2024-09-28 21:18:20 +00:00
<!DOCTYPE html> < html lang = "en-US" > < head > < meta charset = "UTF-8" > < meta name = "viewport" content = "width=device-width,height=device-height,initial-scale=1.0" > < meta name = "apple-mobile-web-app-capable" content = "yes" > < meta http-equiv = "X-UA-Compatible" content = "ie=edge" > < meta property = "og:type" content = "website" > < meta name = "twitter:card" content = "summary" > < style > @ m e d i a s c r e e n { b o d y [ d a t a - b e s p o k e - v i e w = " " ] . b e s p o k e - m a r p - p a r e n t > . b e s p o k e - m a r p - o s c > b u t t o n , b o d y [ d a t a - b e s p o k e - v i e w = n e x t ] . b e s p o k e - m a r p - p a r e n t > . b e s p o k e - m a r p - o s c > b u t t o n , b o d y [ d a t a - b e s p o k e - v i e w = p r e s e n t e r ] . b e s p o k e - m a r p - p r e s e n t e r - c o n t a i n e r . b e s p o k e - m a r p - p r e s e n t e r - i n f o - c o n t a i n e r b u t t o n , b o d y [ d a t a - b e s p o k e - v i e w = p r e s e n t e r ] . b e s p o k e - m a r p - p r e s e n t e r - c o n t a i n e r . b e s p o k e - m a r p - p r e s e n t e r - n o t e - c o n t a i n e r b u t t o n { - w e b k i t - t a p - h i g h l i g h t - c o l o r : t r a n s p a r e n t ; - w e b k i t - a p p e a r a n c e : n o n e ; a p p e a r a n c e : n o n e ; b a c k g r o u n d - c o l o r : t r a n s p a r e n t ; b o r d e r : 0 ; c o l o r : i n h e r i t ; c u r s o r : p o i n t e r ; f o n t - s i z e : i n h e r i t ; o p a c i t y : . 8 ; o u t l i n e : n o n e ; p a d d i n g : 0 ; t r a n s i t i o n : o p a c i t y . 2 s l i n e a r } b o d y [ d a t a - b e s p o k e - v i e w = " " ] . b e s p o k e - m a r p - p a r e n t > . b e s p o k e - m a r p - o s c > b u t t o n : d i s a b l e d , b o d y [ d a t a - b e s p o k e - v i e w = n e x t ] . b e s p o k e - m a r p - p a r e n t > . b e s p o k e - m a r p - o s c > b u t t o n : d i s a b l e d , b o d y [ d a t a - b e s p o k e - v i e w = p r e s e n t e r ] . b e s p o k e - m a r p - p r e s e n t e r - c o n t a i n e r . b e s p o k e - m a r p - p r e s e n t e r - i n f o - c o n t a i n e r b u t t o n : d i s a b l e d , b o d y [ d a t a - b e s p o k e - v i e w = p r e s e n t e r ] . b e s p o k e - m a r p - p r e s e n t e r - c o n t a i n e r . b e s p o k e - m a r p - p r e s e n t e r - n o t e - c o n t a i n e r b u t t o n : d i s a b l e d { c u r s o r : n o t - a l l o w e d ; o p a c i t y : . 1 5 ! i m p o r t a n t } b o d y [ d a t a - b e s p o k e - v i e w = " " ] . b e s p o k e - m a r p - p a r e n t > . b e s p o k e - m a r p - o s c > b u t t o n : h o v e r , b o d y [ d a t a - b e s p o k e - v i e w = n e x t ] . b e s p o k e - m a r p - p a r e n t > . b e s p o k e - m a r p - o s c > b u t t o n : h o v e r , b o d y [ d a t a - b e s p o k e - v i e w = p r e s e n t e r ] . b e s p o k e - m a r p - p r e s e n t e r - c o n t a i n e r . b e s p o k e - m a r p - p r e s e n t e r - i n f o - c o n t a i n e r b u t t o n : h o v e r , b o d y [ d a t a - b e s p o k e - v i e w = p r e s e n t e r ] . b e s p o k e - m a r p - p r e s e n t e r - c o n t a i n e r . b e s p o k e - m a r p - p r e s e n t e r - n o t e - c o n t a i n e r b u t t o n : h o v e r { o p a c i t y : 1 } b o d y [ d a t a - b e s p o k e - v i e w = " " ] . b e s p o k e - m a r p - p a r e n t > . b e s p o k e - m a r p - o s c > b u t t o n : h o v e r : a c t i v e , b o d y [ d a t a - b e s p o k e - v i e w = n e x t ] . b e s p o k e - m a r p - p a r e n t > . b e s p o k e - m a r p - o s c > b u t t o n : h o v e r : a c t i v e , b o d y [ d a t a - b e s p o k e - v i e w = p r e s e n t e r ] . b e s p o k e - m a r p - p r e s e n t e r - c o n t a i n e r . b e s p o k e - m a r p - p r e s e n t e r - i n f o - c o n t a i n e r b u t t o n : h o v e r : a c t i v e , b o d y [ d a t a - b e s p o k e - v i e w = p r e s e n t e r ] . b e s p o k e - m a r p - p r e s e n t e r - c o n t a i n e r . b e s p o k e - m a r p - p r e s e n t e r - n o t e - c o n t a i n e r b u t t o n : h o v e r : a c t i v e { o p a c i t y : . 6 } b o d y [ d a t a - b e s p o k e - v i e w = " " ] . b e s p o k e - m a r p - p a r e n t > . b e s p o k e - m a r p - o s c > b u t t o n : h o v e r : n o t ( : d i s a b l e d ) , b o d y [ d a t a - b e s p o k e - v i e w = n e x t ] . b e s p o k e - m a r p - p a r e n t > . b e s p o k e - m a r p - o s c > b u t t o n : h o v e r : n o t ( : d i s a b l e d ) , b o d y [ d a t a - b e s p o k e - v i e w = p r e s e n t e r ] . b e s p o k e - m a r p - p r e s e n t e r - c o n t a i n e r . b e s p o k e - m a r p - p r e s e n t e r - i n f o - c o n t a i n e r b u t t o n : h o v e r : n o t ( : d i s a b l e d ) , b o d y [ d a t a - b e s p o k e - v i e w = p r e s e n t e r ] . b e s p o k e - m a r p - p r e s e n t e r - c o n t a i n e r . b e s p o k e - m a r p - p r e s e n t e r - n o t e - c o n t a i n e r b u t t o n : h o v e r : n o t ( : d i s a b l e d ) { t r a n s i t i o n : n o n e } b o d y [ d a t a - b e s p o k e - v i e w = " " ] . b e s p o k e - m a r p - p a r e n t > . b e s p o k e - m a r p - o s c > b u t t o n [ d a t a - b e s p o k e - m a r p - o s c = p r e v ] , b o d y [ d a t a - b e s p o k e - v i e w = n e x t ] . b e s p o k e - m a r p - p a r e n t > . b e s p o k e - m a r p - o s c > b u t t o n [ d a t a - b e s p o k e - m a r p - o s c = p r e v ] , b o d y [ d a t a - b e s p o k e - v i e w = p r e s e n t e r ] . b e s p o k e - m a r p - p r e s e n t e r - c o n t a i n e r . b e s p o k e - m a r p - p r e s e n t e r - i n f o - c o n t a i n e r b u t t o n . b e s p o k e - m a r p - p r e s e n t e r - i n f o - p a g e - p r e v { b a c k g r o u n d : t r a n s p a r e n t u r l ( " d a t a : i m a g e / s v g + x m l ; b a s e 6 4 , P H N 2 Z y B 4 b W x u c z 0 i a H R 0 c D o v L 3 d 3 d y 5 3 M y 5 v c m c v M j A w M C 9 z d m c i I H Z p Z X d C b 3 g 9 I j A g M C A x M D A g M T A w I j 4 8 c G F 0 a C B m a W x s P S J u b 2 5 l I i B z d H J v a 2 U 9 I i N m Z m Y i I H N 0 c m 9 r Z S 1 s a W 5 l Y 2 F w P S J y b 3 V u Z C I g c 3 R y b 2 t l L W x p b m V q b 2 l u P S J y b 3 V u Z C I g c 3 R y b 2 t l L X d p Z H R o P S I 1 I i B k P S J N N j g g O T A g M j g g N T B s N D A t N D A i L z 4 8 L 3 N 2 Z z 4 = " ) n o - r e p e a t 5 0 % ; b a c k g r o u n d - s i z e : c o n t a i n ; o v e r f l o w : h i d d e n ; t e x t - i n d e n t : 1 0 0 % ; w h i t e - s p a c e : n o w r a p } b o d y [ d a t a - b e s p o k e - v i e w = " " ] . b e s p o k e - m a r p - p a r e n t > . b e s p o k e - m a r p - o s c > b u t t o n [ d a t a - b e s p o k e - m a r p - o s c = n e x t ] , b o d y [ d a t a - b e s p o k e - v i e w = n e x t ] . b e s p o k e - m a r p - p a r e n t > . b e s p o k e - m a r p - o s c > b u t t o n [ d a t a - b e s p o k e - m a r p - o s c = n e x t ] , b o d y [ d a t a - b e s p o k e - v i e w = p r e s e n t e r ] . b e s p o k e - m a r p - p r e s e n t e r - c o n t a i n e r . b e s p o k e - m a r p - p r e s e n t e r - i n f o - c o n t a i n e r b u t t o n . b e s p o k e - m a r p - p r e s e n t e r - i n f o - p a g e - n e x t { b a c k g r o u n d : t r a n s p a r e n t u r l ( " d a t a : i m a g e / s v g + x m l ; b a s e 6 4 , P H N 2 Z y B 4 b W x u c z 0 i a H R 0 c D o v L 3 d 3 d y 5 3 M y 5 v c m c v M j A w M C 9 z d m c i I H Z p Z X d C b 3 g 9 I j A g M C A x M D A g M T A w I j 4 8 c G F 0 a C B m a W x s P S J u b 2 5 l I i B z d H J v a 2 U 9 I i N m Z m Y i I H N 0 c m 9 r Z S 1 s a W 5 l Y 2 F w P S J y b 3 V u Z C I g c 3 R y b 2 t l L W x p b m V q b 2 l u P S
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* Marp default theme.
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* @author Yuki Hattori
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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 > " 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." < / 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, & Shneiderman 1999]< / em > < / p >
< / section >
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< h2 id = "information-visualization" > Information Visualization< / h2 >
< p > " The use of computer-generated, interactive, visual representations of (abstract) data to < strong > amplify cognition< / strong > ." < / 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 & 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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< 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 > " shape of data" < / li >
< li > clusters< / li >
< li > < em > intuition< / em > and < em > questions< / em > - " why is it like that?" < / 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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< 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 & appreciate what makes a good data visualization.< / li >
< li > Learn practical visualization techniques that will apply in any language & library.< / li >
< li > Build a portfolio of static & 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 & Narrative< / li >
< li > HTML/CSS/JS overview< / li >
< li > D3.js< / li >
< li > Interactive & 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 > " You aren't going to make them learn D3 are you?" < / 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 " minor" assignments: Altair & 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 & 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 & 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 & 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 >
< script > ! f u n c t i o n ( ) { " u s e s t r i c t " ; c o n s t t = { h 1 : { p r o t o : ( ) = > H T M L H e a d i n g E l e m e n t , a t t r s : { r o l e : " h e a d i n g " , " a r i a - l e v e l " : " 1 " } , s t y l e : " d i s p l a y : b l o c k ; f o n t - s i z e : 2 e m ; m a r g i n - b l o c k - s t a r t : 0 . 6 7 e m ; m a r g i n - b l o c k - e n d : 0 . 6 7 e m ; m a r g i n - i n l i n e - s t a r t : 0 p x ; m a r g i n - i n l i n e - e n d : 0 p x ; f o n t - w e i g h t : b o l d ; " } , h 2 : { p r o t o : ( ) = > H T M L H e a d i n g E l e m e n t , a t t r s : { r o l e : " h e a d i n g " , " a r i a - l e v e l " : " 2 " } , s t y l e : " d i s p l a y : b l o c k ; f o n t - s i z e : 1 . 5 e m ; m a r g i n - b l o c k - s t a r t : 0 . 8 3 e m ; m a r g i n - b l o c k - e n d : 0 . 8 3 e m ; m a r g i n - i n l i n e - s t a r t : 0 p x ; m a r g i n - i n l i n e - e n d : 0 p x ; f o n t - w e i g h t : b o l d ; " } , h 3 : { p r o t o : ( ) = > H T M L H e a d i n g E l e m e n t , a t t r s : { r o l e : " h e a d i n g " , " a r i a - l e v e l " : " 3 " } , s t y l e : " d i s p l a y : b l o c k ; f o n t - s i z e : 1 . 1 7 e m ; m a r g i n - b l o c k - s t a r t : 1 e m ; m a r g i n - b l o c k - e n d : 1 e m ; m a r g i n - i n l i n e - s t a r t : 0 p x ; m a r g i n - i n l i n e - e n d : 0 p x ; f o n t - w e i g h t : b o l d ; " } , h 4 : { p r o t o : ( ) = > H T M L H e a d i n g E l e m e n t , a t t r s : { r o l e : " h e a d i n g " , " a r i a - l e v e l " : " 4 " } , s t y l e : " d i s p l a y : b l o c k ; m a r g i n - b l o c k - s t a r t : 1 . 3 3 e m ; m a r g i n - b l o c k - e n d : 1 . 3 3 e m ; m a r g i n - i n l i n e - s t a r t : 0 p x ; m a r g i n - i n l i n e - e n d : 0 p x ; f o n t - w e i g h t : b o l d ; " } , h 5 : { p r o t o : ( ) = > H T M L H e a d i n g E l e m e n t , a t t r s : { r o l e : " h e a d i n g " , " a r i a - l e v e l " : " 5 " } , s t y l e : " d i s p l a y : b l o c k ; f o n t - s i z e : 0 . 8 3 e m ; m a r g i n - b l o c k - s t a r t : 1 . 6 7 e m ; m a r g i n - b l o c k - e n d : 1 . 6 7 e m ; m a r g i n - i n l i n e - s t a r t : 0 p x ; m a r g i n - i n l i n e - e n d : 0 p x ; f o n t - w e i g h t : b o l d ; " } , h 6 : { p r o t o : ( ) = > H T M L H e a d i n g E l e m e n t , a t t r s : { r o l e : " h e a d i n g " , " a r i a - l e v e l " : " 6 " } , s t y l e : " d i s p l a y : b l o c k ; f o n t - s i z e : 0 . 6 7 e m ; m a r g i n - b l o c k - s t a r t : 2 . 3 3 e m ; m a r g i n - b l o c k - e n d : 2 . 3 3 e m ; m a r g i n - i n l i n e - s t a r t : 0 p x ; m a r g i n - i n l i n e - e n d : 0 p x ; f o n t - w e i g h t : b o l d ; " } , s p a n : { p r o t o : ( ) = > H T M L S p a n E l e m e n t } , p r e : { p r o t o : ( ) = > H T M L E l e m e n t , s t y l e : " d i s p l a y : b l o c k ; f o n t - f a m i l y : m o n o s p a c e ; w h i t e - s p a c e : p r e ; m a r g i n : 1 e m 0 ; - - m a r p - a u t o - s c a l i n g - w h i t e - s p a c e : p r e ; " } } , e = " d a t a - m a r p - a u t o - s c a l i n g - w r a p p e r " , i = " d a t a - m a r p - a u t o - s c a l i n g - s v g " , n = " d a t a - m a r p - a u t o - s c a l i n g - c o n t a i n e r " ; c l a s s s e x t e n d s H T M L E l e m e n t { c o n s t r u c t o r ( ) { s u p e r ( ) , t h i s . s v g P r e s e r v e A s p e c t R a t i o = " x M i n Y M i d m e e t " ; c o n s t t = t = > ( [ e ] ) = > { c o n s t { w i d t h : i , h e i g h t : n } = e . c o n t e n t R e c t ; t h i s [ t ] = { w i d t h : i , h e i g h t : n } , t h i s . u p d a t e S V G R e c t ( ) } ; t h i s . a t t a c h S h a d o w ( { m o d e : " o p e n " } ) , t h i s . c o n t a i n e r O b s e r v e r = n e w R e s i z e O b s e r v e r ( t ( " c o n t a i n e r S i z e " ) ) , t h i s . w r a p p e r O b s e r v e r = n e w R e s i z e O b s e r v e r ( ( ( . . . e ) = > { t ( " w r a p p e r S i z e " ) ( . . . e ) , t h i s . f l u s h S v g D i s p l a y ( ) } ) ) } s t a t i c g e t o b s e r v e d A t t r i b u t e s ( ) { r e t u r n [ " d a t a - d o w n s c a l e - o n l y " ] } c o n n e c t e d C a l l b a c k ( ) { v a r t , s , o , r , a ; t h i s . s h a d o w R o o t . i n n e r H T M L = ` \ n < s t y l e > \ n s v g [ $ { i } ] { d i s p l a y : b l o c k ; w i d t h : 1 0 0 % ; h e i g h t : a u t o ; v e r t i c a l - a l i g n : t o p ; } \ n s p a n [ $ { n } ] { d i s p l a y : t a b l e ; w h i t e - s p a c e : v a r ( - - m a r p - a u t o - s c a l i n g - w h i t e - s p a c e , n o w r a p ) ; w i d t h : m a x - c o n t e n t ; } \ n < / s t y l e > \ n < d i v $ { e } > \ n < s v g p a r t = " s v g " $ { i } > \ n < f o r e i g n O b j e c t > < s p a n $ { n } > < s l o t > < / s l o t > < / s p a n > < / f o r e i g n O b j e c t > \ n < / s v g > \ n < / d i v > \ n ` . s p l i t ( / \ n \ s * / ) . j o i n ( " " ) , t h i s . w r a p p e r = n u l l ! = = ( t = t h i s . s h a d o w R o o t . q u e r y S e l e c t o r ( ` d i v [ $ { e } ] ` ) ) & & v o i d 0 ! = = t ? t : v o i d 0 ; c o n s t l = t h i s . s v g ; t h i s . s v g = n u l l ! = = ( o = n u l l = = = ( s = t h i s . w r a p p e r ) | | v o i d 0 = = = s ? v o i d 0 : s . q u e r y S e l e c t o r ( ` s v g [ $ { i } ] ` ) ) & & v o i d 0 ! = = o ? o : v o i d 0 , t h i s . s v g ! = = l & & ( t h i s . s v g C o m p u t e d S t y l e = t h i s . s v g ? w i n d o w . g e t C o m p u t e d S t y l e ( t h i s . s v g ) : v o i d 0 ) , t h i s . c o n t a i n e r = n u l l ! = = ( a = n u l l = = = ( r = t h i s . s v g ) | | v o i d 0 = = = r ? v o i d 0 : r . q u e r y S e l e c t o r ( ` s p a n [ $ { n } ] ` ) ) & & v o i d 0 ! = = a ? a : v o i d 0 , t h i s . o b s e r v e ( ) } d i s c o n n e c t e d C a l l b a c k ( ) { t h i s . s v g = v o i d 0 , t h i s . s v g C o m p u t e d S t y l e = v o i d 0 , t h i s . w r a p p e r = v o i d 0 , t h i s . c o n t a i n e r = v o i d 0 , t h i s . o b s e r v e ( ) } a t t r i b u t e C h a n g e d C a l l b a c k ( ) { t h i s . o b s e r v e ( ) } f l u s h S v g D i s p l a y ( ) { c o n s t { s v g : t } = t h i s ; t & & ( t . s t y l e . d i s p l a y = " i n l i n e " , r e q u e s t A n i m a t i o n F r a m e ( ( ( ) = > { t . s t y l e . d i s p l a y = " " } ) ) ) } o b s e r v e ( ) { t h i s . c o n t a i n e r O b s e r v e r . d i s c o n n e c t ( ) , t h i s . w r a p p e r O b s e r v e r . d i s c o n n e c t ( ) , t h i s . w r a p p e r & & t h i s . w r a p p e r O b s e r v e r . o b s e r v e ( t h i s . w r a p p e r ) , t h i s . c o n t a i n e r & & t h i s . c o n t a i n e r O b s e r v e r . o b s e r v e ( t h i s . c o n t a i n e r ) , t h i s . s v g C o m p u t e d S t y l e & & t h i s . o b s e r v e S V G S t y l e ( t h i s . s v g C o m p u t e d S t y l e ) } o b s e r v e S V G S t y l e ( t ) { c o n s t e = ( ) = > { c o n s t i = ( ( ) = > { c o n s t e = t . g e t P r o p e r t y V a l u e ( " - - p r e s e r v e - a s p e c t - r a t i o " ) ; i f ( e ) r e t u r n e . t r i m ( ) ; r e t u r n ` x $ { ( ( { t e x t A l i g n : t , d i r e c t i o n : e } ) = > { i f ( t . e n d s W i t h ( " l e f t " ) ) r e t u r n " M i n " ; i f ( t . e n d s W i t h ( " r i g h t " ) ) r e t u r n " M a x " ; i f ( " s t a r t " = = = t | | " e n d " = = = t ) { l e t i = " r t l " = = = e ; r e t u r n " e n d " = = = t & & ( i = ! i ) , i ? " M a x " : " M i n " } r e t u r n " M i d " } ) ( t ) } Y M i d m e e t ` } ) ( ) ; i ! = = t h i s . s v g P r e s e r v e A s p e c t R a t i o & & ( t h i s . s v g P r e s e r v e A s p e c t R a t i o = i , t h i s . u p d a t e S V G R e c t ( ) ) , t = = = t h i s . s v g C o m p u t e d S t y l e & & r e q u e s t A n i m a t i o n F r a m e ( e ) } ; e ( )
< / 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 " library's library" 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 it’
Learning D3 both requires working in an unfamiliar environment (JavaScript) and with a very unique style of programming based around their concept of the [" data join" ](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 > / * ! ! L i c e n s e : h t t p s : / / u n p k g . c o m / @ m a r p - t e a m / m a r p - c l i @ 3 . 4 . 0 / l i b / b e s p o k e . j s . L I C E N S E . t x t * /
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