Spatiotemporal analytics of topic trajectory

Jiangen He, Chaomei Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Spatially and temporally relevant text data generated on the Internet by users worldwide is of great value for investigating and understanding emerging trends of user interests and how they may evolve over time and space. However, exploring the spatiotemporal text data and characterizing the evolution of topics over time and space are challenging due to the complexity of such data and associated activities. This paper proposes a new approach to exploring the spatiotemporal text data with visual filters. We introduce a notion of topic trajectory to depict the spatiotemporal evolution of topics. Multiple coordinated visualizations provided in our visualization system enable users to explore topic trajectories and develop their contextual awareness in terms of how information ows across diffierent regions. We demonstrate the use of our system with an analysis of a dataset contributed by users of widely used science mapping software CiteSpace.

Original languageEnglish
Title of host publicationVINCI 2016 - 9th International Symposium on Visual Information Communication and Interaction
EditorsKang Zhang, Andreas Kerren
PublisherAssociation for Computing Machinery
Pages112-116
Number of pages5
ISBN (Electronic)9781450341493
DOIs
Publication statusPublished - 2016 Sep 24
Event9th International Symposium on Visual Information Communication and Interaction, VINCI 2016 - Dallas, United States
Duration: 2016 Sep 242016 Sep 26

Publication series

NameACM International Conference Proceeding Series

Other

Other9th International Symposium on Visual Information Communication and Interaction, VINCI 2016
CountryUnited States
CityDallas
Period16/9/2416/9/26

Fingerprint

Visualization
Trajectories
Internet

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cite this

He, J., & Chen, C. (2016). Spatiotemporal analytics of topic trajectory. In K. Zhang, & A. Kerren (Eds.), VINCI 2016 - 9th International Symposium on Visual Information Communication and Interaction (pp. 112-116). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/2968220.2968244
He, Jiangen ; Chen, Chaomei. / Spatiotemporal analytics of topic trajectory. VINCI 2016 - 9th International Symposium on Visual Information Communication and Interaction. editor / Kang Zhang ; Andreas Kerren. Association for Computing Machinery, 2016. pp. 112-116 (ACM International Conference Proceeding Series).
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He, J & Chen, C 2016, Spatiotemporal analytics of topic trajectory. in K Zhang & A Kerren (eds), VINCI 2016 - 9th International Symposium on Visual Information Communication and Interaction. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 112-116, 9th International Symposium on Visual Information Communication and Interaction, VINCI 2016, Dallas, United States, 16/9/24. https://doi.org/10.1145/2968220.2968244

Spatiotemporal analytics of topic trajectory. / He, Jiangen; Chen, Chaomei.

VINCI 2016 - 9th International Symposium on Visual Information Communication and Interaction. ed. / Kang Zhang; Andreas Kerren. Association for Computing Machinery, 2016. p. 112-116 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Spatially and temporally relevant text data generated on the Internet by users worldwide is of great value for investigating and understanding emerging trends of user interests and how they may evolve over time and space. However, exploring the spatiotemporal text data and characterizing the evolution of topics over time and space are challenging due to the complexity of such data and associated activities. This paper proposes a new approach to exploring the spatiotemporal text data with visual filters. We introduce a notion of topic trajectory to depict the spatiotemporal evolution of topics. Multiple coordinated visualizations provided in our visualization system enable users to explore topic trajectories and develop their contextual awareness in terms of how information ows across diffierent regions. We demonstrate the use of our system with an analysis of a dataset contributed by users of widely used science mapping software CiteSpace.

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He J, Chen C. Spatiotemporal analytics of topic trajectory. In Zhang K, Kerren A, editors, VINCI 2016 - 9th International Symposium on Visual Information Communication and Interaction. Association for Computing Machinery. 2016. p. 112-116. (ACM International Conference Proceeding Series). https://doi.org/10.1145/2968220.2968244