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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CX4242 Information Visualization Crash Course (AKA Information Visualization 101) Chad Stolper Google (graduated from Georgia Tech CS PhD) 1 What is Infovis? Why is it Important? Human Perception Chart Basics (If Time, Some Color Theory)

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Information Visualization Crash Course

Chad Stolper Google

(graduated from Georgia Tech CS PhD)

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(AKA Information Visualization 101)

CX4242

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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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X

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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Morning Temperature: 31°F

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SLIDE 11 11 Tufte, E. R. (2012). Visual explanations: images and quantities, evidence and narrative. Cheshire, CT: Graphics Press.
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SLIDE 12 13 Video originally from: http://www.FeynmanPhysicsLectures.com

Most Watched Science Experiment

Richard Feynman, Physics Nobel laureate explained how rubber became rigid in cold temperate YouTube video: https://youtu.be/6Rwcbsn19c0

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How did this happen?

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SLIDE 14 15 Tufte, E. R. (2012). Visual explanations: images and quantities, evidence and narrative. Cheshire, CT: Graphics Press.

Engineers at Morton Thiokol, the rocket maker, presented on the day before and recommended not to launch.

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So, communication is extremely important.

Visualization can help with that – communicate ideas and insights.

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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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https://en.wikipedia.org/wiki/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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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

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Parallel detection of basic features into an iconic store Serial processing of

  • bject 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 (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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Raise your hand if a RED DOT is present…

(On the left or on the right?)

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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  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

  • nce” instead of a collection of parts

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?

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CLIP

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Past Experience

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CLIP

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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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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.
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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.

significantly find McGill’ confident “Squar ified” McGill’ “quick ” ⇥ first

Crowdsourced Results

1.0 1.5 2.0 2.5 3.0

Log Error

McGill’ confidence confidence

⇥ squarified ’ modified ified qualification April 10–15, 2010, Atlanta, GA, USA

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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

20 40 20 40 20 40 20 40 20 40

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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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“Cumulative”

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

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Tufte’s Chart Principles

DO NOT LIE!

Maximize Data-Ink Ratio Minimize Chart Junk

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  • Chartjunk. (2017, October 05). Retrieved December 01, 2017, from https://en.wikipedia.org/wiki/Chartjunk
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Please…

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No pie charts. No 2.5D charts.

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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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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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Have you heard of RGB?

RGB color model. (2017, November 20). Retrieved December 01, 2017, from https://en.wikipedia.org/wiki/RGB_color_model

Additive color model: colors create by mixing red, green, blue light

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

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Hue Lightness Saturation

Source: color picker in Affinity Designer

HSV Color Model

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Hue

Post & Greene, 1986

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Hue

http://blog.xkcd.com/2010/05/03/color-survey-results/

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Hue and Colorblindness

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

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http://viz.wtf/post/98981561686/ht-matthewbgilmore-noaas-new-weather-modelling

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Color and Quantitative Data

Can you order these (lowhi)?

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

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Color Brewer for Picking Color Scales

COLORBREWER 2.0. (n.d.). Retrieved December 01, 2017, from http://colorbrewer2.org/
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Overview Zoom+Filter Details on Demand

Shneiderman Mantra (Information-Seeking Mantra)

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https://www.mat.ucsb.edu/g.legrady/academic/courses/11w259/schneiderman.pdf

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http://visual.ly/every-single-death-game-thrones-series

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http://www.babynamewizard.com/voyager

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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. html

  • 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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