Journal cover Journal topic
Abstracts of the ICA
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Volume 1
Abstr. Int. Cartogr. Assoc., 1, 175, 2019
https://doi.org/10.5194/ica-abs-1-175-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Abstr. Int. Cartogr. Assoc., 1, 175, 2019
https://doi.org/10.5194/ica-abs-1-175-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Jul 2019

15 Jul 2019

Exploring Spatiotemporal Characteristics of Twitter data Using Topic Modelling Techniques

Young-Hoon Kim and Hyun-Jee Woo Young-Hoon Kim and Hyun-Jee Woo
  • Department of Geography Education, Korea National University of Education, Cheongju, Republic of Korea

Keywords: Twitter data, Topic modelling, Spatiotemporal characteristics, Geographical places, Social media data analysis

Abstract. This research purpose aims to explore the spatiotemporal aspects of social media data with Twitter data by using topic modelling techniques. The spatiotemporal limits are restricted in two areas of the Republic of Korea: Seoul and Jeju Island. This paper searches the semantics and geographical place characteristics of the Twitter data, and the semantics and place characteristics are regarded as topics in the topic modelling. This paper also discusses the temporal intensity over different spatial areas and visualizes the spatiotemporal patterns with GIS techniques.

As Twitter mobility message contains a user’s interests and behavioural patterns in the geo-tagged data corresponding to its location, it is possible to explore geographical locality and the user’s mobility over space by using textual ontological techniques such as topic modelling. Therefore, this paper attempts keywords searching and textural classification to classify the shared spatial activity patterns of the Twitter users. Consequently, two main analysis themes are explored: the tourist activity patterns attracting the visitors in Jeju over time and temporal periodicity for shopping and meal preference in Seoul, respectively.

In conclusion, this research represents a potential of the social network data that enables to fill the gap of spatiotemporal patterns of human beings over the online and mobile environment. Furthermore, our study confirms social data analysis techniques as an alternative geographical data source that can complement and replace the roles of spatial data, which could not be analysed in the conventional offline data.

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