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

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Presentation on theme: "1 SIMS 247: Information Visualization and Presentation Marti Hearst Sept 19, 2005."— Presentation transcript:

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

2 2 ColorColor Most of this segment taken from Colin Ware, Ch. 4

3 3 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)

4 4 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

5 5 Images from lecture by Terrance Brooke CIE Color Model CIE = Commision Internationale L’Eclairage

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

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

8 8 Images from lecture by Terrance Brooke Small Color Patches More Difficult to Distinguish

9 9 Slide 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

10 10 Slide adapted from Wolfgang Muller, http://www.iuw.fh- darmstadt.de/mueller/SS2002/VisWP/07-color-color.pdf Isolating Color Names within a Computer Display

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

12 12 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

13 13 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

14 14 Slide 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)

15 15 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

16 16 Color Scheme Types (Brewer) From http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.htmlhttp://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html

17 17 Binary Example From http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.htmlhttp://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html

18 18 Sequential Examples From http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.htmlhttp://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html

19 19 Sequential Example From http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.htmlhttp://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html

20 20 Spectral Scheme Not suitable for sequential data From http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.htmlhttp://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html

21 21 Qualitative Differences Example From http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.htmlhttp://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html

22 22 Qualitative Color Schemes From http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.htmlhttp://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html

23 23 Diverging Color Examples From http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.htmlhttp://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html

24 24 Divering Color Scheme From http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.htmlhttp://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html

25 25 Qualitative-Binary Example From http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.htmlhttp://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html

26 26 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.htmlhttp://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html

27 27 Application to Class Projects Map of Immigration Routes http://www.sims.berkeley.edu/~vijay/InfoViz/project/writeup/

28 28 Next Time Start Interaction Topics Turn in A1 Learn about A2


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