--- theme: custom-theme --- # 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? --- ## 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?
![](example1a.png)
```python 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?
![](example1b.png) ``` alt.Chart(random_df).mark_line().encode( x="a", y="c", )` ```
--- ## Effectiveness Revisited ![width:800px](effectiveness.png) ---
**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. ---
![](stevens.png)
| 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 ![](stunning-3d-chart.jpg) --- ![](datavizproject.png) --- ![](3d-scatter.png) --- ## 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 --- ## Color --- ## Acknowledgements & References Thanks to Alex Hale, Andrew McNutt, and Jessica Hullman for sharing their materials. - https://www.math.csi.cuny.edu/~mvj/GC-DataViz-S23/lectures/L6.html - https://en.wikipedia.org/wiki/Stevens%27s_power_law