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Visualization of research fronts and knowledge bases by three-dimensional areal densities of bibliographically coupled publications and co-citations

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  • Edgar Schiebel

    (AIT Austrian Institute of Technology GmbH, Technology Management)

Abstract

In this work the well known scientometric concepts of bibliographically coupled publications and co-cited references were applied to produce interactive maps of research fronts and knowledge bases of research fields. This article proposes a method and some standardization for the detection and visualization of research fronts and knowledge bases with two and three dimensional graphics inspired by geographical maps. Agglomerations of bibliographically coupled publications with a common knowledge base are identified and graphically represented by a density function of publications per area unit. The research fronts become visible if publications with similar vectors of common citations are associated and visualized as an ensemble in a three dimensional graphical representation as a mountain scenery measured with the help of a spatial density. Knowledge bases were calculated in the same way. Maps similar to the geographic representation of oceans and islands are used to visualize the two-dimensional spatial density function of references weighted by individual links. The proposed methodology is demonstrated by publications in the field of battery research.

Suggested Citation

  • Edgar Schiebel, 2012. "Visualization of research fronts and knowledge bases by three-dimensional areal densities of bibliographically coupled publications and co-citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 557-566, May.
    • Handle: RePEc:spr:scient:v:91:y:2012:i:2:d:10.1007_s11192-012-0626-8
    DOI: 10.1007/s11192-012-0626-8
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    References listed on IDEAS

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    1. M. M. Kessler, 1963. "Bibliographic coupling between scientific papers," American Documentation, Wiley Blackwell, vol. 14(1), pages 10-25, January.
    2. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    3. 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.
    4. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    5. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
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    Citations

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    Cited by:

    1. Xuezhao Wang & Yajuan Zhao & Rui Liu & Jing Zhang, 2013. "Knowledge-transfer analysis based on co-citation clustering," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 859-869, December.
    2. Mu-hsuan Huang & Chia-Pin Chang, 2015. "A comparative study on detecting research fronts in the organic light-emitting diode (OLED) field using bibliographic coupling and co-citation," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2041-2057, March.
    3. Bart Thijs & Edgar Schiebel & Wolfgang Glänzel, 2013. "Do second-order similarities provide added-value in a hybrid approach?," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(3), pages 667-677, September.
    4. Linqing Liu & Shiye Mei, 2016. "Visualizing the GVC research: a co-occurrence network based bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 953-977, November.
    5. Berndt Jesenko & Christian Schlögl, 2021. "The effect of web of science subject categories on clustering: the case of data-driven methods in business and economic sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6785-6801, August.
    6. Mu-Hsuan Huang & Chia-Pin Chang, 2014. "Detecting research fronts in OLED field using bibliographic coupling with sliding window," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1721-1744, March.
    7. Fabian Meyer-Brötz & Edgar Schiebel & Leo Brecht, 2017. "Experimental evaluation of parameter settings in calculation of hybrid similarities: effects of first- and second-order similarity, edge cutting, and weighting factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1307-1325, June.
    8. Alfonso Ávila-Robinson & Shintaro Sengoku, 2017. "Tracing the knowledge-building dynamics in new stem cell technologies through techno-scientific networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1691-1720, September.
    9. Liu, Yunmei & Yang, Liu & Chen, Min, 2021. "A new citation concept: Triangular citation in the literature," Journal of Informetrics, Elsevier, vol. 15(2).

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