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Graphical perception

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Graphical perception is the human capacity for <a href="Visualization_(graphics)" title="Visualization (graphics)">visually</a> interpreting information on <a href="Chart" title="Chart">graphs and charts</a>. 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.<a href="#cite_note-Cleveland_1984-1">[1]</a> The importance of human graphical perception, what we discern easily versus what our brains have more difficulty decoding, is fundamental to good <a href="Statistical_graphics" title="Statistical graphics">statistical graphics</a> 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.<a href="#cite_note-Cleveland_1993-2">[2]</a><a href="#cite_note-Cleveland_1994-3">[3]</a><a href="#cite_note-Tufte_2001-4">[4]</a><a href="#cite_note-Harrell,_Jr_2018-5">[5]</a><a href="#cite_note-Duke_2015-6">[6]</a><a href="#cite_note-7">[7]</a>

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

<ul><li>detection : recognition of geometry which encodes physical values</li>

<li>assembly : grouping of detected symbol elements; discerning overall patterns in data</li>

<li>estimation : assessment of relative magnitudes of two physical values.</li></ul>

Cleveland and McGill's experiments<a href="#cite_note-Cleveland_1984-1">[1]</a> to elucidate the graphical elements humans detect most accurately is a fundamental component of good <a href="Statistical_graphics" title="Statistical graphics">statistical graphics</a> design principles.<a href="#cite_note-Cleveland_1993-2">[2]</a><a href="#cite_note-Cleveland_1994-3">[3]</a><a href="#cite_note-Harrell,_Jr_2018-5">[5]</a><a href="#cite_note-Duke_2015-6">[6]</a><a href="#cite_note-8">[8]</a><a href="#cite_note-9">[9]</a><a href="#cite_note-10">[10]</a><a href="#cite_note-11">[11]</a><a href="#cite_note-12">[12]</a> 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 <a href="Dot_plot_(statistics)" title="Dot plot (statistics)">dot plot</a>. Conversely, angles are perceived with less accuracy; an example is the <a href="Pie_chart" title="Pie chart">pie chart</a>. Humans do not naturally order color hues. Only a limited number of hues can be discriminated in one graphic.

Graphic designs that utilize visual <a href="Pre-attentive_processing" title="Pre-attentive processing">pre-attentive processing</a> 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. For example in the attached figure, a graphic design feature, table look-up, requires the brain to work harder and take longer to decode than if the graph utilizes our ability to discern patterns.<a href="#cite_note-Cleveland_1994-3">[3]</a>

Graphic design that readily answers the scientific questions of interest will include appropriate estimation. Details for choosing the appropriate graph type for continuous and <a href="Categorical_variable" title="Categorical variable">categorical data</a> and for grouping have been described.<a href="#cite_note-Duke_2015-6">[6]</a><a href="#cite_note-13">[13]</a> Graphics principles for accuracy, clarity and transparency have been detailed<a href="#cite_note-Cleveland_1993-2">[2]</a><a href="#cite_note-Cleveland_1994-3">[3]</a><a href="#cite_note-Tufte_2001-4">[4]</a><a href="#cite_note-14">[14]</a> and key elements summarized.<a href="#cite_note-15">[15]</a>

<ul><li><a href="Spatial_visualization_ability" title="Spatial visualization ability">Spatial visualization ability</a></li></ul>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

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</style><cite id="CITEREFClevelandMcGill1984" class="citation journal cs1">Cleveland, William; McGill, Robert (1984). "Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods". Journal of the American Statistical Association. 79 (387): 531–544. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1080%2F01621459.1984.10478080">10.1080/01621459.1984.10478080</a>. <a href="JSTOR_(identifier)" class="mw-redirect" title="JSTOR (identifier)">JSTOR</a> <a rel="nofollow" class="external text" href="https://www.jstor.org/stable/2288400">2288400</a>.</cite>

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<li id="cite_note-Cleveland_1993-2">^ <a href="#cite_ref-Cleveland_1993_2-0">a</a> <a href="#cite_ref-Cleveland_1993_2-1">b</a> <a href="#cite_ref-Cleveland_1993_2-2">c</a> <cite id="CITEREFCleveland1993" class="citation book cs1">Cleveland, William (1993). <a rel="nofollow" class="external text" href="https://archive.org/details/visualizingdata00will">Visualizing Data</a>. Summit, New Jersey: Hobart Press. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>0-9634884-0-6</bdi>.</cite>

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<li id="cite_note-Cleveland_1994-3">^ <a href="#cite_ref-Cleveland_1994_3-0">a</a> <a href="#cite_ref-Cleveland_1994_3-1">b</a> <a href="#cite_ref-Cleveland_1994_3-2">c</a> <a href="#cite_ref-Cleveland_1994_3-3">d</a> <cite id="CITEREFCleveland1994" class="citation book cs1">Cleveland, William (1994). The elements of graphing data. Summit, New Jersey: Hobart Press. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>0-9634884-1-4</bdi>.</cite>

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<li id="cite_note-Tufte_2001-4">^ <a href="#cite_ref-Tufte_2001_4-0">a</a> <a href="#cite_ref-Tufte_2001_4-1">b</a> <cite id="CITEREFTufte2001" class="citation book cs1">Tufte, Edward (2001). <a rel="nofollow" class="external text" href="https://archive.org/details/visualdisplayofq00tuft">The Visual Display of Quantitative Information</a>. Cheshire, Connecticut: Graphics Press. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>1930824130</bdi>.</cite>

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<li id="cite_note-Harrell,_Jr_2018-5">^ <a href="#cite_ref-Harrell,_Jr_2018_5-0">a</a> <a href="#cite_ref-Harrell,_Jr_2018_5-1">b</a> <cite id="CITEREFHarrell,_Jr2017" class="citation web cs1">Harrell, Jr, Frank (April 24, 2017). <a rel="nofollow" class="external text" href="http://data.vanderbilt.edu/fh/talks/RCTGraphics/graphscourse.pdf">"PRINCIPLES OF GRAPH CONSTRUCTION"</a> (PDF). Vanderbilt. Retrieved June 9, 2018.</cite>

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<li id="cite_note-Duke_2015-6">^ <a href="#cite_ref-Duke_2015_6-0">a</a> <a href="#cite_ref-Duke_2015_6-1">b</a> <a href="#cite_ref-Duke_2015_6-2">c</a> <cite id="CITEREFDukeBanckenCroweSoup2015" class="citation journal cs1">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 (22): 3040–3059. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1002%2Fsim.6549">10.1002/sim.6549</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/26112209">26112209</a>. <a href="S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:36793148">36793148</a>.</cite>

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<li id="cite_note-7"><a href="#cite_ref-7">^</a> <cite id="CITEREFAngraGardner2017" class="citation journal cs1">Angra, Aakanksha; Gardner, Stephanie (2017). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5589433">"Reflecting on Graphs: Attributes of Graph Choice and Construction Practices in Biology"</a>. CBE: Life Sciences Education. 16 (3): ar53. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1187%2Fcbe.16-08-0245">10.1187/cbe.16-08-0245</a>. <a href="PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5589433">5589433</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/28821538">28821538</a>.</cite>

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<li id="cite_note-8"><a href="#cite_ref-8">^</a> <cite id="CITEREFClevelandMcGill1985" class="citation journal cs1">Cleveland, William; McGill, Robert (1985). "Graphical Perception and Graphical Methods for Analyzing Scientific Data". Science. 229 (4716): 828–833. <a href="Bibcode_(identifier)" class="mw-redirect" title="Bibcode (identifier)">Bibcode</a>:<a rel="nofollow" class="external text" href="https://ui.adsabs.harvard.edu/abs/1985Sci...229..828C">1985Sci...229..828C</a>. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1126%2Fscience.229.4716.828">10.1126/science.229.4716.828</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/17777913">17777913</a>. <a href="S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:16342041">16342041</a>.</cite>

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<li id="cite_note-9"><a href="#cite_ref-9">^</a> <cite id="CITEREFRobbins2005" class="citation book cs1">Robbins, Naomi (2005). Creating More Effective Graphs. Hoboken, NJ: John Wiley & Sons. pp. 47–62. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>0985911123</bdi>.</cite>

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<li id="cite_note-10"><a href="#cite_ref-10">^</a> <cite id="CITEREFCarswell1992" class="citation journal cs1">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 (5): 535–554. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1177%2F001872089203400503">10.1177/001872089203400503</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/1459565">1459565</a>. <a href="S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:19818462">19818462</a>.</cite>

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<li id="cite_note-11"><a href="#cite_ref-11">^</a> <cite id="CITEREFHollandsSpence1992" class="citation journal cs1">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 (3): 313–334. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1177%2F001872089203400306">10.1177/001872089203400306</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/1634243">1634243</a>. <a href="S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:28097022">28097022</a>.</cite>

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<li id="cite_note-12"><a href="#cite_ref-12">^</a> <cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://flowingdata.com/2010/08/26/rule-2-explain-your-encodings/">"Graph Design Rule #2: Explain your encodings"</a>. Flowing Data. 2010-08-26. Retrieved June 9, 2018.</cite>

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<li id="cite_note-13"><a href="#cite_ref-13">^</a> <cite id="CITEREFBancken2012" class="citation web cs1">Bancken, Fabrice (September 6, 2012). <a rel="nofollow" class="external text" href="https://www.ctspedia.org/do/view/CTSpedia/SelectRightGraph">"Select the Right Graph"</a>. CTSpedia Safety Graphics Home. Retrieved June 10, 2018.</cite>

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<li id="cite_note-14"><a href="#cite_ref-14">^</a> <cite id="CITEREFHarrell,_Jr2017" class="citation web cs1">Harrell, Jr, Frank (April 24, 2017). <a rel="nofollow" class="external text" href="http://biostat.mc.vanderbilt.edu/wiki/Main/RCTGraphics">"Graphics for Clinical Trials"</a>. Vanderbilt Dept of Biostatistics. Retrieved June 10, 2018.</cite>

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<li id="cite_note-15"><a href="#cite_ref-15">^</a> <cite id="CITEREFLaneDuke2012" class="citation web cs1">Lane, Peter; Duke, Susan (Aug 12, 2012). <a rel="nofollow" class="external text" href="https://www.ctspedia.org/do/view/CTSpedia/BestPractices">"Best Practices Recommendations"</a>. CTSpedia Safety Graphics Home. Retrieved June 10, 2018.</cite>

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<div class="mw-heading mw-heading2"><h2 id="External_links">External links</h2></div>

<ul><li>A <a rel="nofollow" class="external text" href="https://rpubs.com/greddi/GR-DV-Perception-essay">brief description and picture of Cleveland and McGill's nine graphical elements</a></li>

<li><a rel="nofollow" class="external text" href="https://priceonomics.com/how-william-cleveland-turned-data-visualization/">"How William Cleveland Turned Data Visualization Into a Science"</a> (2016) from Priceonomics.com</li>

<li>John Rauser's 2016 presentation, "<a rel="nofollow" class="external text" href="https://www.youtube.com/watch?v=fSgEeI2Xpdc">How Humans See Data</a>" at Velocity Amsterdam. Describes how good visualizations optimize for the human visual system</li>

<li><a href="Michael_Friendly" title="Michael Friendly">Michael Friendly</a>'s <a rel="nofollow" class="external text" href="http://www.datavis.ca/gallery/">Gallery of Data Visualization: The Best and Worst of Statistical Graphics</a></li></ul></div><div><div class="zim-footer">

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