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<!doctype html>
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<title>5a: Marks, Channels: Applied Perception</title>
<meta name="description" content="02.526 Marks, Channels and Color">
<meta name="author" content="Chan Chi-Loong, V/R">
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<h1 class="shadowfont white">5a: Marks, Channels</h1>
<h3>Applied Perception</h3>
</section>
<section>
<h3>Marks</h3>
<p><b>Marks</b> are your basic graphical geometric elements.</p>
<p>E.g. points, bars, circles, lines, etc.</p>
<img src="img/marks_primitives.jpg"/>
</section>
<section>
<h3>Channels</h3>
<h5> (aka <a href="https://en.wikipedia.org/wiki/Visual_variable" target="_blank">Visual variables</a>)</h5>
<p><b>Channels</b> encode information as attributes / properties of a mark.</p>
<p>E.g. Position, shape, size, color, etc.</p>
<img src="img/channels.jpg"/>
</section>
<section>
<h3>Visualization:</h3>
<h5>Collection of marks (expressed via channels)</h5>
<p>A visualization represents data using a collection of graphical <b>marks</b> such as bars, lines, and point symbols.</p>
<p>The attributes of a mark — such as its position, shape, size, or color — serve as <b>channels</b> in which we can encode underlying data values.</p>
</section>
<section>
<h3>Channel: Identity vs Magnitude</h3>
<img src="img/channels-identity-magnitude.jpg"/>
</section>
<section>
<img src="img/fruit-chart.jpg"/>
<p>This fruit chart encodes two data attributes as bar marks.</p>
<ul>
<li>It encodes number of people - a quantitative data set - as a vertical spatial magnitude channel.</li>
<li>It encodes type of fruit - a nominal data set - as a horizontal spatial identity channel.</li>
<li>Type of fruit is also encoded as a redundant color identity channel.</li>
</ul>
</section>
<section>
<p>How many channels are there in this chart? What data scales are encoded?</p>
<img src="img/scatterplot-channels.png"/>
</section>
<section>
<p>How many channels are there in this chart? What data scales are encoded?</p>
<a href="https://viz.vslashr.com/attilatech/attila-force-6.html" target="_blank"><img src="img/network_diagram.jpg"/></a>
</section>
<section>
<h3>Revisiting old examples</h3>
<p><a href="week1a.html#/21" target="_blank">Historical Viz 1</a></p>
<p><a href="week1a.html#/22" target="_blank">Historical Viz 2</a></p>
</section>
<section>
<h3>Channel: Expressiveness</h3>
<p>The <b>expressiveness</b> principle dictates that the visual encoding should express all of, and only, the information in the dataset attributes.</p>
<p>Color channel violation of expressiveness principle:</p>
<a href="https://www.eld.gov.sg/elections_map_electoral.html" target="_blank"><img src="img/eld_bad.jpg"></a>
</section>
<section>
<h3>Channel: Effectiveness</h3>
<p>The <b>effectiveness</b> principle dictates that the importance of the attribute should match the salience of the channel; that is, its noticeability.</p>
<p>The most important attributes should be encoded with the most effective channels to be noticeable, and so on and so forth.</p>
<p>Channel effectiveness can be described by these principles:</p>
<ul>
<li>Accuracy</li>
<li>Discriminability</li>
<li>Separability</li>
<li>Salience (pop-out)</li>
<li>Grouping</li>
</ul>
</section>
<section>
<h3>Channel ranking (based on effectiveness)</h3>
<img src="img/channels-identity-magnitude.jpg"/>
<p class="smallfont"><i>Visualization Analysis and Design, chapter 5, Marks and Channels, Munzner</i></p>
</section>
<section>
<h3>Human perception</h3>
<h5><a href="https://en.wikipedia.org/wiki/Weber%E2%80%93Fechner_law" target="_blank">Weber-Fechner Law</a></h5>
<p>Weber: The minimum increase of stimulus which will produce a perceptible increase of sensation is proportional to the pre-existent stimulus.</p>
<p>Fechner: The intensity of our sensation increases as the logarithm of an increase in energy.</p>
</section>
<section>
<h3>Weber-Fechner Law</h3>
<img src="img/weber-law-formula.jpg"/>
</section>
<section>
<h3>Weber-Fechner Law 2</h3>
<img src="img/weber-law-example.jpg"/>
<p class="smallfont">Example of Weber's law using area</p>
<p><a href="https://www.youtube.com/watch?v=hHG8io5qIU8" target="_blank">Numberphile's video on Weber's Law</a></p>
</section>
<section>
<h3><a href="https://en.wikipedia.org/wiki/Stevens%27s_power_law" target="_blank">Steven's Power Law</a></h3>
<p class="smallfont"><b>Psychophysics</b>: the subfield of psychology devoted to the systematic measurement of general human perception.</p>
<img src="img/steven-psychophysical-power-law.jpg"/>
</section>
<section>
<h4>Graphical Perception: Cleveland and McGill</h4>
<img src="img/cleveland_and_mcgill.jpg"/>
<p class="smallfont">Reading: <a href="https://priceonomics.com/how-william-cleveland-turned-data-visualization/" target="_blank">How William Cleveland Turned Data Visualization Into a Science</a></p>
<p class="smallfont">Reading: <a href="http://euclid.psych.yorku.ca/www/psy6135/papers/ClevelandMcGill1984.pdf" target="_blank">Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods</a></p>
<p class="smallfont">Reading: <a href="https://visualthinking.psych.northwestern.edu/publications/Harrison-weberlaw-infovis2014.pdf" target="_blank">Ranking Visualizations of Correlation Using Weber's Law</a></p>
</section>
<section>
<h3>Channel effectiveness: Accuracy</h3>
<p>Pick higher ranking channels (with reason).</p>
<p>E.g. Position > length > angles > area</p>
</section>
<section>
<section>
<h3>Channel effectiveness: Accuracy</h3>
<p>Example: Analyze profit over time for 3 categories</p>
<img src="img/accuracy_implications1.jpg"/>
</section>
<section>
<img src="img/accuracy_implications2.jpg"/>
</section>
</section>
<section>
<h3>Channel effectiveness: Discriminability</h3>
<p>Must be able to discriminate between a reasonable number of bins (typically max 5 to 7)</p>
<img src="img/flawed-piechart.jpg"/>
<p class="smallfont">So how to deal with lots of data? You use techniques like grouping, filtering or faceting (we'll cover in later lectures)</p>
</section>
<section>
<h3>Channel effectiveness: Separability</h3>
<p>Channels range from the <b>seperable</b> (e.g. position and hue) to the <b>integral</b> (e.g. multiple color scales)</p>
<img src="img/trivariate_map.jpg"/>
</section>
<section>
<section>
<h3>Channel effectiveness: Salience (popout)</h3>
<p>Many visual channels provide visual <b>popout</b>, where a distinct item stands out from many others immediately.</p>
<img src="img/threes1.jpg"/>
<p class="smallfont">Count the number of threes</p>
</section>
<section>
<img src="img/threes2.jpg"/>
<p class="smallfont">Far easier, right? Using the color channel to popout elements.</p>
<p><a href="https://www.csc2.ncsu.edu/faculty/healey/PP/" target="_blank">Perception in visualization, Healey</a></p>
<p class="smallfont">Concepts: Pre-attention in vision (200 to 250 ms)</p>
<p class="smallfont">Many pairs of channels do not support popout, and 3 or more channels definitely do not. As a general rule, use popout for one channel only.</p>
</section>
</section>
<section>
<h3>Channel effectiveness: Salience (popout)</h3>
<img src="img/color_preattention.jpg"/>
<p class="smallfont"><i>Storytelling with Data: A Data Visualization Guide for Business Professionals, chapter 4, Nussbaumer</i></p>
</section>
<section>
<h3>Concept: Change blindness</h3>
<p>A phenomenon of visual perception that occurs when a stimulus undergoes a change without this being noticed by its observer.</p>
<p class="smallfont"><a href="https://www.youtube.com/watch?v=bh_9XFzbWV8" target="_blank">Change blindness demo</a></p>
<p class="smallfont"><a href="https://www.youtube.com/watch?v=5O71iuTPTTI" target="_blank">Change blindness in User Interfaces</a></p>
<p class="smallfont">So how to deal with this? Minimize changes, keep changes in the focal view, or use animations / transitions (we'll talk more about animations in later lectures)</p>
<p class="smallfont">Example: <a href="https://elections.viz.sg/" target="_blank">Elections SG</a></p>
</section>
<section>
<h3>Channel effectiveness: Grouping (Gestalt laws)</h3>
<p><a href="https://en.wikipedia.org/wiki/Principles_of_grouping" target="_blank">Gestalt principles of grouping</a></p>
<p>A classification on how we group similar elements and recognize patterns.</p>
<p class="smallfont"><i>"The whole is more than the sum of its parts"</i></p>
<img src="img/gestalt_principles.jpg"/>
<p class="smallfont">Reading: <a href="https://www.topcoder.com/blog/gestalt-principles-for-data-visualization/" target="_blank">Gestalt principles for data visualization, Topcoder</a></p>
</section>
<section>
<h3>Gestalt principles examples</h3>
<p class="smallfont">Reading: <a href="https://stattlc.com/2021/07/08/making-awesome-tables-and-figures-using-gestalt-principles/" target="_blank">Making Awesome Tables and Figures Using Gestalt Principles, Silas Bergen</a></p>
<p class="smallfont">Discussion: <a href="https://github.com/WSU-DataScience/USCOTS23_Bergen_Gestalt/blob/main/Gestalt_activity.pdf" target="_blank">Gestalt Activity, Silas Bergen</a></p>
</section>
<section>
<h3>Recap</h3>
<p>Color channel violation of gestalt similarity principle:</p>
<a href="https://www.eld.gov.sg/elections_map_electoral.html" target="_blank"><img src="img/eld_bad.jpg"></a>
</section>
<section>
<h2>Questions?</h2>
<div class="plain">
<div class="rotate-icon"><a href="http://www.vslashr.com"><img width="100" src="img/VslashR_logo_white.svg"></a></div>
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<p><small>Chi-Loong | V/R</small></p>
</section>
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