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05.uncertainity/slides.md

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+# Visualizing Uncertainty
+
+## CAPP 30239
+
+---
+
+![bg fit](climate1.png)
+
+<!--
+What is this trying to show?
+source: https://www.ipcc.ch/report/ar6/wg1/figures/chapter-3/figure-3-4/
+-->
+
+---
+
+![bg fit](climate2.png)
+
+<!--
+These are showing essentially the same thing, one shows individual models and the other uses some aggregates with confidence intervals.
+These are from the same page of the IPCC report.
+-->
+
+---
+
+## What causes uncertainty?
+
+1) **measurement error** - An instrument used has some non-perfect degree of accuracy. In a survey, this could be a poorly-worded question.
+2) **model uncertainty** - Models make assumptions and simplifications, different assumptions lead to different outcomes.
+3) **sampling variability** - Differences between sample & population.
+4) **missing data** - How missing data is accounted for & represented.
+
+The result is that we have a range or distribution, where we want a number to use with one of our channels (Hue, X, Y, etc.).
+
+---
+
+## Challenges of Uncertainty
+
+Often left out, in part due to being hard to understand, and even harder to visualize.
+
+Omission however misleads audiences, especially where a lot of significant figures are included.
+
+![width:100%](clock.png)
+
+*Global Population Uncertainty: ±160 million people (2%)*
+
+---
+
+## Challenges of Uncertainty
+
+Uncertainty estimates are simplified, often out of necessity.
+
+![](weather.png)
+
+30% chance of rain: "A 30% chance that at least 0.01" of rain will fall somewhere within a given area over a 12 hour period."
+
+**Do I bring an umbrella?**
+
+---
+
+## Challenges of Uncertainty
+
+Complexity of visualization can overwhelm audience, obscure other meaning.
+
+![](matplotlib.png)
+
+From a data-ink ratio perspective, it is understandable why if the error bars do  not seem relevant to a narrative, that they would be omitted.
+
+---
+
+## Including Uncertainty
+
+If omitting uncertainty misleads, it violates our prime directive of **graphical integrity**.
+
+The job then, is to find ways that are **audience appropriate** & **don't obfuscate the meaning**.
+
+The difficulty will be in resolving this tension.
+
+---
+
+## Common Techniques
+
+- Uncertainty as Probability
+- Error Bars
+- Confidence Bands
+
+---
+
+## Uncertainty As Probability
+
+
+![width:100%](probability-waffle.png)
+
+Random waffle chart: works for cases with discrete outcomes.
+
+---
+
+## Uncertainty As Probability
+
+In practice, we often care about more than boolean outcome.
+![](election-prediction.png)
+
+---
+
+![bg fit](election-quantile.png)
+
+<!--
+
+We can convert this to discrete measurements: quantile dot plot.
+
+-->
+
+
+---
+
+![bg fit](biden-trump.webp)
+
+<!-- source: fivethirtyeight -->
+
+---
+
+## Uncertainty of Point Estimates
+
+These work when we're focused on uncertainty around a particular outcome.
+
+Sometimes we need to show uncertainty around discrete measurements, or projections.
+
+---
+
+### Error Bars
+
+![](data-ci.png)
+
+---
+
+### Error Bands
+
+![bg left](altair-errorband.png)
+
+```python
+line = alt.Chart(source).mark_line().encode(
+    x='Year',
+    y='mean(Miles_per_Gallon)'
+)
+
+band = alt.Chart(source).mark_errorband(extent='ci').encode(
+    x='Year',
+    y=alt.Y('Miles_per_Gallon').title('Miles/Gallon'),
+)
+
+band + line
+```
+
+---
+
+### Issues with Error Bars & Confidence Bands
+
+1) There is no pre-defined meaning of these intervals.
+  **If error bars or bands are included, the legend must include information on the meaning.**
+2) Error bars are common in scientific & academic literature, other audiences cannot be assumed to understand them.
+3) Restricted to 1D/2D dots. If variable being expressed is mapped to color, area, etc. then alternative presentations needed.
+
+---
+
+![bg fit](2016.jpg)
+
+---
+
+### Variations on Error Bars & Intervals
+
+![](ci-2.png)
+
+<!-- when appropriate, can also be used to show multiple intervals -->
+
+---
+
+![bg fit](ci-3.png)
+
+<!--care should be taken that distribution is indeed normal if curves/etc. chosen  -->
+
+---
+
+![](2d-bars.png)
+
+---
+
+### Regression Uncertainty
+
+![width:900px](multiple-reg.png)
+
+---
+
+### Regression Uncertainty
+
+![width:900px](translucent-band.png)
+
+---
+
+## Other Approaches
+
+---
+### Showing Multiple Futures
+
+![](multiple-outcomes.webp)
+
+---
+
+![width:900px](climate1.png)
+
+---
+
+### Hurricane Uncertainity
+
+<div class="container">
+<div class="col">
+
+![width:450](hurricane2.png)
+
+</div>
+<div class="col">
+
+![width:450px](hurricane1.png)
+
+  </div>
+</div>
+
+<!-- source: https://tamucoa.b-cdn.net/app/uploads/2021/10/House2011TrackUncertaintyVisualization.pdf -->
+
+---
+### On Maps
+
+![bg fit](map-uncertainity.png)
+
+<!-- source: https://www.e-education.psu.edu/geog486/sites/www.e-education.psu.edu.geog486/files/Lesson_07/Images/ex_vs_ont.PNG --> 
+
+---
+### "Sketchiness"
+
+![](sketchy.gif)
+
+---
+### Animating Uncertainty
+
+- 
+
+- HOP Plot: <https://vallandingham.me/animating_uncertainty.html>
+
+---
+## References & Acknowledgements
+
+- <https://clauswilke.com/dataviz/visualizing-uncertainty.html>
+- <https://www.mjskay.com/presentations/aalto2020-uncertainty.pdf>
+- <https://nightingaledvs.com/dark-sky-weather-data-viz/>
+- <https://mucollective.co/uncertainty_collection/>