1887
Volume 25, Issue 1
  • ISSN 0142-5471
  • E-ISSN: 1569-979X

Abstract

Abstract

Data visualizations are often represented in public discourse as objective proof of facts. However, a visualization is only a single translation of reality, just like any other media, representation devices, or modes of representation. If we wish to encourage thoughtful, informed, and literate consumption of data visualizations, it is crucial that we consider why they are often presented and interpreted as objective. We reflect theoretically on data visualization as a system of representation historically anchored in science, rationalism, and notions of objectivity. It establishes itself within a lineage of conventions for visual representations which extends from the Renaissance to the present and includes perspective drawing, photography, cinema and television, as well as computer graphics. By examining our tendency to see credibility in data visualizations and grounding that predisposition in a historical context, we hope to encourage more critical and nuanced production and interpretation of data visualizations in the public discourse.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 license.
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2020-03-16
2020-11-25
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References

  1. Arnheim, R.
    (2009) Art and visual perception: A psychology of the creative eye; the new version (Expanded and rev. ed., with some new ill., [Nachdr.], 50th anniversary printing). Berkeley, Calif.: University of California Press.
    [Google Scholar]
  2. Bateman, S., Mandryk, R. L., Gutwin, C., Genest, A., McDine, D., & Brooks, C.
    (2010) Useful junk?: the effects of visual embellishment on comprehension and memorability of charts. InProceedings of the 28th international conference on Human factors in computing systems – CHI ’10 (p.2573). Atlanta, Georgia, USA: ACM Press.
    [Google Scholar]
  3. Baudelaire, C., & University of Florida, G. A. S. L.
    (1956) Mirror of art: Critical studies. Garden City, N.Y.: Doubleday.
    [Google Scholar]
  4. Berger, J.
    (Ed.) (1987) Ways of seeing: Based on the BBC television series with John Berger (Reprinted). London: BBC [u.a.].
    [Google Scholar]
  5. Bertin, J.
    (2010) Semiology of graphics. Wisconsin: The University of Wisconsin Press. (Original work published 1983).
    [Google Scholar]
  6. Bloch, M., Byron, L., Carter, S., & Cox, A.
    (2008) The ebb and flow of movies: Box office receipts 1986–2008. [Interactive graphic] NYTimes.com. Retrieved fromarchive.nytimes.com/www.nytimes.com/interactive/2008/02/23/movies/20080223_REVENUE_GRAPHIC.html
    [Google Scholar]
  7. Borkin, M. A., Vo, A. A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., & Pfister, H.
    (12/2013) What Makes a Visualization Memorable?IEEE Transactions on Visualization and Computer Graphics, 19(12), 2306–2315. 10.1109/TVCG.2013.234
    https://doi.org/10.1109/TVCG.2013.234 [Google Scholar]
  8. Buneman, P., Khanna, S., & Wang-Chiew, T.
    (2001) Why and Where: A Characterization of Data Provenance. InJ. Van den Bussche & V. Vianu (Eds.), Database Theory – ICDT 2001 (Vol.1973, pp.316–330). Berlin, Heidelberg: Springer Berlin Heidelberg. 10.1007/3‑540‑44503‑X_20
    https://doi.org/10.1007/3-540-44503-X_20 [Google Scholar]
  9. Callahan, S. P., Freire, J., Santos, E., Scheidegger, C. E., Silva, C. T., & Vo, H. T.
    (2006) VisTrails: visualization meets data management. InProceedings of the 2006 ACM SIGMOD international conference on Management of data – SIGMOD ’06 (p.745). Chicago, IL, USA: ACM Press. 10.1145/1142473.1142574
    https://doi.org/10.1145/1142473.1142574 [Google Scholar]
  10. Cleveland, W. S.
    (1985) The elements of graphing data (Vol.2). Wadsworth Advanced Books and Software Monterey, CA.
    [Google Scholar]
  11. Davidson, S. B., & Freire, J.
    (2008) Provenance and scientific workflows: challenges and opportunities. InProceedings of the 2008 ACM SIGMOD international conference on Management of data – SIGMOD ’08 (p.1345). Vancouver, Canada: ACM Press. 10.1145/1376616.1376772
    https://doi.org/10.1145/1376616.1376772 [Google Scholar]
  12. D’Ignazio, C., & Klein, L. F.
    (2016) Feminist data visualization. Presented at theWorkshop on Visualization for the Digital Humanities (VIS4DH), Baltimore: IEEE. Retrieved fromhttps://static1.squarespace.com/static/574dd51d62cd942085f12091/t/5c157dfe562fa7836b296000/1544912383037/Feminist_Data_Visualization.pdf
    [Google Scholar]
  13. Dimara, E., Bezerianos, A., & Dragicevic, P.
    (2017) The Attraction Effect in Information Visualization. IEEE Transactions on Visualization and Computer Graphics, 23(1), 471–480. 10.1109/TVCG.2016.2598594
    https://doi.org/10.1109/TVCG.2016.2598594 [Google Scholar]
  14. Dörk, M., Feng, P., Collins, C., & Carpendale, S.
    (2013) Critical InfoVis: Exploring the Politics of Visualization. InCHI ’13 Extended Abstracts on Human Factors in Computing Systems. New York, NY: ACM – Critical InfoVis: Exploring the Politics of Visualization. 10.1145/2468356.2468739
    https://doi.org/10.1145/2468356.2468739 [Google Scholar]
  15. Elkins, J.
    (1995) The poetics of perspective. Ithaca and London: Cornell.
    [Google Scholar]
  16. Flusser, V.
    (2000) Towards a philosophy of photography. London: Reaktion. (Original work published 1983)
    [Google Scholar]
  17. Goodman, N.
    (1976) Languages of art. Indianapolis: Hackett Publishing Co.
    [Google Scholar]
  18. (1978) Ways of Worldmaking. Indianapolis: The Harvester Press.
    [Google Scholar]
  19. Hajian, S., Bonchi, F., & Castillo, C.
    (2016) Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining. InProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD ’16 (pp.2125–2126). San Francisco, California, USA: ACM Press. 10.1145/2939672.2945386
    https://doi.org/10.1145/2939672.2945386 [Google Scholar]
  20. Hall, P.
    (2008) Critical Visualization. InP. Antonelli & Museum of Modern Art (New York N.Y.) (Eds.), Design and the elastic mind (pp.120–131). New York: Museum of Modern Art.
    [Google Scholar]
  21. Havre, S., Hetzler, B., & Nowell, L.
    (2000) ThemeRiver: visualizing theme changes over time. InIEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings (pp.115–123). 10.1109/INFVIS.2000.885098
    https://doi.org/10.1109/INFVIS.2000.885098 [Google Scholar]
  22. Ivins, W. M.
    (1938) On the rationalization of sight: With an examination of three Renaissance texts on perspective. New York: Da Capo Press.
    [Google Scholar]
  23. Kahneman, D.
    (2012) Thinking, fast and slow. London: Penguin Books.
    [Google Scholar]
  24. Kennedy, H., Hill, R. L., Aiello, G., & Allen, W.
    (2016) The work that visualisation conventions do. Information, Communication and Society, 19(6), 715–735. 10.1080/1369118X.2016.1153126
    https://doi.org/10.1080/1369118X.2016.1153126 [Google Scholar]
  25. King, D.
    (1997) The commissar vanishes: The falsification of photographs and art in Stalin’s Russia (1st ed). New York: Metropolitan Books.
    [Google Scholar]
  26. Klinghoffer, A. J.
    (2006) The power of projections: how maps reflect global politics and history. Westport, Conn: Praeger Publishers.
    [Google Scholar]
  27. Kosara, R.
    (2016) An Empire Built On Sand. Proceedings of the Beyond Time and Errors on Novel Evaluation Methods for Visualization – BELIV ’16. doi:  10.1145/2993901.2993909
    https://doi.org/10.1145/2993901.2993909 [Google Scholar]
  28. Manovich, L.
    (06/1999) Database as Symbolic Form. Convergence: The International Journal of Research into New Media Technologies, 5(2), 80–99. 10.1177/135485659900500206
    https://doi.org/10.1177/135485659900500206 [Google Scholar]
  29. Martinez-Conde, S., & Macknik, S. L.
    (8/2007) Windows on the Mind. Scientific American, 297(2), 56–63. 10.1038/scientificamerican0807‑56
    https://doi.org/10.1038/scientificamerican0807-56 [Google Scholar]
  30. Mauri, M., Elli, T., Caviglia, G., Uboldi, G., & Azzi, M.
    (2017) RAWGraphs: A Visualisation Platform to Create Open Outputs. Proceedings of the 12th Biannual Conference on Italian SIGCHI Chapter – CHItaly ’17, 1–5. doi:  10.1145/3125571.3125585
    https://doi.org/10.1145/3125571.3125585 [Google Scholar]
  31. McCurdy, N., Gerdes, J., & Meyer, M.
    (2018) A Framework for Externalizing Implicit Error Using Visualization. IEEE Transactions on Visualization and Computer Graphics. doi:  10.1109/TVCG.2018.2864913
    https://doi.org/10.1109/TVCG.2018.2864913 [Google Scholar]
  32. Mitchell, W. J.
    (2001) The reconfigured eye: Visual truth in the post-photographic era (4. print). Cambridge, MA: The MIT Press.
    [Google Scholar]
  33. Monmonier, M. S.
    (1991) How to lie with maps. Chicago: University of Chicago Press.
    [Google Scholar]
  34. Offenhuber, D., & Telhan, O.
    (2015) Indexical visualization: The data-less information display. InU. Ekman (Ed.), Ubiquitous computing, complexity and culture (pp.288–301). New York: Routledge, Taylor & Francis Group. 10.4324/9781315781129‑31
    https://doi.org/10.4324/9781315781129-31 [Google Scholar]
  35. Onuoha, M.
    (2019) An overview and exploration of the concept of missing datasets. MimiOnuoha/missing-datasets. Retrieved fromhttps://github.com/MimiOnuoha/missing-datasets
    [Google Scholar]
  36. Panofsky, E.
    (1997) Perspective as symbolic form. (C. S. Wood, Trans.) (1st paperback ed.). New York, NY: Zone Books.
    [Google Scholar]
  37. Ragan, E. D., Endert, A., Sanyal, J., & Chen, J.
    (2016) Characterizing Provenance in Visualization and Data Analysis: An Organizational Framework of Provenance Types and Purposes. IEEE Transactions on Visualization and Computer Graphics, 22(1), 31–40. 10.1109/TVCG.2015.2467551
    https://doi.org/10.1109/TVCG.2015.2467551 [Google Scholar]
  38. Ribecca, S.
    (2014) The data visualisation catalogue. Retrieved fromhttps://datavizcatalogue.com/
    [Google Scholar]
  39. Schofield, T., Dörk, M., & Dade-Robertson, M.
    (2013) Indexicality and Visualization: Exploring Analogies with Art, Cinema and Photography. InProceedings of the 9th ACM Conference on Creativity & Cognition. (pp.175–184). New York, NY: ACM. 10.1145/2466627.2466641
    https://doi.org/10.1145/2466627.2466641 [Google Scholar]
  40. Sharma, J., & Sharma, R.
    (2017) Analysis of Key Photo Manipulation Cases and their Impact on Photography. The IISU-JOA Journal of Arts, pp.88–99.
    [Google Scholar]
  41. Sontag, S.
    (1977, June23). Photography Unlimited. The New York review of books. Retrieved fromhttps://www.nybooks.com/articles/1977/06/23/photography-unlimited/
    [Google Scholar]
  42. Stefaner, M.
    (2013) Finding truth & beauty in data: Moritz Stefaner at European Communication Summit 2013. Retrieved fromhttps://vimeo.com/71798105
    [Google Scholar]
  43. Thudt, A., Perin, C., Willett, W., & Carpendale, S.
    (2017) Subjectivity in personal storytelling with visualization. Information Design Journal, 23(1), 48–64. 10.1075/idj.23.1.07thu
    https://doi.org/10.1075/idj.23.1.07thu [Google Scholar]
  44. Tufte, E. R.
    (2001) The visual display of quantitative information (2nd ed.). Cheshire, Conn: Graphics Press.
    [Google Scholar]
  45. Viègas, F., & Wattenberg, M.
    (2011) Interview for infosthetics.com, In Manovich, L. What is visualization?Visual Studies, 26(1), 36.
    [Google Scholar]
  46. Ware, C.
    (2009) Information visualization: Perception for design (2nd ed.). Amsterdam: Elsevier.
    [Google Scholar]
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  • Article Type: Research Article
Keyword(s): data visualization , historical context , objectivity and representation
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