Spatiotemporal analytics of topic trajectory

Jiangen He; Chaomei Chen

doi:10.1145/2968220.2968244

Spatiotemporal analytics of topic trajectory

Jiangen He, Chaomei Chen

Department of Library and Information Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 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 language	English
Title of host publication	VINCI 2016 - 9th International Symposium on Visual Information Communication and Interaction
Editors	Kang Zhang, Andreas Kerren
Publisher	Association for Computing Machinery
Pages	112-116
Number of pages	5
ISBN (Electronic)	9781450341493
DOIs	https://doi.org/10.1145/2968220.2968244
Publication status	Published - 2016 Sept 24
Event	9th International Symposium on Visual Information Communication and Interaction, VINCI 2016 - Dallas, United States Duration: 2016 Sept 24 → 2016 Sept 26

Publication series

Name	ACM International Conference Proceeding Series

Other

Other	9th International Symposium on Visual Information Communication and Interaction, VINCI 2016
Country/Territory	United States
City	Dallas
Period	16/9/24 → 16/9/26

Bibliographical note

Publisher Copyright:
© 2016 ACM.

All Science Journal Classification (ASJC) codes

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

Access to Document

10.1145/2968220.2968244

Cite this

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title = "Spatiotemporal analytics of topic trajectory",

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.",

author = "Jiangen He and Chaomei Chen",

note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 9th International Symposium on Visual Information Communication and Interaction, VINCI 2016 ; Conference date: 24-09-2016 Through 26-09-2016",

year = "2016",

month = sep,

day = "24",

doi = "10.1145/2968220.2968244",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

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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 proceeding › Conference contribution

TY - GEN

T1 - Spatiotemporal analytics of topic trajectory

AU - He, Jiangen

AU - Chen, Chaomei

PY - 2016/9/24

Y1 - 2016/9/24

N2 - 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.

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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M3 - Conference contribution

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T3 - ACM International Conference Proceeding Series

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EP - 116

BT - VINCI 2016 - 9th International Symposium on Visual Information Communication and Interaction

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ER -

Spatiotemporal analytics of topic trajectory

Abstract

Publication series

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Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

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Cite this