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The ecological approach to text visualization

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  • James A. Wise

Abstract

This article presents both theoretical and technical bases on which to build a “science of text visualization.” These conceptually produce “the ecological approach,” which is rooted in ecological and evolutionary psychology. The basic idea is that humans are genetically selected from their species history to perceptually interpret certain informational aspects of natural environments. If information from text documents is visually spatialized in a manner conformal with these predilections, its meaningful interpretation to the user of a text visualization system becomes relatively intuitive and accurate. The SPIRE text visualization system, which images information from free text documents as natural terrains, serves as an example of the “ecological approach” in its visual metaphor, its text analysis, and its spatializing procedures.

Suggested Citation

  • James A. Wise, 1999. "The ecological approach to text visualization," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 50(13), pages 1224-1233.
    • Handle: RePEc:bla:jamest:v:50:y:1999:i:13:p:1224-1233
    DOI: 10.1002/(SICI)1097-4571(1999)50:133.0.CO;2-4
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    Cited by:

    1. Gaviria-Marin, Magaly & Merigó, José M. & Baier-Fuentes, Hugo, 2019. "Knowledge management: A global examination based on bibliometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 194-220.
    2. Zhigao Liu & Yimei Yin & Weidong Liu & Michael Dunford, 2015. "Visualizing the intellectual structure and evolution of innovation systems research: a bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 135-158, April.
    3. Feng Hu & Wei Liu & Sang-Bing Tsai & Junbin Gao & Ning Bin & Quan Chen, 2018. "An Empirical Study on Visualizing the Intellectual Structure and Hotspots of Big Data Research from a Sustainable Perspective," Sustainability, MDPI, vol. 10(3), pages 1-19, March.
    4. Yue Guiling & Siti Aisyah Panatik & Mohammad Saipol Mohd Sukor & Noraini Rusbadrol & Li Cunlin, 2022. "Bibliometric Analysis of Global Research on Organizational Citizenship Behavior From 2000 to 2019," SAGE Open, , vol. 12(1), pages 21582440221, February.
    5. Skupin, André, 2009. "Discrete and continuous conceptualizations of science: Implications for knowledge domain visualization," Journal of Informetrics, Elsevier, vol. 3(3), pages 233-245.
    6. Aaron W. Baur, 0. "Harnessing the social web to enhance insights into people’s opinions in business, government and public administration," Information Systems Frontiers, Springer, vol. 0, pages 1-21.
    7. Hugo Baier-Fuentes & José M. Merigó & José Ernesto Amorós & Magaly Gaviria-Marín, 2019. "International entrepreneurship: a bibliometric overview," International Entrepreneurship and Management Journal, Springer, vol. 15(2), pages 385-429, June.
    8. Matthew Ward & Wei Peng & Xiaoning Wang, 2004. "Hierarchical visual data mining for large-scale data," Computational Statistics, Springer, vol. 19(1), pages 147-158, February.
    9. Aaron W. Baur, 2017. "Harnessing the social web to enhance insights into people’s opinions in business, government and public administration," Information Systems Frontiers, Springer, vol. 19(2), pages 231-251, April.

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