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Perception & Color
CAPP 30239
Today
- What matters most when creating a visualization?
- How does human perception factor into visualization design?
- Understanding color, and computational representations of it.
What is the most important question when creating a visualization?
What is the most important question when creating a visualization?
Where will the data come from?What type of chart do I use?- Who is the audience?
Audience First
- Who are you presenting to?
- How familiar are they with the data?
- What is their numerical & visualization literacy?
- Via what medium will they receive the information?
- What are you trying to do? (Persuade, Inform, Inspire?)
Only now can we start thinking about data and presentation.
Perception
- Selective - We can only pay attention to so much.
- Patterns - Our brains are pattern-matching machines, audience will benefit from intentional patterns & be distracted by unintentional ones.
- Limited working memory - We hold a very limited set of information in our minds at once.
What do you see?
alt.Chart(random_df).mark_point().encode(
alt.X("a"),
alt.Y("c"),
alt.Color("b"),
alt.Size("c"),
alt.Shape("a:N"),
alt.Fill("b"),
alt.Opacity("b"),
)
What do you see?
alt.Chart(random_df).mark_line().encode(
x="a",
y="c",
)`
Effectiveness Revisited
Altair Channels
- Position (
X, Y) - Angle (
Angle) - Area (
Radius,Size) - Hue, Saturation (
Color) - Texture (
Opacity,Fill) - Shape (mark type,
Shape)
What about?
- Length
- Slope
- Volume
- Density
- Connection
- Containment
Derived Properties
- Length/Area - size of bars (
X,Y) - Slope & Density - affected by scale
- Connection - ex. layering of lines w/ points
- Containment - achieved with layering
What about volume?
Stevens' Power Law
Stevens (1975): Human response to sensory stimulus is characterized by a power law with different exponents with different stimuli.
perception = (magnitude of sensation)a
Smaller a exponent: harder to perceive changes.
Stevens measured values of a by exposing people to varied stimulus and asking them to compare magnitudes.
| Continuum | Exponent |
|---|---|
| Color Brightness | 0.33-0.5 |
| Smell | 0.6 |
| Loudness | 0.67 |
| Depth Perception | 0.67 |
| Area | 0.7 |
| 2D Planar Position | 1.0 |
| Warmth | 1.3-1.6 |
| Color Saturation | 1.7 |
| Electric Shock | 3.5 |
3D Graphs
Instead of 3D Graphs
- Find other channels: hue & size are good candidates.
- Combine different dimensions into side-by-side 2D graphs.
TODO: example of 2D decomposition of a graph
What is Color?
Wavelengths of light are perceived as particular colors:
In practice: we almost never see monochromatic color, light sources emit a spectrum & we perceive peaks.
Color & the Eye
Rods
- "brightness"
- spread throughout retina
- more sensitive in low light conditions
Cones
- 3 types with peak sensitivity at different frequencies
- concentrated in center of eye
- less sensitive in low light conditions
Metamers ...
TODO: slides from Alex maybe? or drop?
Color Naming
Color Models
- CIE
- RGB
- HS(V|L|B)
Color Channels & Data Types
TODO: https://docs.google.com/presentation/d/1avOsobdcsTG6qaDVCSesIOBFcfxjH12d/edit#slide=id.p27 TODO: (also 33 and 34)
Back to Visualization
Uses of Color
Identify, Group, Layer, Highlight
Types of Palettes
- qualitative
- sequential
- diverging
Hue Separation
- distinct
- grouped
"Get it right in black & white"
Legibility
Cultural Considerations
Human Variation
What about "alpha"?
You will often see a fourth channel: RGBA, HSLA.
This is known as alpha transparency (translucency).
This has to do with how the program in question blends the colors.
By default, the second color drawn overdraws the first.
With translucency we can get a sense of depth without resorting to 3D.
- Use sparingly.
- Variations are very subtle, and background dependent.
TODO: example
Tools
Acknowledgements & References
Thanks to Alex Hale, Andrew McNutt, and Jessica Hullman for sharing their materials.