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<h1 id="30239-data-visualization-for-policy-analysis">30239: Data Visualization for Policy Analysis</h1>
<h2 id="james-turk">James Turk</h2>
</section>
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<h2 id="today">Today</h2>
<ul>
<li>What is the value of data visualization?</li>
<li>Focus of this course</li>
<li>Course Logistics</li>
</ul>
</section>
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<p><img src="a-day-in-data.jpg" alt="infographic: a day in data" /></p>
</section>
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<p><img src="datagov.png" alt="data.gov" /></p>
</section>
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<p><img src="taxis.png" alt="taxis" /></p>
</section>
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<p>OK, there is <strong>a lot</strong> of data, but isn't that a <strong>good thing</strong>?</p>
</section>
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<p>&quot;What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.&quot;</p>
<p>~Herb Simon<br />
<em>as quoted by Hal Varian. Scientific American, September 1995</em></p>
</section>
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<h2 id="what-is-visualization">What is Visualization?</h2>
<p>“... finding the <strong>artificial memory</strong> that best supports our natural means of perception.”<br />
<em>[Bertin 1967]</em></p>
<p>“Transformation of the symbolic into the geometric”<br />
<em>[McCormick et al. 1987]</em></p>
<p>“The use of computer-generated, interactive, visual representations of data to <strong>amplify cognition</strong>.”<br />
<em>[Card, Mackinlay, &amp; Shneiderman 1999]</em></p>
</section>
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<h2 id="information-visualization">Information Visualization</h2>
<p>&quot;The use of computer-generated, interactive, visual representations of (abstract) data to <strong>amplify cognition</strong>.&quot;</p>
<p>What does it mean to amplify cognition?</p>
<p>Why do we often seek visualizations?</p>
</section>
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<h2 id="why-do-we-create-visualizations">Why do we create visualizations?</h2>
<ul>
<li>What visualizations have you created?</li>
<li>What visualizations have you seen that you remember?</li>
</ul>
</section>
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<h2 id="reasons-for-data-visualization">Reasons for Data Visualization</h2>
<ul>
<li>Means of reasoning about large quantities without reduction/over-simplification.</li>
<li>Assist in gaining unique insights into data: clustering, correlation, trends, etc.</li>
<li>Deepen understanding, for ourselves or others.</li>
</ul>
</section>
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<h3 id="four-samples">Four Samples</h3>
<table>
<thead>
<tr>
<th>x1</th>
<th>y1</th>
<th>x2</th>
<th>y2</th>
<th>x3</th>
<th>y3</th>
<th>x4</th>
<th>y4</th>
</tr>
</thead>
<tbody>
<tr>
<td>10.0</td>
<td>8.04</td>
<td>10.0</td>
<td>9.14</td>
<td>10.0</td>
<td>7.46</td>
<td>8.0</td>
<td>6.58</td>
</tr>
<tr>
<td>8.0</td>
<td>6.95</td>
<td>8.0</td>
<td>8.14</td>
<td>8.0</td>
<td>6.77</td>
<td>8.0</td>
<td>5.76</td>
</tr>
<tr>
<td>13.0</td>
<td>7.58</td>
<td>13.0</td>
<td>8.74</td>
<td>13.0</td>
<td>12.74</td>
<td>8.0</td>
<td>7.71</td>
</tr>
<tr>
<td>9.0</td>
<td>8.81</td>
<td>9.0</td>
<td>8.77</td>
<td>9.0</td>
<td>7.11</td>
<td>8.0</td>
<td>8.84</td>
</tr>
<tr>
<td>11.0</td>
<td>8.33</td>
<td>11.0</td>
<td>9.26</td>
<td>11.0</td>
<td>7.81</td>
<td>8.0</td>
<td>8.47</td>
</tr>
<tr>
<td>14.0</td>
<td>9.96</td>
<td>14.0</td>
<td>8.10</td>
<td>14.0</td>
<td>8.84</td>
<td>8.0</td>
<td>7.04</td>
</tr>
<tr>
<td>6.0</td>
<td>7.24</td>
<td>6.0</td>
<td>6.13</td>
<td>6.0</td>
<td>6.08</td>
<td>8.0</td>
<td>5.25</td>
</tr>
<tr>
<td>4.0</td>
<td>4.26</td>
<td>4.0</td>
<td>3.10</td>
<td>4.0</td>
<td>5.39</td>
<td>19.0</td>
<td>12.50</td>
</tr>
<tr>
<td>12.0</td>
<td>10.84</td>
<td>12.0</td>
<td>9.13</td>
<td>12.0</td>
<td>8.15</td>
<td>8.0</td>
<td>5.56</td>
</tr>
<tr>
<td>7.0</td>
<td>4.82</td>
<td>7.0</td>
<td>7.26</td>
<td>7.0</td>
<td>6.42</td>
<td>8.0</td>
<td>7.91</td>
</tr>
<tr>
<td>5.0</td>
<td>5.68</td>
<td>5.0</td>
<td>4.74</td>
<td>5.0</td>
<td>5.73</td>
<td>8.0</td>
<td>6.89</td>
</tr>
</tbody>
</table>
<p>What sense can we make of this?</p>
</section>
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<table>
<thead>
<tr>
<th></th>
<th>Sample 1</th>
<th>Sample 2</th>
<th>Sample 3</th>
<th>Sample 4</th>
</tr>
</thead>
<tbody>
<tr>
<td>Mean of x</td>
<td>9</td>
<td>9</td>
<td>9</td>
<td>9</td>
</tr>
<tr>
<td>Variance of x</td>
<td>11</td>
<td>11</td>
<td>11</td>
<td>11</td>
</tr>
<tr>
<td>Mean of y</td>
<td>7.50</td>
<td>7.50</td>
<td>7.50</td>
<td>7.50</td>
</tr>
<tr>
<td>Variance of y (±0.003 )</td>
<td>4.125</td>
<td>4.125</td>
<td>4.125</td>
<td>4.125</td>
</tr>
<tr>
<td>Correlation x &amp; y</td>
<td>0.816</td>
<td>0.816</td>
<td>0.816</td>
<td>0.816</td>
</tr>
<tr>
<td>Linear Regression</td>
<td>y = 3.00 + 0.500x</td>
<td>y = 3.00 + 0.500x</td>
<td>y = 3.00 + 0.500x</td>
<td>y = 3.00 + 0.500x</td>
</tr>
<tr>
<td>R² coefficient</td>
<td>0.67</td>
<td>0.67</td>
<td>0.67</td>
<td>0.67</td>
</tr>
</tbody>
</table>
</section>
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<p><img src="anscome.svg" alt="Anscombe's quartet" /></p>
</section>
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</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="15" lang="en-US">
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<p>Our understanding of the data is enhanced by these visualizations in a way summary statistics won't capture.</p>
<p>What things are easier to see?</p>
</section>
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<h2 id="benefits-of-visualizing-data">Benefits of Visualizing Data</h2>
<ul>
<li>outliers</li>
<li>&quot;shape of data&quot;</li>
<li>clusters</li>
<li><em>intuition</em> and <em>questions</em> - &quot;why is it like that?&quot;</li>
<li>tap into human capacity for pattern recognition</li>
<li>often easier to test theories or models</li>
</ul>
<p><em>exploratory visualization</em></p>
</section>
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<h2 id="explanatory-visualization">Explanatory Visualization</h2>
<ul>
<li>Highlight interesting findings</li>
<li>Tell a story</li>
<li>Present a thesis</li>
<li>Persuade</li>
<li>Support larger story/argument</li>
<li>Inspire</li>
</ul>
</section>
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<h2 id="visualizations-and-policy">Visualizations and Policy</h2>
<p>Visualizations have an outsized influence on human cognition, we seem to trust images more than words.</p>
<p>This means that visualizations can be used to <strong>persuade</strong>, but also <strong>mislead</strong>.</p>
<p>There does not need to be intention, we can easily deceive ourselves without realizing it.</p>
</section>
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<p><img src="rockets_chart.png" alt="" /></p>
</section>
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</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section lang="en-US" data-marpit-advanced-background="background"><div data-marpit-advanced-background-container="true" data-marpit-advanced-background-direction="horizontal"><figure style="background-image:url(&quot;challenger.jpg&quot;);"></figure><figure style="background-image:url(&quot;feynman.jpg&quot;);"></figure></div></section></foreignObject><foreignObject width="1280" height="720"><section id="20" lang="en-US" data-marpit-advanced-background="content"></section>
</foreignObject><foreignObject width="1280" height="720" data-marpit-advanced-background="pseudo"><section lang="en-US" data-marpit-advanced-background="pseudo" style=""></section></foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="21" lang="en-US">
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<p><img src="cholera.jpg" alt="" /></p>
</section>
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<h2 id="data-visualization-for-policy-analysis">Data Visualization for Policy Analysis</h2>
</section>
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<h2 id="course-goals">Course Goals</h2>
<ul>
<li>Understand &amp; appreciate what makes a good data visualization.</li>
<li>Learn practical visualization techniques that will apply in any language &amp; library.</li>
<li>Build a portfolio of static &amp; interactive visualizations using real-world policy data.</li>
<li>Gain exposure to useful libraries in Python and JavaScript.</li>
</ul>
</section>
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<h2 id="topics">Topics</h2>
<ul>
<li>Fundamentals, Grammar of Graphics</li>
<li>Design principles. How to use color, human perception, chart design.</li>
<li>How to evaluate and critique visualizations.</li>
<li>Uncertainty &amp; Narrative</li>
<li>HTML/CSS/JS overview</li>
<li>D3.js</li>
<li>Interactive &amp; non-chart data visualization.</li>
<li>Geospatial visualization</li>
<li>Special Topics: to be discussed</li>
</ul>
</section>
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<h2 id="programming-workload">Programming Workload</h2>
<p>Key Idea: You get better at visualizations by making <em>a lot</em> of them. (20-30 this quarter)</p>
<ul>
<li>Expect to write code every week, mostly fairly short Python functions.</li>
<li>You will be learning at least one library (Altair) mostly independently.</li>
<li>You'll also need to be comfortable with <code>pandas</code> or <code>polars</code>.</li>
</ul>
<h3 id="javascript-and-d3">JavaScript and D3</h3>
<p><em>&quot;You aren't going to make them learn D3 are you?&quot;</em></p>
<ul>
<li>1 Assignment</li>
<li>1-2 Lectures</li>
</ul>
<p>Final project will have a place where D3 will be helpful, but other options will be presented.</p>
<p>After introductory lecture, some examples will continue to be in D3, but you will not need to understand their inner workings.</p>
</section>
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<h2 id="course-structure">Course Structure</h2>
<ul>
<li><strong>Lecture and Discussion</strong>: Introduce and explore key concepts, mostly focused on theory.</li>
<li><strong>Readings</strong>: Supplement course materials with more examples, technical tutorials.</li>
<li><strong>2 &quot;minor&quot; assignments: Altair &amp; D3</strong> - Gain practice with commonly used tools in a structured setting.</li>
<li><strong>Projects</strong>: Learn to explore a topic on your own from conception to practice. Leave here with a portfolio you can show off.</li>
</ul>
</section>
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<h2 id="course-staff">Course Staff</h2>
<ul>
<li>James Turk</li>
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<li>Krisha Mehta</li>
<li>Sam Huang</li>
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</ul>
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<p><strong>All official information will be on the course site and/or Ed.</strong></p>
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</section>
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<h2 id="projects">Projects</h2>
<table>
<thead>
<tr>
<th></th>
<th>Static</th>
<th>Interactive</th>
</tr>
</thead>
<tbody>
<tr>
<td>Proposal</td>
<td>Week 1</td>
<td>Week 6</td>
</tr>
<tr>
<td>Feedback Draft</td>
<td>Week 3</td>
<td>Week 8</td>
</tr>
<tr>
<td>Peer Critique</td>
<td>Week 4</td>
<td>Week 9</td>
</tr>
<tr>
<td>Final</td>
<td>Week 5</td>
<td>Week 10</td>
</tr>
</tbody>
</table>
<h2 id="practice-assignments">Practice Assignments</h2>
<ul>
<li>Altair (Week 2)</li>
<li>D3 (Week 6)</li>
</ul>
</section>
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<h2 id="grading">Grading</h2>
<p>15 SNU grades</p>
<p><strong>Completion:</strong> 9 (2x Proposals, Drafts &amp; Critiques + 2 Labs + 1 Participation)</p>
<p><strong>Quality Grades:</strong> 6 (2x Design, Narrative, Code Quality)</p>
</section>
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<h2 id="ai-policy">AI Policy</h2>
<p>In this course, all usage of generative AI must be <em>fully cited</em>.</p>
<p>Details on specific rules: <a href="https://capp30239.netlify.app/policies/ai/">https://capp30239.netlify.app/policies/ai/</a></p>
<p>You are, as always, expected to turn in <strong>your own work</strong>, not the work of an LLM.</p>
</section>
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<h2 id="static-visualization-project">Static Visualization Project</h2>
<p><a href="https://capp30239.netlify.app/coursework/static/">https://capp30239.netlify.app/coursework/static/</a></p>
<p><strong>End Product</strong></p>
<p>Using real data of your choosing:</p>
<ul>
<li>8-12 distinct images, of at least 5 different types.</li>
<li>Presented as part of a <strong>narrative</strong>: an article, infographic, poster.</li>
<li>Cohesive visual design: custom theme for graphs, matching colors and fonts with supplementary material.</li>
</ul>
<p>Use of Altair is <strong>strongly</strong> recommended, but other libraries allowed.</p>
</section>
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<h2 id="examples">Examples</h2>
<p><a href="https://capp-30239-winter-2021.netlify.app/#staticShowcase">https://capp-30239-winter-2021.netlify.app/#staticShowcase</a></p>
</section>
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<h2 id="milestones">Milestones</h2>
<ul>
<li>Milestone 1 (Week 1): Draft proposal.</li>
<li>Milestone 2 (Week 3): Draft of 8 visualizations for review &amp; critique.</li>
<li>Milestone 3 (Week 4): Peer Critique</li>
<li>Milestone 4 (Week 5): Final Deliverable</li>
</ul>
</section>
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<h2 id="acknowledgements--references">Acknowledgements &amp; References</h2>
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<p>Thanks to Alex Hale, Andrew McNutt, and Jessica Hullman for sharing their materials.</p>
<p><a href="https://hdsr.mitpress.mit.edu/pub/zok97i7p/release/4">Why Is Data Visualization Important? What Is Important in Data Visualization?</a> - An tony Unwin</p>
</section>
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</script></foreignObject></svg></div><div class="bespoke-marp-note" data-index="2" tabindex="0"><p>Modern data visualization is largely a product of the sheer amount of data we produce.</p></div><div class="bespoke-marp-note" data-index="3" tabindex="0"><p>In part this is a function of the digitization of most of our lives.
15 years ago there was no data.gov, it has added 300k data sets since then.</p></div><div class="bespoke-marp-note" data-index="4" tabindex="0"><p>As technology advances, our data gets to be incredibly fine grained.
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This is a sample of ride share rides over a six year period. Each pixel is a pick up or drop off.</p></div><div class="bespoke-marp-note" data-index="18" tabindex="0"><p>images from presentation on O-ring temperature anomaly</p></div><div class="bespoke-marp-note" data-index="19" tabindex="0"><p>the result</p></div><div class="bespoke-marp-note" data-index="20" tabindex="0"><p>Cholera outbreak. London, 1854. John Snow.</p></div><div class="bespoke-marp-note" data-index="24" tabindex="0"><p>It has however, become a &quot;library's library&quot; in some ways. Most developers interact with D3 through a higher-level interface.
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We will be learning Altair, which generates Vega JSON, which in turn is drawn using D3.
D3 underpins dozens of other charting libraries as well, and D3's own documentation says:
D3 makes things possible, not necessarily easy; even simple things that should be easy are often not. To paraphrase Amanda Cox: “Use D3 if you think its perfectly normal to write a hundred lines of code for a bar chart.”
Learning D3 both requires working in an unfamiliar environment (JavaScript) and with a very unique style of programming based around their concept of the [&quot;data join&quot;](https://d3js.org/d3-selection/joining), which requires a decent understanding of the HTML Document Object Model.
So, if you are here to learn visualization, I think that it is fair that you can succeed in this class without putting yourself through that.</p></div><script>/*!! License: https://unpkg.com/@marp-team/marp-cli@3.4.0/lib/bespoke.js.LICENSE.txt */
2024-09-26 04:57:11 +00:00
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