I started work on my final project for Professor Cairo’s Intro to Visualization course. My topic: HIV. This was based on a story I read in The New York Times about how HIV has become most prevalent in the South, in rural areas, and among gay, male, people of color. I wanted to explore how HIV has shifted over the years.
Above are my most recent drafts and I think I finally have a direction I feel confident about. It’s quite astonishing the number of details and polish required before a visualization feels complete especially in print.
What I’m working on to refine:
- Color choices. I think one of the greatest challenges is creating a color palette that is attractive and is colorblind safe. This is harder than it may seem.
- Copy. Writing never comes easy to me, so this is going to take more time than anything else.
- Typography. This is a particular favorite of mine. I love type and tweaking its use… well, I might need to cut myself off. I use Suitcase to manage my type library so I’ve created a folder just for typefaces that work well for data visualizations.
Professor Cairo mentioned how 80 percent of visualization is understanding the data. This project is proof that statement is true.
The Excel workbook above is just one of many I created and combed through to understand what the numbers show. In my case, I needed to visualize it because with a table this large it is difficult to see or compare much of anything.
My initial plan was to show the shift of HIV in the U.S. over 20 years. But after downloading 20 years of PDFs and using Tabula to extract the data, I discovered that in 2007, a change in how the data was reported presented me with anomalies and a decision; actually a question. What do I do? In sketching the numbers with Flourish, there was clearly a dip that without a note people could interpret incorrectly. In fact, I wasn’t sure what to make of it.
There were two options, according to Professor Cairo:
- Annotate 2007 with a note about the change in reported data
- Visualize only last decade.
I chose the latter because after reading through the technical notes, 2008 was when all states had enough data and it could be standardized. What is shocking to me is that data about HIV wasn’t standardized until 2008!
Above is a sketch of visualizing HIV diagnosis in nearly every state. Its more than our project brief required because there isn’t room for a grid of mini line charts but once I started to see how each state compared to each other and the national rate between 2008 and 2017, I couldn’t stop. The group of this mini line charts is visually interesting. I plan to organize each one regionally and in the future, I want to explore further iterations.