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Graphical perception

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This is an old revision of this page, as edited by Econterms (talk | contribs) at 18:25, 27 July 2018 (wikify; clarify encode/decode in context). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.
  • Comment: This draft appears to be about work by William Cleveland and Robert McGill. It does not establish the notability of their work, because that should involve describing how their work has been cited by other scholars.
    Fix reference error. Robert McClenon (talk) 17:28, 9 June 2018 (UTC)

Graphical perception is the human capacity for visually interpreting information on graphs and charts. Both quantitative and qualitative information can be said to be encoded into the image, and the human capacity to interpret it is sometimes called decoding.[1] The importance of human graphical perception, what we discern easily versus what our brains have more difficulty decoding, is fundamental to good statistical graphics design, where clarity, transparency, accuracy and precision in data display and interpretation are essential for understanding the translation of data in a graph to clarify and interpret the science.[2][3][4][5][6][7]

Graphical perception is achieved in dimensions or steps of discernment by:

  • detection : recognition of geometry which encodes physical values
  • assembly : grouping of detected symbol elements; discerning overall patterns in data
  • estimation : assessment of relative magnitudes of two physical values.

Cleveland and McGill's experiments[1] to elucidate the graphical elements humans detect most accurately is a fundamental component of good statistical graphics design principles.[2][3][5][6][8][9][10][11][12] See external link [1] for further description and picture of these elements ordered from human perception's best to worst. In practical terms, graphs displaying relative position on a common scale most accurately are most effective. A graph type that utilizes this element is the dot plot. Conversely, angles are perceived with less accuracy; an example is the pie chart. Humans do not naturally order color hues. Only a limited number of hues can be discriminated in one graphic.

Graphic designs that utilize visual pre-attentive processing in the graph design's assembly is why a picture can be worth a thousand words by using the brain's ability to perceive patterns. Not all graphs are designed to consider pre-attentive processing. A commonly used graphic design feature, similar to a lookup table, requires the brain to work harder and take longer to see the data comparison.[3]

Graphic design that readily answers the scientific questions of interest will include appropriate estimation. Details for choosing the appropriate graph type for continuous and categorical data and for grouping have been described.[6][13] Graphics principles for accuracy, clarity and transparency have been detailed[2][3][4][14] and key elements summarized.[15]

References

  1. ^ a b Cleveland, William; McGill, Robert (1984). "Graphical Perception and Graphical Methods for Analyzing Scientific Data". Journal of the American Statistical Association. 79: 531–544.
  2. ^ a b c Cleveland, William (1993). Visualizing Data. Summit, New Jersey: Hobart Press. ISBN 0-9634884-0-6.
  3. ^ a b c d Cleveland, William (1994). The elements of graphing data. Summit, New Jersey: Hobart Press. ISBN 0-9634884-1-4.
  4. ^ a b Tufte, Edward (2001). The Visual Display of Quantitative Information. Cheshire, Connecticut: Graphics Press. ISBN 1930824130.
  5. ^ a b Harrell, Jr, Frank (April 24, 2017). "PRINCIPLES OF GRAPH CONSTRUCTION" (PDF). Vanderbilt. Retrieved June 9, 2018. {{cite web}}: Cite has empty unknown parameter: |dead-url= (help)
  6. ^ a b c Duke, Susan; Bancken, Fabrice; Crowe, Brenda; Soup, Mat; Botsis, Taxiarchis; Forshee, Richard (2015). "Seeing is believing: good graphic design principles for medical research". Statistics in Medicine. 34: 3040–3059.
  7. ^ Angra, Aakanksha; Gardner, Stephanie (2017). "Reflecting on Graphs: Attributes of Graph Choice and Construction Practices in Biology". CBE—Life Sciences Education. 16: 1–15.
  8. ^ Cleveland, William; McGill, Robert (1985). "Graphical Perception and Graphical Methods for Analyzing Scientific Data". Science. 229: 828–833.
  9. ^ Robbins, Naomi (2005). Creating More Effective Graphs. Hoboken, NJ: John Wiley & Sons. pp. 47–62. ISBN 0985911123.
  10. ^ Carswell, C. Melody (1992). "Choosing Specifiers: An Evaluation of the Basic Tasks Model of Graphical Perception". Human Factors: The Journal of the Human Factors and Ergonomics Society. 34: 535–554.
  11. ^ Hollands, J. G.; Spence, Ian (1992). "Judgments of Change and Proportion in Graphical Perception". Human Factors: The Journal of the Human Factors and Ergonomics Society. 34: 313–334.
  12. ^ "Graph Design Rule #2: Explain your encodings". Flowing Data. Retrieved June 9, 2018. {{cite web}}: Cite has empty unknown parameter: |dead-url= (help)
  13. ^ Bancken, Fabrice (September 6, 2012). "Select the Right Graph". CTSpedia Safety Graphics Home. Retrieved June 10, 2018. {{cite web}}: Cite has empty unknown parameter: |dead-url= (help)
  14. ^ Harrell, Jr, Frank (April 24, 2017). "Graphics for Clinical Trials". Vanderbilt Dept of Biostatistics. Retrieved June 10, 2018. {{cite web}}: Cite has empty unknown parameter: |dead-url= (help)
  15. ^ Lane, Peter; Duke, Susan (Aug 12, 2012). "Best Practices Recommendations". CTSpedia Safety Graphics Home. Retrieved June 10, 2018. {{cite web}}: Cite has empty unknown parameter: |dead-url= (help)

Graphical Perception

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