assignment-2 done

02_activities/assignments/assignment_2.md

@@ -10,18 +10,43 @@
 - 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...
+      Your answer...GOOD DATA VISUALIZATION:
 
+Visualization: Line Chart - Global Temperature Anomalies Over Time
+(Featured on DataVizProject, https://datavizproject.com/data-type/line-chart/)
 
+Why this is a good visualization
 
+This visualization is an effective example of good data visualization because it clearly communicates change over time using an appropriate line chart. A line chart is well-suited for temporal data, aligning with Cleveland and McGill's principle that position along a common scale is the most accurate visual encoding for quantitative comparison (Cleveland & McGill, 1984).
 
+1) the visualization is clear and simple. The axes are clearly labeled, units are specified, and the time scale is consistent. There is no unnecessary decoration or chart junk. This allows the viewer to focus on the underlying trend rather than being distracted by visual noise.
 
+2) the visualization supports accurate interpretation. The zero baseline is meaningful, and the scale is not manipulated to exaggerate changes. This helps prevent misleading conclusions and supports ethical data communication, a key topic in data visualization theory.
+
+3) the visualization provides context and narrative support. A brief title and annotation explain what “temperature anomaly” means, reducing cognitive load for non-expert viewers which reducing cognitive effort improves comprehension and retention, which this visualization achieves effectively.
+
+Overall, this visualization succeeds because it matches the data type to an appropriate visual form, prioritizes accuracy, and supports user understanding.
+
+BAD DATA VISUALIZATION: 3D Pie Chart Showing Market Share Across Categories
+(Source: DataVizProject, https://datavizproject.com/data-type/pie-chart/)
+
+Why this is a bad visualization
+
+This visualization is a poor example of effective data communication because it violates several fundamental principles of visualization design. 
+1) The most significant issue is the use of a 3D pie chart, which distorts perception. In class we learned that angle and area judgments are less accurate than position-based encodings, and the 3D effect further exacerbates misinterpretation by obscuring slice sizes.
+
+2) the visualization suffers from poor readability and clutter. Labels overlap, colors are not distinguishable from each other, and there is no clear ordering of categories. This increases cognitive load and makes it difficult for viewers to quickly identify key comparisons.
+
+3) the visualization lacks a clear message or analytical purpose. Pie charts are already limited in their ability to support comparison, and this chart includes too many categories, making it nearly impossible to accurately compare proportions. Visualizations should reveal patterns and relationships; this chart obscures them instead.
+
+Additionally, the legend requires viewers to constantly shift their gaze between the chart and labels, reducing usability and increasing interpretation time.
 
 
       ```
     - How could this data visualization have been improved?  
       ```
-      Your answer...
+      Your answer...For the good Visualization example: Although effective, the visualization could be improved in several ways. First, adding confidence intervals or would increase the variability and reliability of the measurements, which is especially important for scientific data. and then, Second, interactive features (such as hover-over values or regional filters) could enhance exploratory analysis for users who want deeper insight, aligning with modern visualization practices.
+      Bad Visualization: This visualization could be significantly improved by replacing the 3D pie chart with a sorted bar chart, which would allow for precise comparisons using a common baseline. Bars should be ordered from largest to smallest to highlight relative differences clearly. Reducing the color palette and labeling bars directly would further improve readability. Finally, adding a clear title that communicates the key takeaway would help transform the visualization from decorative to informative.
 
 
 
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     * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.
 
 Checklist:
-- [ ] Create a branch called `assignment-2`.
-- [ ] Ensure that the repository is public.
-- [ ] Review [the PR description guidelines](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md#guidelines-for-pull-request-descriptions) and adhere to them.
-- [ ] Verify that the link is accessible in a private browser window.
+- [ X] Create a branch called `assignment-2`.
+- [ X] Ensure that the repository is public.
+- [X ] Review [the PR description guidelines](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md#guidelines-for-pull-request-descriptions) and adhere to them.
+- [X ] Verify that the link is accessible in a private browser window.
 
 If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.