CS-5630 / CS-6630 Visualization for Data Science Design Guidelines; - - PowerPoint PPT Presentation

cs 5630 cs 6630 visualization for data science design
SMART_READER_LITE
LIVE PREVIEW

CS-5630 / CS-6630 Visualization for Data Science Design Guidelines; - - PowerPoint PPT Presentation

CS-5630 / CS-6630 Visualization for Data Science Design Guidelines; Tasks Alexander Lex alex@sci.utah.edu [xkcd] Exam Theory Questions Whats bad about a rainbow color scale? Which channels are good for quantitative data? Design Critique


slide-1
SLIDE 1

CS-5630 / CS-6630 Visualization for Data Science Design Guidelines; Tasks

Alexander Lex alex@sci.utah.edu

[xkcd]

slide-2
SLIDE 2

Exam

Theory Questions

What’s bad about a rainbow color scale? Which channels are good for quantitative data?

Design Critique

Given a vis, analyze what’s good/bad and redesign.

Conceptual questions about HTML/D3/JavaScript

How does data binding work? How do you access data? Where is the bound data stored in the DOM? What is the DOM?

Find the bug question.

slide-3
SLIDE 3

Next Week

Tuesday: D3 Maps Thursday: Interaction

Mandatory Reading

Heer, J., & Shneiderman, B. (2012). Interactive dynamics for visual analysis. https://doi.org/ 10.1145/2133806.2133821

slide-4
SLIDE 4

Next Homework

slide-5
SLIDE 5

Design Guidelines

slide-6
SLIDE 6

Rule #1: Use the Best Visual Channel Available for the Most Important Aspect of your Data

Effectiveness Principle

slide-7
SLIDE 7

Rule #2: The visualization should show all of the data, and only the data

Expressiveness Principle

slide-8
SLIDE 8

Book Recommendation

Great book with simple design guidelines Not a “Visualization” book, but a “charting” book

slide-9
SLIDE 9

Edward Tufte

graphical integrity and excellence

slide-10
SLIDE 10

Design Excellence

“Well-designed presentations of interesting data are a matter of substance, of statistics, and of design.”

slide-11
SLIDE 11

Tufte: SparklinesTM

http://www.nytimes.com/interactive/2016/upshot/presidential-polls-forecast.html#recent-state-changes

slide-12
SLIDE 12
slide-13
SLIDE 13

Tufte’s Integrity Principles

Show data variation, not design variation Clear, detailed, and thorough labeling and appropriate scales Size of the graphic effect should be directly proportional to the numerical quantities (“lie factor”)

slide-14
SLIDE 14

The Lie Factor

Size of effect shown in graphic Size of effect in data

slide-15
SLIDE 15

Lie Factor - Graphical Integrity

Magnitude in data must correspond to magnitude of mark

Flowing Data

Effect in Data: factor 1.14 Effect in Graphic: factor 5 Lie Factor: 5/1.14 = 4.38

slide-16
SLIDE 16

Scale Distortions

Flowing Data

slide-17
SLIDE 17

What’s wrong?

slide-18
SLIDE 18

What’s wrong?

slide-19
SLIDE 19

What’s wrong?

slide-20
SLIDE 20
slide-21
SLIDE 21

Start Scales at 0?

  • A. Kriebel,

VizWiz

slide-22
SLIDE 22

Scales at 0

slide-23
SLIDE 23

Use a baseline that shows the data, not the zero-point.

  • E. Tufte

Think about: what is a meaningful baseline?

slide-24
SLIDE 24

Global Warming?

The Daily Mail, UK, Jan 2012

slide-25
SLIDE 25

Global Warming?

Mother Jones

slide-26
SLIDE 26

Global Warming - Frame the Data

Mother Jones

Also see: USA Temperature: can I sucker you?

slide-27
SLIDE 27

What’s wrong?

slide-28
SLIDE 28

Scale Distortions

slide-29
SLIDE 29

Temporal Data

slide-30
SLIDE 30

What’s wrong?

slide-31
SLIDE 31

Height of the Bar encodes mean of a distribution Which value is more likely to belong to the distribution? 
 A or B?

http://www.tandfonline.com/doi/full/10.1080/00031305.2016.1141706

slide-32
SLIDE 32

Biases

We can plot the data faithfully, but still perceive it wrongly!

slide-33
SLIDE 33

What about now?

B

slide-34
SLIDE 34

Within the Bar Bias

Experimental Conditions Results

Christopher S. Pentoney & Dale E. Berger (2016) Confidence Intervals and the Within-the-Bar Bias, The American Statistician, 70:2, 215-220

slide-35
SLIDE 35

What’s the Trendline?

slide-36
SLIDE 36

Regression by eye

http://idl.cs.washington.edu/files/2017-RegressionByEye-CHI.pdf [Corell & Heer, 2017]

We’re good at spotting trends But the wrong vis technique can deceive us

slide-37
SLIDE 37

Death to Pie Charts

Cole Nussbaumer www.storytellingwithdata.com/2011/07/death-to-pie-charts.html

“I hate pie charts. I mean, really hate them.”

Share of coverage

  • n TechCrunch
slide-38
SLIDE 38

Redesign

slide-39
SLIDE 39

Can you spot the differences?

slide-40
SLIDE 40

Can you spot the differences?

slide-41
SLIDE 41

My favorite pie chart

slide-42
SLIDE 42

My second favorite pie chart

slide-43
SLIDE 43

So, what to use instead?

http://www.storytellingwithdata.com/blog/2014/06/alternatives-to-pies

imagine you just completed a pilot summer learning program on science aimed at improving perceptions of the field among 2nd and 3rd grade elementary children

slide-44
SLIDE 44

Alternative #1: Show the Number(s) Directly

slide-45
SLIDE 45

Alternative #2: Simple Bar Graph

slide-46
SLIDE 46

Alternative #3: 100% Stacked Horizontal Bar Graph

slide-47
SLIDE 47

Alternative #4: Slopegraph

slide-48
SLIDE 48

Visualization Design Principles

slide-49
SLIDE 49

Maximize Data-Ink Ratio

0-$24,999 $25,000+ 0-$24,999 $25,000+

slide-50
SLIDE 50

Maximize Data-Ink Ratio

175 350 525 700 Males Females

0-$24,999 $25,000+ 0-$24,999 $25,000+

slide-51
SLIDE 51

Avoid Chart Junk

  • ngoing, Tim Brey

Extraneous visual elements that distract from the message

slide-52
SLIDE 52

Avoid Chart Junk

  • ngoing, Tim Brey
slide-53
SLIDE 53

Avoid Chart Junk

  • ngoing, Tim Brey
slide-54
SLIDE 54

Avoid Chart Junk

  • ngoing, Tim Brey
slide-55
SLIDE 55

Avoid Chart Junk

  • ngoing, Tim Brey
slide-56
SLIDE 56

Avoid Chart Junk

  • ngoing, Tim Brey
slide-57
SLIDE 57

Which is better?

[Bateman et al. 2010]

slide-58
SLIDE 58

Chart Junk

https://twitter.com/simongerman600/status/883061933577871360

slide-59
SLIDE 59

Which is better?

https://eagereyes.org/criticism/chart-junk-considered-useful-after-all

[Bateman et al. 2010]

slide-60
SLIDE 60
slide-61
SLIDE 61

EXPERIMENTAL RESULTS

  • 1. No difference for interpretation accuracy
  • 2. No difference in recall accuracy after a five-minute gap
  • 3. Significantly better recall for Holmes charts of both the chart topic

and the details (categories and trend) after long-term gap (2-3 weeks).

  • 4. Participants saw value messages in the Holmes charts significantly

more often than in the plain charts.

  • 5. Participants found the Holmes charts more attractive, most enjoyed

them, and found that they were easiest and fastest to remember.

slide-62
SLIDE 62

PROS persuasion memorability engagement CONS biased analysis trustworthiness interpretability space efficiency

Use Chart Junk? It depends!

slide-63
SLIDE 63

Alignment Matters

https://twitter.com/infowetrust/status/760521739092627457 http://www.visualisingdata.com/2016/08/little-visualisation-design-part-21/

slide-64
SLIDE 64

No Unjustified 3D

Depth judgment is bad

N = 0.67 Sensation=Intensity^N

Occlusion Perspective Distortion Color: Lighting / Shadows / 
 Shading Tilted Text illegible

slide-65
SLIDE 65

Don’t

matplotlib gallery

Excel Charts Blog
slide-66
SLIDE 66

Don’t

https://www.vice.com/en_uk/read/foi-uk-drug-conviction-ethnicity-282

slide-67
SLIDE 67

3D Design Alternatives

http://interactions.acm.org/archive/view/july-august-2018/the-good-the-bad-and-the-biased

slide-68
SLIDE 68

3D Design Alternatives

http://interactions.acm.org/archive/view/july-august-2018/the-good-the-bad-and-the-biased

slide-69
SLIDE 69

Example: Hierarchy Visualization

[F. van Ham ; J.J. van Wijk, 2002]

slide-70
SLIDE 70

Eyes Beat Memory

Don’t make people memorize: Show them

http://www.randalolson.com/2015/08/23/small-multiples-vs-animated-gifs-for-showing-changes-in-fertility-rates-over-time/

slide-71
SLIDE 71

What can we do differently?

slide-72
SLIDE 72

Eyes Beat Memory: Small Multiples

A lot of charts Do we need all of them?

slide-73
SLIDE 73

Eyes Beat Memory: Small Multiples

slide-74
SLIDE 74

Simplify!

slide-75
SLIDE 75

Small Multiple Design Alternatives

http://interactions.acm.org/archive/view/july-august-2018/the-good-the-bad-and-the-biased

slide-76
SLIDE 76

Design Critique / Redesign

slide-77
SLIDE 77

Sunday Star Times, 2012

https://goo.gl/lHWp4x

slide-78
SLIDE 78
  • R. Cunliffe, Stats Chat

Quantity encoded by diameter, not area! Fixing that:

slide-79
SLIDE 79
  • R. Cunliffe, Stats Chat

But is this visual encoding appropriate in the first place?