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

Meen Chul Kim (), Yongjun Zhu () and Chaomei Chen ()
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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

Scientometrics, 2016, vol. 107, issue 1, No 8, 123-165

Abstract: 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.

Keywords: Visual analytics; Domain analysis; Information visualization; Data visualization; Data science; Scientometrics (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s11192-015-1830-0

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