Adam Sage

Stephanie Doctor

The normalized maps were very interesting to see. I don’t think there was anything surprising for me there – Clinton with NY and CA, Sanders with the Northeast, Cruz with Texas, and Kasich with Ohio. I do think it was important to normalize. I think the use of small multiples is perfect here.

The bar charts were a little more interesting to me. They really help tell the story behind the mess that is the GOP primary. The donations are scattered among several candidates, which if they were to have coalesced behind fewer candidates, Trump wouldn’t be in the lead – at least in individual donations. It would definitely be interesting to see how other sources of campaign finance stack up against individual donations. Especially with PACs – there’s a story that would definitely benefit from some data viz.

I really like the last visualization that shows the donation size for each candidate. The small multiples really help here. It allows you to see the distribution of each, which makes me think more about the story or stories that can be told just by looking at the visualization. For instance, what does it mean to have an even distribution across different tiers of donations versus seeing donations clustered at one end? I think it’s very telling of the type of voters Sanders and Clinton are getting in the primaries. My one recommendation would be to make the vertical axis # of donations rather than total amount in donations.

Anne Harding

Obviously the results alone suggest these are perfect visualizations.

The first chart of wins each year is a little tough to decipher because there’s so much overlap. It might be improved by using a bar chart, or a divergent bar chart to show wins in the positive and losses in the negative.

The total wins pie chart is pretty cool – I like that it’s interactive. I think a website with interactive tools like this would be fun for any sports rivalry.

The total points/wins by each team for each year is a little difficult to take in. It might help to see whether the total point differential corresponds to the win/loss ratio for each year. Plotting it along a timeline is hard to digest as well. I think this might be better if the lines were plotted in one graph, like you did with the first chart. You might also be able to shade each line based on whether they had more wins than losses.

For the last chart, I think a win loss ratio for each home team might also be helpful to see where the home court advantage really is. UNC has more wins at home, but there were also more games played at UNC 89 vs 76.