Information Visualization Crash Course (AKA Information - - PowerPoint PPT Presentation

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Information Visualization Crash Course (AKA Information - - PowerPoint PPT Presentation

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Information Visualization Crash Course (AKA Information Visualization 101) Chad Stolper Assistant Professor Southwestern University (graduated from Georgia Tech CS PhD) 1 What is Infovis? Why is it Important? Human Perception Chart Basics

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Information Visualization Crash Course

Chad Stolper Assistant Professor Southwestern University

(graduated from Georgia Tech CS PhD) 1

(AKA Information Visualization 101)

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What is Infovis? Why is it Important? Human Perception Chart Basics

(If Time, Some Color Theory)

The Shneiderman Mantra Where to Learn More

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What is Information Visualization?

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Information Visualization

“The use of computer-supported, interactive, visual representations of abstract data to amplify cognition.” Card, Mackinlay, and Shneiderman 1999

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Communication Exploratory Data Analysis (EDA)

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Communication

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(gone wrong)

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Edward Tufte

An American statistician and professor emeritus of political science, statistics, and computer science at Yale University. He is noted for his writings

  • n information design and

as a pioneer in the field of data visualization.

  • Wikipedia
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Space Shuttle Challenger

January 28, 1986

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What happened?

Morning Temperature: 31°F

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https://www.youtube.com/watch?v=6Rwcbsn19c0

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How did this happen?

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Morton Thiokol’s Presentation

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So, communication is extremely important.

Visualization can help with that – communicate ideas and insights.

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SLIDE 26 32 http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html
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Visualization can also help with Exploratory Data Analysis (EDA)

But why do you need to explore data at all???

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“There are three kinds of lies: lies, damned lies, and statistics.”

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Mystery Data Set

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Mystery Data Set

Property Value mean( x ) 9 variance ( x ) 11 mean( y ) 7.5 variance ( y ) 4.122 correlation ( x,y ) 0.816 Linear Regression Line y = 3 + 0.5x

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Anscombe’s Quartet

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https://en.wikipedia.org/wiki/Anscombe%27s_quartet

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Anscombe’s Quartet Sanity Checking Models Outlier Detection

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Anscombe’s Quartet Sanity Checking Models Outlier Detection

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Anscombe’s Quartet Sanity Checking Models Outlier Detection

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Anscombe’s Quartet Sanity Checking Models Outlier Detection

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Data visualization leverages human perception

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Name the five senses.

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Sense Bandwidth (bits/sec) Sight 10,000,000 Touch 1,000,000 Hearing 100,000 Smell 100,000 Taste 1,000

http://www.britannica.com/EBchecked/topic/287907/information-theory/214958/Physiology
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A (Simple) Model

  • f Human Visual Perception
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A (Simple) Model of Human Perception

Parallel detection of basic features into an iconic store Serial processing

  • f object

identification and spatial layout

Stage 1 Stage 2

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Stage 1: Pre-Attentive Processing

Rapid Parallel Automatic

(Fleeting = lasting for a short time)

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Stage 2: Serial Processing Relatively Slow (Incorporates Memory) Manual

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Stage 1: Pre-Attentive Processing

The eye moves every 200ms

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Stage 1: Pre-Attentive Processing

The eye moves every 200ms (so this processing occurs every 200ms-250ms)

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Example

1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686

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Example

1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686

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A few more examples from

  • Prof. Chris Healy at NC State
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Left Side Right Side

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Left Side Right Side

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Raise your hand if a RED DOT is present…

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Color (hue) is pre-attentively processed.

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Raise your hand if a RED DOT is present…

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Shape is pre-attentively processed.

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Determine if a RED DOT is present…

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Hue and shape together are NOT pre-attentively processed.

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Pre-Attentive Processing

  • length
  • width
  • size
  • curvature
  • number
  • terminators
  • intersection
  • closure
  • hue
  • lightness
  • flicker
  • direction of motion
  • binocular lustre
  • stereoscopic depth
  • 3-D depth cues
  • lighting direction
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Stephen Few “Now You See It”

  • pg. 39
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Pre-Attentive à Cognitive

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Gestalt Psychology

Berlin, Early 1900s

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Gestalt Psychology

Goal was to understand pattern perception

Gestalt (German) = “seeing the whole picture all at once”

Identified 8 “Laws of Grouping”

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http://study.com/academy/lesson/gestalt-psychology-definition-principles-quiz.html

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Gestalt Psychology

  • 1. Proximity
  • 2. Similarity
  • 3. Closure
  • 4. Symmetry
  • 5. Common Fate
  • 6. Continuity
  • 7. Good Gestalt
  • 8. Past Experience
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How many groups are there?

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Proximity

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How many groups are there?

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Similarity

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How many shapes are there?

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Closure

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How many items are there?

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( ) { } [ ]

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( ) { } [ ]

Symmetry

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How many sets are there?

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Common Fate

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How many objects are there?

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Continuity

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How many objects are there?

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Good Gestalt

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What is this word? (Please Shout)

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FLICK

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Past Experience

FLICK

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Past Experience

FLICK

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Pre-Attentive Processing Gestalt Laws

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Detect Quickly

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Detect quickly does NOT mean

detect accurately

Ideally you want both.

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SLIDE 101 110 Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design.Heer and Bostock. Proc ACM Conf. Human Factors in Computing Systems (CHI) 2010, p. 203–212.

Positions Rectangular areas

(aligned or in a treemap)

Angles Circular areas

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SLIDE 102 111 Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design.Heer and Bostock. Proc ACM Conf. Human Factors in Computing Systems (CHI) 2010, p. 203–212.

Crowdsourced Results

1.0 1.5 2.0 2.5 3.0 T1 T2 T3 T4 T5 T6 T7 T8 T9

Log Error

Positions Rectangular areas (aligned or in a treemap) Angles Circular areas
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More accurate Less accurate

I I

Position

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1 I

Length

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Iha I

I0.I

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Volume

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l¶kJ Color

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mot Shown)
  • Fig. 14. Accuracy ranking of quantitative
perceptual tasks. Higher tasks are accom- plished more accurately than lower tasks. Cleveland and McGill empirically verified the basic properties of this ranking. Quantitative Ordinal Nominal Position Position Color Saturation Position Color Hue Texture Connection Containment Density Color Saturation Color Saturation Shape Length Angle Slope Area Volume
  • Fig. 15. Ranking of perceptual tasks. The tasks shown in the gray boxes are not relevant to these
types of data.

An example analysis for area perception is shown in Figure 16. The top line shows that a series of decreasing areas can be used to encode a tenfold quantitative

  • range. Of course, in a real diagram such as Figure 13, the areas would be laid out

randomly, making it more difficult to judge the relative sizes of different areas accurately (hence, area is ranked fifth in Figure 14). Nevertheless, small mis- judgments about the size of an area only leads to small misperceptions about the corresponding quantitative value that is encoded. The middle line shows that area can encode three ordinal values. However, one must be careful to make sure

ACM Transactions
  • n Graphics, Vol. 5, No. 2, April
1986.

Mackinlay, 1986

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Stephen Few “Now You See It”

  • pg. 41
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What does this tell us?

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Barcharts, scatterplots, and line charts are really effective for quantitative data

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(and for statistical distributions) Tukey Box Plots

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Median Outliers Largest < Q3 + 1.5 IQR Smallest > Q1 - 1.5 IQR Largest < Q3 Smallest > Q1

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Tufte’s Chart Principles

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Edward Tufte

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Tufte’s Chart Principles

DO NOT LIE!

Maximize Data-Ink Ratio Minimize Chart Junk

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Tufte’s Chart Principles

DO NOT LIE!

Maximize Data-Ink Ratio Minimize Chart Junk

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http://www.perceptualedge.com/blog/?p=790

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10 20 30 10 20 30 10 20 30 10 20 30 10 20 30

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Tufte’s Chart Principles

DO NOT LIE!

Maximize Data-Ink Ratio Minimize Chart Junk

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SLIDE 119 http://skilfulminds.com/2011/04/05/exploring-the-usefulness-of-chartjunk-at-stl-ux-2011/ 133
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Please…

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No pie charts. No 2.5D charts.

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37 36 24 2 1

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5 10 15 20 25 30 35 40

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PLEASE DON’T EVER DO THIS!

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10 20 30 40

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Two times to use a pie chart…

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50-50

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75-25

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But otherwise…

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Barcharts, scatterplots, and line charts are really effective for quantitative data

20 40 20 40 20 40 20 40 20 40

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Anyone else bored by my color choices?

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In fact, grayscale can be risky…

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In fact, grayscale can be risky…

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Color is Powerful

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Call attention to information Increase appeal Increase memorability Another dimension to work with

Color

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How many of you have heard of RGB?

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We see in RGB, but we don’t interpret in RGB…

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How many have heard of HSV?

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HSV Color Model

Hue/“Color” Saturation/Chroma Value/Lightness

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Hue

Post & Greene, 1986

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Hue

http://blog.xkcd.com/2010/05/03/color-survey-results/ 160
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Hue and Colorblindness

10% of males and 1% of females are Red-Green Colorblind

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Color and Quantitative Data

Gray scale Single sequence part spectral scale Full spectral scale Single sequence single hue scale Double-ended multiple hue scale 169
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Color and Quantitative Data

Can you order these (lowàhi)?

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us

Binary Diverging Categorical Sequential Categorical Categorical

http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html via Munzner
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Color Scales

Color Brewer

http://colorbrewer2.org/

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Overview Zoom+Filter Details on Demand

Shneiderman Mantra (Information-Seeking Mantra)

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SLIDE 154 174 http://www.babynamewizard.com/voyager
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http://visual.ly/every-single-death-game-thrones-series

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Where to learn more?

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CS 7450 Information Visualization Every Fall

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Visualization @GeorgiaTech

vis.gatech.edu

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How to Make Good Charts

  • Edward Tufte’s One-Day Workshop

– http://www.edwardtufte.com/tufte/courses

  • Edward Tufte, Visual Display of Quantitative

Information

– http://www.edwardtufte.com/tufte/books_vdqi

  • Stephen Few, Show Me the Numbers:

Designing Tables and Graphs to Enlighten

– http://www.amazon.com/Show-Me-Numbers- Designing- Enlighten/dp/0970601972/ref=la_B001H6IQ5M_1_ 2?s=books&ie=UTF8&qid=1385050724&sr=1-2

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Visualization Theory “Books”

  • Tamara Munzner VIS Tutorial and Book

– http://www.cs.ubc.ca/~tmm/talks.html – http://www.cs.ubc.ca/~tmm/vadbook/

  • Colin Ware, Information Visualization: Perception for Design

– http://www.amazon.com/Information-Visualization-Perception-Interactive- Technologies/dp/1558605118

  • Stephen Few, Now You See It

– http://www.amazon.com/Now-You-See-Visualization- Quantitative/dp/0970601980/ref=pd_bxgy_b_img_z

  • Edward Tufte, Envisioning Information

– http://www.edwardtufte.com/tufte/books_ei

  • Edward Tufte, Visual Explanations

– http://www.edwardtufte.com/tufte/books_visex

  • Edward Tufte, Beautiful Evidence

– http://www.edwardtufte.com/tufte/books_be

  • Tamara Munzner, Visualization Analysis & Design

– http://www.amazon.com/Visualization-Analysis-Design-AK- Peters/dp/1466508914

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Perception and Color Websites

  • Chris Healy, NC State

– http://www.csc.ncsu.edu/faculty/healey/PP/index.h tml

  • Color Brewer

– http://colorbrewer2.org/

  • Maureen C. Stone (Color Links, Blog,

Workshops)

– http://www.stonesc.com/color/index.htm

  • Subtleties of Color by Robert Simmon of

NASA

– http://blog.visual.ly/subtleties-of-color/

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Visualization Blogs

  • Flowing Data by Nathan Yau

– http://flowingdata.com/

  • Information Aesthetics by Andrew Vande Moere

– http://infosthetics.com/

  • Information is Beautiful by David McCandless

– http://www.informationisbeautiful.net/

  • Visual.ly Blog

– http://blog.visual.ly/

  • Indexed Comic by Jessica Hagy

– http://thisisindexed.com/

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Infographics

Visual.ly/view

(wtfviz.net)

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Thanks!

Chad Stolper

chadstolper@gatech.edu

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Questions?

Chad Stolper

chadstolper@gatech.edu

194 thisisindexed.com Jessica Hagy