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1 SIMS 247: Information Visualization and Presentation Marti Hearst Sept 19, 2005.

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1 SIMS 247: Information Visualization and Presentation Marti Hearst Sept 19, 2005
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Page 1: 1 SIMS 247: Information Visualization and Presentation Marti Hearst Sept 19, 2005.

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SIMS 247: Information Visualization and PresentationMarti Hearst

Sept 19, 2005 

 

Page 2: 1 SIMS 247: Information Visualization and Presentation Marti Hearst Sept 19, 2005.

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Color

Most of this segment taken from Colin Ware, Ch. 4

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Terms• Hue

– The differences in color that languages assign names to• Saturation:

– Sometimes called “vividness”, sometimes “brightness”• Lightness:

– A relative measure– How much light appears to reflect from an object compared

to what looks like white in a scene (Brewer)– Also sometimes called “value”

• Other terms– These are used inconsistently– Intensity (often used to mean Saturation + Lightness)– Luminance (physically measured amount of reflected light) – Chromaticity (hue without brightness)

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Color Issues• Complexity of color space

– 3-dimensional– Computer vs. Print display– There are many models and standards

• Color not critical for many visual tasks– Doesn’t help with determination of:

• Layout of objects in space• Motion of objects• Shape of objects

– Color-blind people often go for years without knowing about their condition

• Color is essential for– “Breaking camouflage”– Recognizing distinctions

• Picking berries out from leaves• Spoiled meat vs. good

– Aesthetics

Page 5: 1 SIMS 247: Information Visualization and Presentation Marti Hearst Sept 19, 2005.

5Images from lecture by Terrance Brooke

CIE Color ModelCIE = Commision Internationale L’Eclairage

Page 6: 1 SIMS 247: Information Visualization and Presentation Marti Hearst Sept 19, 2005.

6Slide adapted from Wolfgang Muller, http://www.iuw.fh-darmstadt.de/mueller/SS2002/VisWP/07-color-color.pdf

CIE Color Model Properties

Page 7: 1 SIMS 247: Information Visualization and Presentation Marti Hearst Sept 19, 2005.

7Slide adapted from Wolfgang Muller, http://www.iuw.fh-darmstadt.de/mueller/SS2002/VisWP/07-color-color.pdf

CIE Color Model Properties

Page 8: 1 SIMS 247: Information Visualization and Presentation Marti Hearst Sept 19, 2005.

8Images from lecture by Terrance Brooke

Small Color Patches More Difficult to Distinguish

Page 9: 1 SIMS 247: Information Visualization and Presentation Marti Hearst Sept 19, 2005.

9Slide adapted from Wolfgang Muller, http://www.iuw.fh-darmstadt.de/mueller/SS2002/VisWP/07-color-color.pdf

Order of Appearance of Color Names across World Cultures

Page 10: 1 SIMS 247: Information Visualization and Presentation Marti Hearst Sept 19, 2005.

10Slide adapted from Wolfgang Muller, http://www.iuw.fh-darmstadt.de/mueller/SS2002/VisWP/07-color-color.pdf

Isolating Color Names within a Computer Display

Page 11: 1 SIMS 247: Information Visualization and Presentation Marti Hearst Sept 19, 2005.

11Slide adapted from Wolfgang Muller, http://www.iuw.fh-darmstadt.de/mueller/SS2002/VisWP/07-color-color.pdf

Background Color Contrast

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Some Color Fun Facts

• People agree strongly on what pure yellow is• There may be two unique greens• Brown is dark yellow, requires a reference

white nearby

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Colors for Labeling

• Ware recommends to take into account:– Distinctness– Unique hues– Contrast with background– Color blindness– Number

• Only a small number of codes can be rapidly perceived

– Field Size• Small changes in color are difficult to perceive

– Conventions

Page 14: 1 SIMS 247: Information Visualization and Presentation Marti Hearst Sept 19, 2005.

14Slide adapted from Terrance Brooke

Ware’s Recommended Colors for Labeling

Red, Green, Yellow, Blue, Black, White, Pink, Cyan, Gray, Orange, Brown, Purple.The top six colors are chosen because they are the unique colors that mark the ends of the opponent color axes. The entire set corresponds to the eleven color names found to be the most common in a cross-cultural study, plus cyan (Berlin and Kay)

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More Color Use Guidelines

• From Cynthia Brewer reading– She’s a cartographer, has a unique perspective– Geocoordinates are already taken

• Four-way Guidelines:– Binary– Qualitative– Diverging– Sequential

• Make combinations of these– Seq-Seq, Seq-Qual, etc.– I’m not convinced these all work

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Color Scheme Types (Brewer)

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

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Binary Example

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

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Sequential Examples

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

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Sequential Example

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

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Spectral SchemeNot suitable for sequential data

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

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Qualitative Differences Example

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

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Qualitative Color Schemes

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

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Diverging Color Examples

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

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Divering Color Scheme

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

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Qualitative-Binary Example

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

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Qualitative-Sequential Example

I suspect this is too much to keep track of.

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

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Application to Class Projects

• Map of Immigration Routeshttp://www.sims.berkeley.edu/~vijay/InfoViz/project/writeup/

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Next Time

• Start Interaction Topics• Turn in A1• Learn about A2


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