Data Visualization Accessibility

assignment_3.md

@@ -0,0 +1,73 @@
+# Data Visualization
+
+## Assignment 3: Data Visualization Ethics
+
+### Requirements:
+- Let’s return to the data visualizations we evaluated for Assignment 2.  
+- For each visualization: 
+    - Explain (with reference to material covered up to date, along with readings and other scholarly sources, as needed) whether or not you think this data visualization is accessible, reproducible, and equitable. 
+        ```
+        Your answer...
+CASE STUDY 1: "Iran Protests", visualization created by Federica Fragapane (https://www.behance.net/gallery/154164323/Iran-protests) 
+      By representing the datapoints through the strand of hair, the visual graph is linked to the originating story.
+      Accessibility is not the strength of this visualization. The Color choice and the graphical representations do need to be explained in the context in order for the reader to understand why the author has chosen this particular visual representation of the data. The legend explain what the markers mean, but the way that are overlapped in the viz is not explained in the legend.  
+      The reproducibility of the visualization is very low, mainly due to the aesthetical qualities of the representation. It would be a challenge to reproduce the work of the author and get an identical or very similar result.
+      With the barriers to understanding the message of the visualization, the representation is not equitable. Many readers might be left out from acting upon reading the visualization because the information, while beautifully presented, is not easely understood.
+
+        ```
+    - How could this data visualization have been improved (in terms of accessibility, reproducibility, equity)?  
+        ```
+        Your answer...
+      By representing the datapoints through the strand of hair, the visual graph is linked to the originating story. A more detailed contextualization would have made easier to understand the graphical representation. Adding descriptive details imporoves the accesibility.
+      Choosing the colors and explaining (by using labels) what it means would improve its readability.
+      Helping the reader to understand the message of the visualization makes it more equitable.
+
+
+CASE STUDY 2: "Number of World Heritage Sites" (https://100.datavizproject.com/data-type/viz9/)
+The representation is a bar plot, representing data collected in two different years. The graph is highly reproducible.
+The accessibility is achieved through the choice of color, labelled values and lack of clutter. However, the lack of context and any explanation of how the data was collected, what might explain the different numerical values, and what is the value the graph bring to the reader using it is also notable about this visualization.
+While the plot is clean and easily understood, the context is missing and this rends the visualization less equitable.
+
+
+    - How could this data visualization have been improved (in terms of accessibility, reproducibility, equity)?  
+        ```
+        Your answer...
+Anchoring the data and its representation in its context will make the graph more equitable. The message of the viz will be more clear, leading to a call of action or change. The plot's accessibility will be improved with the color choice and use of legends (what each of the three colors represents). Contextualization will bring a point of reference for the numerical values in the labels. Explaining how the entries were selected would add to clarity to the message of the graph. 
+The plot chosen to represent data is easily reproducible.
+
+
+
+        ```
+
+- Word count should not exceed (as a maximum) 300 words for each visualization. 
+
+### Why am I doing this assignment?:
+- This ongoing assignment ensures active participation in the course, and assesses learning outcomes 2 and 3:  
+* Apply general design principles to create accessible and equitable data visualizations
+* Use data visualization to tell a story
+
+### Rubric:
+| Component               | Scoring   | Requirement                                                 |
+|-------------------------|-----------|-------------------------------------------------------------|
+| Data viz classification and justification | Complete/Incomplete | - Data viz are clearly classified as good or bad<br />- At least three reasons for each classification are provided<br />- Reasoning is supported by course content or scholarly sources |
+| Suggested improvements  | Complete/Incomplete | - At least two suggestions for improvement<br />- Suggestions are supported by course content or scholarly sources |
+
+## Submission Information
+
+🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.
+
+### Submission Parameters:
+* Submission Due Date: `HH:MM AM/PM - DD/MM/YYYY`
+* The branch name for your repo should be: `assignment-3`
+* What to submit for this assignment:
+    * This markdown file (assignment_3.md) should be populated and should be the only change in your pull request.
+* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/visualization/pull/<pr_id>`
+    * 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-3`.
+- [ ] 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.
+
+If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-3-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.