Chakiera Shields

I am little torn between what exactly I want to do for my midterm presentation, but as for now I would like to create a visualization that displays crime rates in various cities across the country. I thought this topic was very interesting because I come from an area with a pretty high crime rate (Baltimore) and always wondered how these statistics compared to other cities, bigger and smaller, in our country. I always knew that Baltimore had a very high crime rate, especially in proportion to the population of the city, but I think doing a visualization like this could bring awareness to people on how crime rates compare in different areas.

I would like to do so by dividing up tasks in a group where someone could create a map that darkens areas in the America where crime rates are higher and lighter colors where the rates are lower, another person could create a map that shows the population vs crime rates of an area and someone else could create a map that breaks down crimes the different types of crimes that occur in each state with the data given. I would like to create a visualization with background information that says how different factors such as poverty, amount of young people in the area, climate, etc., can contribute to an area's crime rate. All of this can be done in infogr.am or tableau.

Lingjie's Comment:

I think they did a great job explaining the whole process of they work as they clearly followed the CUT-DDV structure. Step by step they showed us the background information of what they want to analyze, and focused on one precise purpose of presenting match-up comparisons between the big four teams. I think it particularly helpful that they decided to present the scope of project first, then presented the proposals they considered to use, explained the weakness of these drafts, and after all these, they gave the final version of proposal which they thought to best express the information. This way of presenting effectively helped me understand their thought step by step.

As for the visualization, the colors they applied was harmonious, and using logos to represent each team is truly a good idea. And I think the second proposal is not that confusing, as in the 6 graphs, each team is represented by one single color, which might make the graph more readable.

As I’m quite unfamiliar with the matches, I’m a little confused about the wins/losses of each pair of teams, since it seems that only one team’s data is shown in one year. Is the y-axis the net number of wins/losses? I’m not sure if I understand it.

Generally, Chakeria and Cheng did a great job and I like the way they choose to present both the game data and how they achieved their goals.

Lance Stokes Review:

Chakiera and Cheng, I really enjoyed your presentation because Premier League football has long been a mystery to me, and your presentation helped me to get a better handle on it. You did a very nice job, especially given all the permutations of team matchup combinations that you had to deal with. I liked that you used the logo of each team in the visualization.

I also enjoyed seeing the creative iterations that you tried to optimize the project. I, too, would've tried a line graph first, but it really isn't best for this application. I liked the multiple approach of the bar graphs, and, as Dr. H mentioned, orienting them rotated 90 degrees (so that the wins/losses would be horizontal) would've helped tell the story better- losses to the left, wins to the right.

Personally, I liked the more colorful version with the bar color keyed to the team color. It is much easier to find a particular team as you scan the matchup array. And the colorfulness is in keeping with the spirited nature of the game.

As a neophyte, I would've enjoyed seeing a further distillation of your data- so what team had a higher winning percentage over the span of time? I'm not sure what metric you would use, but what team would be (arguably, of course!) the best team of the last ten years. That would make your visualization more valuable to hard core fans too, I'd think.

But overall- great job! I learned a lot from your project, and the visual aspects made it much more informative than mere statistics would have. Thanks!