Data analysis and data visualizations are commonly used to communicate information about a civic issue. For example, the advocacy organization Circulate San Diego began a campaign in 2015 called “Vision Zero” to eliminate traffic fatalities and severe injuries at intersections. Circulate San Diego took a data-driven approach to identify the 15 most dangerous intersections (here’s a link to the original press statement and to their interactive map). The analysis presented in the interactive map is based on Traffic collision data that is publicly available through the San Diego Data Portal (https://data.sandiego.gov/). As of 2019, the city has completed upgrades to the 15 most dangerous intersections and has announced plans to make improvements to 300 more (see press release). The Vision Zero campaign demonstrates the impact that data analysis can have on a civic issue.

In this assignment, you will identify news articles that present data analysis and visualizations related to the mobility topics (i.e., last mile, safe roadways, equitable access, autonomous vehicles, or any topic related to the D4SD challenge) and then participate in a class discussion to (a) interpret the data and (b) generate ideas about additional analyses you might perform to learn more about the mobility topic. For example, Circulate San Diego identified the 15 most dangerous intersections by ordering the Traffic Collision data from 2001-2015 by the highest number of collisions, injuries, and deaths. We might perform additional analysis to investigate intersection characteristics that increase the likelihood of a collision:

• When do most collisions occur (e.g., early morning, late evening)?
• How has the collision rate around an intersection changed over time (e.g., steady increase, sudden increase, remained static)?
• What about the environment surrounding an intersection might contribute to collisions (e.g., highway off-ramp, lack of bike lanes, neighborhood bars)?

Hypothesizing about additional data collection and analysis can shed light on a mobility problem and can help you to develop ideas about where and how to focus your further exploration of the problem-space, e.g., through field-work, interviews, prototyping, community feedback.

Learning goals

• Gaining a deeper understanding of a civic issue by finding and interpreting publicly available data
• Critiquing the data analyses and visualizations presented in a news article by raising questions about the data provenance (e.g., collection), visual representation, and narrative
• Recommending additional steps to understand the data and to more deeply investigate the mobility issue

What to do

For this assignment you can stick with the topic you explored in A1 or pick any topic related to the D4SD challenge.

1. Spend at least 30 minutes searching the internet for an article that presents a data visualization related to your topic. Ideally the article is about a mobility issue in San Diego, but it does not necessarily need to be local to San Diego. If you cannot find an article with a data visualization related to your problem, you could find data in raw form or you can reach out to stakeholder organizations to see if they have any data to share (see the advice on reaching out to people).
2. At the top of your personal note-taking Google document used in A1, please think about and write reflections on the following questions:
   • According to the article text, what are the key takeaways from the data analysis? How does the data visualization support the main point of the article?
   • Where did the data come from? Look for a cited source in the article and trace it back to the original data, report, or data search tool.
   • What do you notice in the visualization? Write 1-3 sentences about the observations, trends, clusters, or categories that stand out as you look at the visualization.
   • What do you wonder? Look at the visualization for a few minutes and write 1-3 sentences about what your curious about? You might be curious, for instance, about additional data sets or deeper analyses on this data set.
   • What groups of people are included and missing from the visualization? Discuss why being included and excluded from the data analysis may be problematic.
   • What additional analysis might we perform to learn more about how residents of San Diego are affected by the mobility topic?
3. By Friday Jan 17th at 5pm, synthesize your notes above into a short post (~200-300 words) on the Slack forum that summarizes your notes. Make sure to include a link to the news article. Your discussion should comment on what you notice, wonder, and think is going on in the visualization. You might critique the origins of the data, how it was analyzed, or how the data are represented. You might reflect on what other analyses or datasets we need to better understand the civic problem.
4. Finally, before class on Tuesday Jan 21st (the following week), read two other students’ posts, as well as the original articles, and add a comment to the reply thread for each (if someone else has not already created a thread, click on “Start a thread”). Your two comments on different articles could touch on:
   1. How would you interpret the data presented in this article differently than your peers?
   2. What about the data, its collection or analysis, may be problematic?
   3. What additional analysis might we perform to learn more about how residents of San Diego are affected by the mobility topic?
   4. Who might we contact to learn more about the issue presented in the data visualization (e.g., speak with Circulate San Diego)?

Deliverables

• Raw notes on the data analysis and visualization for a specific news article (do this in your personal digital notebook Google doc)
• A Slack forum post that summarizes what you notice, wonder, and think is going on in a data visualization presented in a news article (before Friday Jan 17th at 5pm)
• Responses to two other students’ posts on different articles (due by class time on Tuesday Jan 21st)

Grading rubric

Grades will be on a 5-pt scale (5% of total class grade) based on the following:

• How extensively does the student identify and discuss different factors associated with the data visualization?
• How clear and concise is the summary posted to Slack?
• How strong are the recommendations for additional analysis?
• How deeply did the student engage with other student posts?