Information Visualization Crash Coursepoloclub.gatech.edu/cse6242/2017fall/slides/CSE... · Crash...
Transcript of Information Visualization Crash Coursepoloclub.gatech.edu/cse6242/2017fall/slides/CSE... · Crash...
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Information VisualizationCrash Course
Chad StolperAssistant Professor
Southwestern University(graduated from Georgia Tech CS PhD)
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(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 MantraWhere 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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9 �X
Edward TufteAn American statistician and professor emeritus of political science, statistics, and computer science at Yale University.
He is noted for his writings on information design and as a pioneer in the field of data visualization.
-Wikipedia
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Space Shuttle ChallengerJanuary 28, 1986
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What happened?
Morning Temperature: 31°F
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14https://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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32http://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 SetProperty 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
43https://en.wikipedia.org/wiki/Anscombe%27s_quartet
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Anscombe’s Quartet
Sanity Checking ModelsOutlier Detection
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Anscombe’s Quartet
Sanity Checking ModelsOutlier Detection
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Anscombe’s Quartet
Sanity Checking ModelsOutlier Detection
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Anscombe’s Quartet
Sanity Checking ModelsOutlier Detection
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Data visualization leverageshuman perception
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Name the five senses.
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Sense Bandwidth(bits/sec)
Sight 10,000,000Touch 1,000,000Hearing 100,000Smell 100,000Taste 1,000
http://www.britannica.com/EBchecked/topic/287907/information-theory/214958/Physiology
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A (Simple) Modelof Human Visual Perception
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A (Simple) Model of Human Perception
Parallel detection of
basic features into an iconic
store
Serial processing of object
identification and spatial layout
Stage 1 Stage 2
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Stage 1: Pre-Attentive Processing
RapidParallel
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
1281768756138976546984506985604982826762980985845822450985645894509845098094358590910302099059595957725646750506789045678845789809821677654876364908560912949686
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Example
1281768756138976546984506985604982826762980985845822450985645894509845098094358590910302099059595957725646750506789045678845789809821677654876364908560912949686
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A few more examples fromProf. 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.
75
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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
76
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Stephen Few“Now You See It”pg. 39
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Pre-Attentive à Cognitive
78
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Gestalt Psychology
Berlin, Early 1900s
79
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Gestalt Psychology
Goal was to understandpattern perception
Gestalt (German) = “seeing the whole picture all at once”
Identified 8 “Laws of Grouping”
80
http://study.com/academy/lesson/gestalt-psychology-definition-principles-quiz.html
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Gestalt Psychology
1. Proximity2. Similarity3. Closure4. Symmetry5. Common Fate6. Continuity
7. Good Gestalt8. Past Experience
81
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How many groups are there?
82
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83
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Proximity
84
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How many groups are there?
85
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86
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Similarity
87
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How many shapes are there?
88
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89
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Closure
90
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How many items are there?
91
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( ) { } [ ]
92
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( ) { } [ ]
Symmetry
93
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How many sets are there?
94
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95
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96
Common Fate
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How many objects are there?
97
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98
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Continuity
99
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How many objects are there?
100
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101
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Good Gestalt
102
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What is this word?(Please Shout)
103
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FLICK104
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Past Experience
FLICK105
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Past Experience
FLICK106
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Pre-Attentive Processing
Gestalt Laws
107
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Detect Quickly
108
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Detect quickly does NOT meandetect accurately
Ideally you want both.
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110Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design.Heerand Bostock. Proc ACM Conf. Human Factors in Computing Systems (CHI) 2010, p. 203–212.
Accuracy: Vis experiments
22after Michael McGuffin course slides, http://profs.etsmtl.ca/mmcguffin/
[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
Cleveland & McGill’s Results
Crowdsourced Results
1.0 3.01.5 2.52.0Log Error
1.0 3.01.5 2.52.0Log Error
Accuracy: Vis experiments
22after Michael McGuffin course slides, http://profs.etsmtl.ca/mmcguffin/
[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
Cleveland & McGill’s Results
Crowdsourced Results
1.0 3.01.5 2.52.0Log Error
1.0 3.01.5 2.52.0Log Error
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111Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design.Heerand Bostock. Proc ACM Conf. Human Factors in Computing Systems (CHI) 2010, p. 203–212.
1.0
1.5
2.0
2.5
3.0
10 20 30 40 50 60 70 80 90
T1T2T3T4T5T6T7T8T9
True Proportional Difference (%)
Lo
g E
rro
r
Figure 3: Midmeans of log absolute errors againsttrue percentages for each proportional judgment type;superimposed are curves computed with lowess.
the results for the position-angle experiment to those for theposition-length experiment. By designing judgment types 6and 7 to adhere to the same format as the others, the resultsshould be more apt for comparison. Indeed, the new resultsmatch expectations: psychophysical theory [7, 34] predictsarea to perform worse than angle, and both to be significantlyworse than position. Theory also suggests that angle shouldperform worse than length, but the results do not support this.Cleveland & McGill also did not find angle to perform worsethan length, but as stated their position-angle results are notdirectly comparable to their position-length results.
EXPERIMENT 1B: RECTANGULAR AREA JUDGMENTSAfter successfully replicating Cleveland & McGill’s results,we further extended the experiment to more judgment types.We sought to compare our circular area judgment (T7) re-sults with rectangular area judgments arising in visualiza-tions such as cartograms [9] and treemaps [26]. We hypoth-esized that, on average, subjects would perform similarly tothe circular case, but that performance would be impacted byvarying the aspect ratios of the compared shapes. Based onprior results [19, 34], we were confident that extreme varia-tions in aspect ratio would hamper area judgments. “Squar-ified” treemap algorithms [3, 35] address this issue by at-tempting to minimize deviance from a 1:1 aspect ratio, but itis unclear that this approach is perceptually optimal. We alsowanted to assess if other differences, such as the presence ofadditional distracting elements, might bias estimation.
MethodWe again used Cleveland & McGill’s proportional judgmenttask: subjects were asked to identify which of two rectangles(marked A or B) was the smaller and then estimate the per-centage the smaller was of the larger by making a “quickvisual judgment.” We used a 2 (display) ⇥ 9 (aspect ra-tios) factorial design with 6 replications for a total of 108unique trials (HITs). In the first display condition (T8) we
Cleveland & McGill's Results
1.0 1.5 2.0 2.5 3.0 3.5
T1
T2
T3
T4
T5
Log Error
Crowdsourced Results
1.0 1.5 2.0 2.5 3.0 3.5
T1
T2
T3
T4
T5
T6
T7
T8
T9
Log Error
Figure 4: Proportional judgment results (Exp. 1A & B).Top: Cleveland & McGill’s [7] lab study. Bottom: MTurkstudies. Error bars indicate 95% confidence intervals.
1.0 1.5 2.0 2.5 3.0 3.5
2/3 : 2/3
1 : 1
3/2 : 3/2
2/3 : 1
2/3 : 3/2
1 : 3/2
Log Error
Aspect R
atios
Figure 5: Rectangular area judgments by aspect ratios(1B). Error bars indicate 95% confidence intervals.
showed two rectangles with horizontally aligned centers; inthe second display condition (T9) we used 600⇥400 pixeltreemaps depicting 24 values. Aspect ratios were determinedby the cross-product of the set { 2
3 , 1, 32} with itself, roughly
matching the mean and spread of aspect ratios produced bya squarified treemap layout (we generated 1,000 treemaps of24 uniformly-distributed random values using Bruls et al.’slayout [3]: the average aspect ratio was 1.04, the standard de-viation was 0.28). We systematically varied area and propor-tional difference across replications. We modified the squar-ified treemap layout to ensure that the size and aspect ratioof marked rectangles matched exactly across display condi-tions; other rectangle areas were determined randomly.
As a qualification task, we used multiple-choice versions oftwo trial stimuli, one for each display condition. For eachtrial (HIT), we requested N=24 assignments. We also re-duced the reward per HIT to $0.02. We chose this numberin an attempt to match the U.S. national minimum wage (as-suming a response time of 10 seconds per trial).
CHI 2010: Visualization April 10–15, 2010, Atlanta, GA, USA
206
Accuracy: Vis experiments
22after Michael McGuffin course slides, http://profs.etsmtl.ca/mmcguffin/
[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
Cleveland & McGill’s Results
Crowdsourced Results
1.0 3.01.5 2.52.0Log Error
1.0 3.01.5 2.52.0Log Error
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Automating the Design of Graphical Presentations l 125
More accurate
Less accurate
I I Position
IMll 1 I Length
F-l
Iha I I0.I I I
Volume
rl l¶kJ
Color cl 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 on Graphics, Vol. 5, No. 2, April 1986.
Mackinlay, 1986112
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Stephen Few“Now You See It”pg. 41 113
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What does this tell us?
114
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Barcharts, scatterplots, and line charts are really effective
for quantitative data
0 20 400
2040
0 20 400
20
40
0 20 40115
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(and for statistical distributions)Tukey Box Plots
119
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Median
Outliers
Largest<Q3+1.5IQR
Smallest>Q1- 1.5IQR
Largest<Q3
Smallest>Q1
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121
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Tufte’s Chart Principles
122
Edward Tufte
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Tufte’s Chart Principles
DO NOT LIE!Maximize Data-Ink Ratio
Minimize Chart Junk
125
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Tufte’s Chart Principles
DO NOT LIE!Maximize Data-Ink Ratio
Minimize Chart Junk
126
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127
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http://www.perceptualedge.com/blog/?p=790129
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http://xkcd.com/1138/
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Tufte’s Chart Principles
DO NOT LIE!Maximize Data-Ink Ratio
Minimize Chart Junk
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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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PLEASE DON’TEVER DO THIS!
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Two times to usea 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
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Anyone else boredby 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 informationIncrease appeal
Increase memorabilityAnother dimension to work with
Color
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How many of you haveheard 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, 1986159
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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 femalesare Red-Green Colorblind
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168http://viz.wtf/post/98981561686/ht-matthewbgilmore-noaas-new-weather-modelling
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Color and Quantitative Data
Gray scale Single sequencepart spectral scale
Full spectral scale Single sequencesingle hue scale
Double-endedmultiple hue scale
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Color and Quantitative Data
Can you order these (lowàhi)?
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Colormaps
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• categorical limits: noncontiguous– 6-12 bins hue/color
• far fewer if colorblind
– 3-4 bins luminance, saturation
– size heavily affects salience• use high saturation for small regions, low saturation for large
after [Color Use Guidelines for Mapping and Visualization. Brewer, 1994. http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html]
Binary
Diverging
Categorical
Sequential
Categorical
Categorical
http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html viaMunzner
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Color Scales
Color Brewerhttp://colorbrewer2.org/
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OverviewZoom+Filter
Details on Demand
Shneiderman Mantra(Information-Seeking Mantra)
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174http://www.babynamewizard.com/voyager
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http://visual.ly/every-single-death-game-thrones-series176
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Where to learn more?
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CS 7450Information 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/1466508914189
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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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