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Completed Assignment 2: Filled good/bad visualization type evaluation #118

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Completed Assignment 2: Filled good/bad visualization type evaluation #118

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32 changes: 17 additions & 15 deletions 02_activities/assignments/assignment_2.md
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- For each visualization (good and bad):
- Explain (with reference to material covered up to date, along with readings and other scholarly sources, as needed) why you classified that visualization the way you did.
```
Your answer...
Good Visualization: Polar area (rose/coxcomb) diagram: https://datavizproject.com/data-type/polar-area-chart/

Reasons:
1. Perceptual quality: Effectively uses proportional area (wedge size) to show relative contributions, allowing quick perception of dominant categories without distortion when proportions are accurate
2. Substantive quality: Can accurately and honestly represent categorical data with clear emphasis on key differences
3. Aesthetic quality: Innovative and visually pleasing layout with color differentiation draws attention to the main message without unnecessary clutter

Improvements:
1. Combine with modern interactivity (e.g., hover details) to enhance accessibility for detailed reading
2. Add patterns or high-contrast alternatives to colors for better equity and colorblind accessibility

Bad Visualization: Pie Chart: https://datavizproject.com/data-type/pie-chart

Reasons:
1. Perceptual quality — Difficult to accurately compare slice areas or angles, especially with many categories
2. Substantive quality — Easily misleads on proportions (e.g., small differences appear large or vice versa), violating data accuracy/honesty
3. Aesthetic quality — Can become cluttered and visually overwhelming with >5 slices, distracting from clear message conveyance

Improvements:
1. Replace with bar chart (position/length for better comparison) to improve perceptual accuracy
2. Limit to ≤4 categories and add labels/percentages for clarity, or use donut variation with caution to reduce visual noise



```
- How could this data visualization have been improved?
```
Your answer...







```
- Word count should not exceed (as a maximum) 500 words for each visualization (i.e.
300 words for your good example and 500 for your bad example)

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