534 lines
12 KiB
Markdown
534 lines
12 KiB
Markdown
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theme: custom-theme
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---
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# Perception & Color
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## CAPP 30239
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---
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## Today
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- What matters most when creating a visualization?
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- How does human **perception** factor into visualization design?
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- Understanding **color**, and computational representations of it.
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---
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## What is the most important question when creating a visualization?
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---
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## What is the most important question when creating a visualization?
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<ul>
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<li><s>Where will the data come from?</s>
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<li><s>What type of chart do I use?</s></li>
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<li>Who is the audience?</li>
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</ul>
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---
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## Audience First
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- Who are you presenting to?
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- How familiar are they with the data?
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- What is their numerical & visualization literacy?
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- Via what medium will they receive the information?
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- What are you trying to do? (Persuade, Inform, Inspire?)
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*Only now can we start thinking about data and presentation.*
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---
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## Perception
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- **Selective** - We can only pay attention to so much.
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- **Patterns** - Our brains are pattern-matching machines, audience will benefit from intentional patterns & be distracted by unintentional ones.
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- **Limited working memory** - We hold a very limited set of information in our minds at once.
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---
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## What do you see?
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<div class="container">
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<div class="col">
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![](viz-1.png)
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</div><div class="col">
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```python
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alt.Chart(random_df).mark_point().encode(
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alt.X("a"),
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alt.Y("c"),
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alt.Color("b"),
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alt.Size("c"),
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alt.Shape("a:N"),
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alt.Fill("b"),
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alt.Opacity("b"),
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)
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```
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</div>
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</div>
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---
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## What do you see?
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<div class="container">
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<div class="col">
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![](viz-2.png)
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```
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alt.Chart(random_df).mark_line().encode(
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x="a",
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y="c",
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)`
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```
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</div>
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</div>
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---
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## Effectiveness Revisited
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![width:800px](effectiveness.png)
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---
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<div class="container">
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<div class="col">
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**Altair Channels**
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- Position (`X, Y`)
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- Angle (`Angle`)
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- Area (`Radius`, `Size`)
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- Hue, Saturation (`Color`)
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- Texture (`Opacity`, `Fill`)
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- Shape (mark type, `Shape`)
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</div>
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<div class="col">
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**What about?**
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- Length
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- Slope
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- Volume
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- Density
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- Connection
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- Containment
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</div>
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</div>
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---
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**Derived Properties**
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- Length/Area - size of bars (`X`, `Y`)
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- Slope & Density - affected by scale
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- Connection - ex. layering of lines w/ points
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- Containment - achieved with layering
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What about *volume*?
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---
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## Stevens' Power Law
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Stevens (1975): Human response to sensory stimulus is characterized by a power law with different exponents with different stimuli.
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perception = (magnitude of sensation)<sup>a</sup>
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Smaller <sup>a</sup> exponent: harder to perceive changes.
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Stevens measured values of a by exposing people to varied stimulus and asking them to compare magnitudes.
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---
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<div class="container"><div class="col">
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![](stevens.png)
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</div><div class="col">
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| Continuum | Exponent |
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|-|-|
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| Color **Brightness**| 0.33-0.5 |
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| Smell| 0.6 |
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| Loudness | 0.67 |
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| **Depth Perception** | 0.67 |
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| Area | 0.7 |
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| 2D Planar Position | 1.0 |
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| Warmth | 1.3-1.6 |
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| Color **Saturation** | 1.7 |
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| Electric Shock | 3.5 |
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</div></div>
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---
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## 3D Graphs
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![](stunning-3d-chart.jpg)
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---
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![](datavizproject.png)
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---
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![](3d-scatter.png)
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---
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## Instead of 3D Graphs
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- Find other channels: hue & size are good candidates. (bubble chart)
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- Or make multiple 2D graphs with XY/YZ/XZ pairs.
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![bg left](scatter-matrix.png)
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---
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## What is Color?
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Wavelengths of light are perceived as particular colors:
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![](linear_visible_spectrum.svg.png)
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What's missing?
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<!-- credit: https://en.wikipedia.org/wiki/File:Linear_visible_spectrum.svg -->
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---
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## Color & the Eye
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### Rods
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- spread throughout retina
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- more sensitive in low light conditions
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- brightness ("lightness")
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### Cones
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- 3 types with peak sensitivity at different frequencies
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- concentrated in center of eye
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- less sensitive in low light conditions
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- hue & saturation
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![bg right](cone-fundamentals.png)
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<!-- source https://commons.wikimedia.org/wiki/File:Cone-fundamentals-with-srgb-spectrum.svg -->
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---
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## Spectrum vs. What We See
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What we actually see is always a blend of multiple peaks.
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This is impacted by ambient light conditions, as well as quirks of our visual processing.
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![height:400px](the-dress.jpg)
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In actuality, multiple combinations of light can give same color (**metamers**).
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---
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## Chromatic Adaptation
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![](demo-bw-illusion-andrew-steele.gif)
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Source: Andrew Steele <https://www.youtube.com/channel/UC-XYsDNh4-886rMNLnnwR_w>
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---
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## Color Naming
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Color naming is highly subjective, and research has shown that the ability to name a color correlates highly with the ability to distinguish it.
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![bg right](xkcd-color-map.png)
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Be particularly careful with blue/green boundaries, as there are significant cultural differences.
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Source: https://blog.xkcd.com/2010/05/03/color-survey-results/
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---
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## Cultural Considerations
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![bg right width:600px](hok-uk.svg)
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- American audiences associated <span color="red">red</span> & <span color="blue">blue</span> with political parties on any map in a political context.
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- Also international meaning of <span color="red">red</span> & <span color="blue">blue</span> is flipped: red is left, blue is right.
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- Most other colors have contradictory meanings depending on culture. For example, yellow might be chosen to denote success (parts of Africa) or be associated with death (Middle East).
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<!--Source: https://www.color-meanings.com/color-symbolism-different-cultures/-->
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<!-- image from wikipedia: UK House of Commons -->
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---
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## Color Vision Deficiency
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More accurate name for what is commonly known as colorblindness.
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- Red-Green CVD - most common
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- four types: Dueteranomaly and Protanomaly (mild) to Protanopia and Dueteranopia (complete)
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- Tritanomaly/Tritanopia: blue/green and yellow/red confusion.
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- rarest, complete lack of color vision, usually corresponds to other vision issues as well
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![bg right width:600px](colorblind.jpg)
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---
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## Color on a Page
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![bg right](cmyk.svg)
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Ink absorbs light, so we work with subtractive blending. Our base colors are cyan, magenta, and yellow. To save on ink costs, we throw in black/contrast as well.
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We call this CMYK color.
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---
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## Color on a Screen
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Screens emit light, which means we use **additive blending** of red, green, and blue light. Every pixel of a screen can emit these three colors in different intensities.
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![bg right](additive.png)
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---
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## Color Spaces
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Ways of describing a color mathematically, usually have 3 components to match our perception of color:
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- RGB (early photography)
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- CIE XYZ (1931)
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- HSB/HSV/HSL (1970s)
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![bg right width:700px](rgb-khan.jpg)
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<!-- https://commons.wikimedia.org/wiki/File:Rgb-compose-Alim_Khan.jpg -->
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---
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A common way to refer to colors is by their intensity in each of these three channels.
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<span style="color: rgb(0% 100% 0%)">this is 0% red, 100% green, 0% blue intensity (#00ff00)</span>
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<span style="color: rgb(20% 60% 20%)">this is 20% red, 60% green, 20% blue intensity: (#143c14)</span>
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<span style="color: #ff00ff">this is 100% red, 0% green, 100% blue intensity: #ff00ff</span>
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This is sometimes expressed in hexadecimal:
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![height:120px](hexrgb.png)
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![bg right](rgb-pixels.jpg)
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---
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### RGB space as a cube
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![cube](LinearRGBCube.png)
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---
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### RGB as pair plots
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![pair plots](RGBPairPlots.png)
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Remember this trick for your own 3-dimensional data!
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---
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![height:500px](TriangleSliceRGB.png)
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A slice through the middle of the cube gives colors of comparable brightness. (You may have seen such a triangle in color pickers.)
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---
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## HSL
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![height:500px](HSL.png)
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An alternative color space that's very useful for visualization is HSL color space.
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Hue, Saturation, Lightness | <https://hslpicker.com/>
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---
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## Aside: What about "alpha"?
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You will often see a fourth channel: RGB**A**, HSL**A**.
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This is known as alpha transparency (translucency).
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This has to do with how the program in question *blends* the colors. The final pixel values on the screen will still be converted to RGB components.
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- Use sparingly.
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- Variations are very subtle, and background dependent.
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<!-- image source: https://upload.wikimedia.org/wikipedia/commons/3/34/RGB_pixels.jpg -->
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---
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## CIE (RGB / XYZ / CIELAB)
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Based on human perception experiments where people would adjust dials to recreate colors out of red, green, and blue light.
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First from 1920s, revised in 1970s.
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*Commission internationale de l'éclairage* (Illumination)
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![bg right](cie-xyz.png)
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---
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## Screen Gamut
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Screens can't show the entire range of visible colors accurately, they define a "gamut". Since ~1996 most devices aim at a standard gamut to ensure similar representations of color, but even high end devices are not perfectly aligned.
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![bg right](gamut2.png)
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Projectors (like the one you're likely viewing this on) usually have skewed gamut.
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Moral of the story: **Consider your medium!**
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---
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## What does all this mean for visualization?
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Color choices should be made with respect to:
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- medium (screen vs. print, type of screen)
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- audience (culture, vision differences, expectations)
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- differentiability
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---
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## Role of Color
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- **Identify** - Different color per category/actor.
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- opt for distinct hues
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- **Group** - Group like entities using same/like colors.
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- often with similar hues
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- **Layer** - Overlay different information while keeping contrast.
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- saturation differences very important to not overwhelm eye
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- **Highlight** - Call out important/relevant information.
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- brightness and hue differences important
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---
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## Color Channels & Data Types
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### Lightness is perceived as ordered
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Good for **Ordinal** variables
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![height:50px](ordinal.png)
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**Quantitative** (Continuous) variables harder to discern
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![height:50px](qual.png)
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### Hue typically unordered
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**Nominal** variables.
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![height:50px](nominal.png)
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---
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### Types of Palettes
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- Qualitative - Nominal data
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- Sequential - Quantitative data
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- Diverging - Data with a meaningful zero-point (increase/decrease, more/less)
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![](palette-types.jpg)
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<!-- source: Peter Aldhous, NICAR 2016 -->
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---
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### Hue Separation
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Pick distinct hues for unrelated variables.
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Grouped schemes can be used where there are relationships among the categories.
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![bg left width:600px](vega-schemes.png)
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<https://vega.github.io/vega/docs/schemes/>
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---
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## "Get it right in black & white"
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A common mantra among visual designers.
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Ensure that your hues have different brightness levels.
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Ensure that you aren't using hue alone for your image.
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![bg left width:600px](vega-schemes-bw.jpg)
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---
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## Text Legibility
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An important issue when using colored text and/or backgrounds is **legibility**.
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Web Content Accessibility Guidelines require a 4.5 color contrast (3:1 for large text).
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Minimize *saturation* in backgrounds, pick a font color with opposing *lightness*.
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<https://webaim.org/resources/contrastchecker/>
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![bg left width:600px](contrast.png)
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---
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## Tools
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- Vega Schemes: https://vega.github.io/vega/docs/schemes/
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- Contrast/theme exploration: https://schubert-da.github.io/dataviz-palette-tool/
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- Theme exploration for cartography: <https://colorbrewer2.org/>
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- Color-theory based theme creator: https://meodai.github.io/poline/
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- Theme creator w/ theme sharing: https://coolors.co
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- HSL/RGB picker: <https://hslpicker.com/>
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- Contast checker: <https://webaim.org/resources/contrastchecker/>
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### Color-Blindness
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- MacOS/iOS app: https://michelf.ca/projects/sim-daltonism/
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- Browser extensions (search "colorblindness" in your browser of choice)
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---
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## Acknowledgements & References
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Thanks to Alex Hale, Andrew McNutt, and Jessica Hullman for sharing their materials.
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Color space images are from <https://jamie-wong.com/post/color/>, which is an incredible resource if you'd like to go deeper into both the biology and math of color.
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- https://www.math.csi.cuny.edu/~mvj/GC-DataViz-S23/lectures/L6.html
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- https://en.wikipedia.org/wiki/Stevens%27s_power_law
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- https://colorusage.arc.nasa.gov
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- https://vega.github.io/vega/docs/schemes/
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