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How are they different? A quantitative domain comparison of information visualization and data visualization (2000–2014)

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  • Meen Chul Kim

    (Drexel University College of Computing and Informatics)

  • Yongjun Zhu

    (Drexel University College of Computing and Informatics)

  • Chaomei Chen

    (Drexel University College of Computing and Informatics)

Abstract

Information visualization and data visualization are often viewed as similar, but distinct domains, and they have drawn an increasingly broad range of interest from diverse sectors of academia and industry. This study systematically analyzes and compares the intellectual landscapes of the two domains between 2000 and 2014. The present study is based on bibliographic records retrieved from the Web of Science. Using a topic search and a citation expansion, we collected two sets of data in each domain. Then, we identified emerging trends and recent developments in information visualization and data visualization, captivated in intellectual landscapes, landmark articles, bursting keywords, and citation trends of the domains. We found out that both domains have computer engineering and applications as their shared grounds. Our study reveals that information visualization and data visualization have scrutinized algorithmic concepts underlying the domains in their early years. Successive literature citing the datasets focuses on applying information and data visualization techniques to biomedical research. Recent thematic trends in the fields reflect that they are also diverging from each other. In data visualization, emerging topics and new developments cover dimensionality reduction and applications of visual techniques to genomics. Information visualization research is scrutinizing cognitive and theoretical aspects. In conclusion, information visualization and data visualization have co-evolved. At the same time, both fields are distinctively developing with their own scientific interests.

Suggested Citation

  • Meen Chul Kim & Yongjun Zhu & Chaomei Chen, 2016. "How are they different? A quantitative domain comparison of information visualization and data visualization (2000–2014)," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 123-165, April.
    • Handle: RePEc:spr:scient:v:107:y:2016:i:1:d:10.1007_s11192-015-1830-0
    DOI: 10.1007/s11192-015-1830-0
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    References listed on IDEAS

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    1. Meen Chul Kim & Chaomei Chen, 2015. "A scientometric review of emerging trends and new developments in recommendation systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 239-263, July.
    2. M.J. Cobo & A.G. López‐Herrera & E. Herrera‐Viedma & F. Herrera, 2011. "Science mapping software tools: Review, analysis, and cooperative study among tools," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(7), pages 1382-1402, July.
    3. Lea A I Vaas & Johannes Sikorski & Victoria Michael & Markus Göker & Hans-Peter Klenk, 2012. "Visualization and Curve-Parameter Estimation Strategies for Efficient Exploration of Phenotype Microarray Kinetics," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-18, April.
    4. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    5. Chaomei Chen & Fidelia Ibekwe-SanJuan & Jianhua Hou, 2010. "The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(7), pages 1386-1409, July.
    6. Chaomei Chen & Loet Leydesdorff, 2014. "Patterns of connections and movements in dual-map overlays: A new method of publication portfolio analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(2), pages 334-351, February.
    7. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    8. M.J. Cobo & A.G. López-Herrera & E. Herrera-Viedma & F. Herrera, 2011. "Science mapping software tools: Review, analysis, and cooperative study among tools," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(7), pages 1382-1402, July.
    9. Warren Torgerson, 1952. "Multidimensional scaling: I. Theory and method," Psychometrika, Springer;The Psychometric Society, vol. 17(4), pages 401-419, December.
    10. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

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    2. Huamei Shao & Gunwoo Kim & Qing Li & Galen Newman, 2021. "Web of Science-Based Green Infrastructure: A Bibliometric Analysis in CiteSpace," Land, MDPI, vol. 10(7), pages 1-19, July.
    3. Li, Nianqiao & Yan, Fei & Hirota, Kaoru, 2022. "Quantum data visualization: A quantum computing framework for enhancing visual analysis of data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    4. Floris Goerlandt & Jie Li & Genserik Reniers, 2020. "The Landscape of Risk Communication Research: A Scientometric Analysis," IJERPH, MDPI, vol. 17(9), pages 1-31, May.
    5. Roozbeh Haghnazar Koochaksaraei & Frederico Gadelha Guimarães & Babak Hamidzadeh & Sarfaraz Hashemkhani Zolfani, 2021. "Visualization Method for Decision-Making: A Case Study in Bibliometric Analysis," Mathematics, MDPI, vol. 9(9), pages 1-27, April.
    6. Jie Li & Floris Goerlandt & Karolien van Nunen & Koen Ponnet & Genserik Reniers, 2022. "Conceptualizing the Contextual Dynamics of Safety Climate and Safety Culture Research: A Comparative Scientometric Analysis," IJERPH, MDPI, vol. 19(2), pages 1-22, January.
    7. Tang, Ling & Wang, Haohan & Li, Ling & Yang, Kaitong & Mi, Zhifu, 2020. "Quantitative models in emission trading system research: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    8. Chi-Swian Wong, 2021. "Science Mapping: A Scientometric Review on Resource Curses, Dutch Diseases, and Conflict Resources during 1993–2020," Energies, MDPI, vol. 14(15), pages 1-48, July.
    9. Aida Khakimova & Xuejie Yang & Oleg Zolotarev & Maria Berberova & Michael Charnine, 2020. "Tracking Knowledge Evolution Based on the Terminology Dynamics in 4P-Medicine," IJERPH, MDPI, vol. 17(20), pages 1-19, October.

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