CS-5630 / CS-6630 Visualization Design Guidelines; Tasks Alexander - - PowerPoint PPT Presentation

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CS-5630 / CS-6630 Visualization Design Guidelines; Tasks Alexander - - PowerPoint PPT Presentation

Dec 13, 2023 313 likes •930 views

CS-5630 / CS-6630 Visualization Design Guidelines; Tasks Alexander Lex alex@sci.utah.edu [xkcd] Design Critique / Redesign https://goo.gl/lHWp4x Sunday Star Times, 2012 Quantity encoded by diameter, not area! Fixing that: R. Cunliffe,

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CS-5630 / CS-6630 Visualization Design Guidelines; Tasks

Alexander Lex alex@sci.utah.edu

[xkcd]

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Design Critique / Redesign

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Sunday Star Times, 2012

https://goo.gl/lHWp4x

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  • R. Cunliffe, Stats Chat

Quantity encoded by diameter, not area! Fixing that:

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  • R. Cunliffe, Stats Chat

But is this visual encoding appropriate in the first place?

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Design Guidelines

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Edward Tufte

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Design Excellence

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

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Tufte’s Lessons

Practice: graphical integrity and excellence Theory: design principles for data graphics

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Graphical Integrity

Flowing Data

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Scale Distortions

Flowing Data

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What’s wrong?

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Scale Distortions

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Scale Distortions

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Start Scales at 0?

  • A. Kriebel,

VizWiz

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Global Warming?

The Daily Mail, UK, Jan 2012

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Global Warming?

Mother Jones

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Global Warming - Frame the Data

Mother Jones

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The Lie Factor

Tufte, VDQI

Size of effect shown in graphic Size of effect in data

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The Lie Factor

(Size of effect in graphic)/(size of effect in data)

5.3 − 0.6 0.6 /27.5 − 18 18 = 14.8

Tufte, VDQI

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The Lie Factor

Tufte, VDQI

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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”)

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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
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Redesign

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Can you spot the differences?

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Can you spot the differences?

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My favorite pie chart

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My second favorite pie chart

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Visualization Design Principles

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Maximize Data-Ink Ratio

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

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Maximize Data-Ink Ratio

175 350 525 700 Males Females

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

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Avoid Chartjunk

  • ngoing, Tim Brey

Extraneous visual elements that distract from the message

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Avoid Chartjunk

  • ngoing, Tim Brey
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Avoid Chartjunk

  • ngoing, Tim Brey
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Avoid Chartjunk

  • ngoing, Tim Brey
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Avoid Chartjunk

  • ngoing, Tim Brey
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Avoid Chartjunk

  • ngoing, Tim Brey
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Which is better?

[Bateman et al. 2010]

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Which is better?

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

[Bateman et al. 2010]

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EXPERIMENTAL RESULTS

  • 1. No significant difference between plain and image charts for interactive

interpretation accuracy

  • 2. No significant 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.

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PROS persuasion memorability engagement CONS unbiased analysis trustworthiness interpretability space efficiency

Use Chart Junk? It depends!

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Don’t

matplotlib gallery

Excel Charts Blog
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Tasks

Why are we using Visualization?

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Domain and Abstract Tasks

Infinite numbers of domain tasks Can be broken down into simpler abstract tasks We know how to address the abstract tasks! Identify task - data combination: solutions probably exist

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Tasks

Analyze

high-level choices consume vs produce

Search

find a known/unknown item

Query

find out about characteristics of item by itself or relative to others

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Example 1

Find good universities with a high faculty student ratio.

Identify high-ranked universities In this subset: compare universities & identify high faculty student ratio

OR

Derive a ranking with a high weight for faculty student ratio

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Example 2

Contrast Harvard’s reputation scores with MIT’s Match up Harvard with Yale

First, find Harvard and Yale, then compare their (two) reputation scores

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Example 3

Find a combination of weights and parameters where Harvard is better than MIT

Produce a new dataset by deriving from the input parameters

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Result

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High-level actions: Analyze

Consume discover vs present

classic split: explore vs explain

enjoy: casual, social Produce Annotate, record Derive: crucial design choice

Analyze Consume

Present Enjoy Discover

Produce

Annotate Record Derive

tag

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Example: Annotate

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Example: Derive

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Example: Derive

Country Club Club Continent Ronaldo Portugal Real Madrid Europe Lahm Germany Bayern München Europe Robben Netherlands Bayern München Europe Khedira Germany Real Madrid Europe Phogba Italy Juventus Europe Messi Argentina Barcelona Europe

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Actions: Mid-level search, low- level query

what does user know?

target, location

how much of the data matters?

  • ne, some, all

Search Query Identify Compare Summarize

Target known Target unknown Location known Location unknown

Lookup Locate Browse Explore

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Example Compare (& Derive)

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Why: Targets

Trends ALL DATA Outliers Features ATTRIBUTES One Many

Distribution Dependency Correlation Similarity Extremes

NETWORK DATA SPATIAL DATA Shape Topology

Paths

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Examples

Trends: How did the job market develop since the recession overall? Outliers: Looking at real estate related jobs

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How? A Preview

Encode Manipulate Facet Reduce Arrange Map Change Select Navigate Express Separate Order Align Use Juxtapose Partition Superimpose Filter Aggregate Embed from categorical and ordered attributes