growing industries in the economic sector. A trajectory of tourists, a movement of tourists over time, is a very valuable information since it shows tourism characteristics, such as the length of tourists’ visit, tourists’ preferred attraction, the time of arrival to specific tourist attractions, and the movements in between different tourists’ destinations. Earlier studies regarding the movements of tourists were conducted by surveys, or by analyzing the data derived from GPS devices that were handed out to the research areas. However, these approaches using surveys or GPS devices are not only time consuming and requiring a lot of time for the analysis, but also difficult to detect the actual travel patterns. Recently, advances in mobile technologies and multimedia have allowed large amounts of user-generated data, such as travel photos, to be created and shared. This expands the use of such data on tourism industry since it enables to extract and analyze the trajectory of users by using geotagged data that were uploaded on Social Network Services (SNS)(Vu et al. 2015; Zheng et al. 2012).

The purpose of this research is to analyze the movement pattern of tourists who visited Seoul by using Flickr data, which is one type of SNS. For this, we went through the following process. First, we collected the Flickr data and preprocessed. Second, we separated the visitors and residents from the collected data. Third, we selected the main tourist attractions. Fourth, we produced trajectory data targeting tourists. Lastly, we analyzed the movement characteristics of tourists using trajectories.

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