In part 1 of the History Moves project I focused on using Voyant to visualize data patterns. Somewhat buried in my proposal, I raised the idea of customizing a Voyant corpus, where each document would focus on the same turning point in these women’s lives. I continued to think about whether there were ways to capture individual turning points for the current History Moves visualization project. My ostensible and admittedly quixotic and naive idea for this project was to see if focusing on turning points could in some way become an intervention tool to help other young women in similar situations. If we could home in on these important turning points could we learn some worthy social intelligence that could be applied in the real world?
In keeping with the interactive approach to this data, the women themselves would discuss and highlight turning points in their lives — either in the transcripts or in new interviews.
The research and visualizations in the History Moves Visualization Resources housed in the March 21 introduction to data and visualizations does a good job at mapping out important events in these women’s lives; where they spent time; and what they were thinking at the time. In particular, I thought the Brooklyn map effectively couples key quotes from the transcripts with various geospatial connections. It seemed a challenge to come up with a way that would illuminate and contextualize turning point data.
Pairing ideas with the correct research tool is a challenge.
I had the notion that if I learned more about Carto I’d be able to find new ways to show something about turning points. I sought out Andrew Battista, who echoed the guidance oft-heard in class: that the decision about what tool to use is secondary to what you are trying to investigate and visualize. Among other things, paraphrasing: Before you choose a tool, figure out what you want to do; create a consistent convention to geolocate data and create a sound ontology that could be applied to the data. Develop a methodology that transfers that data into geolocatable units of analysis. Andrew kindly showed me several tools, including Palladio and Story Maps. I decided to give Palladio a go. I studied the tool for a couple of days and also read through these three good tutorials:
While the geolocations and the places are true to each woman, the age of turning points and the connections between turning points and places are completely of my own making, loosely and subjectively tied to their stories.
Here’s the spreadsheet that I used for the data
The first batch of data is the attribute data:
Name of woman
Place Type (Turning Point)
Place Name (Turning Point)
Age of Turning Point
How the women said they contracted HIV
Second batch of data:
Places and Locations
After downloading the data into Palladio. On the left are the attributes and on the right are the related locations.
Using the graph function in Palladio, I connected the women and the “location” where they were at the time of the “turning point.”
Below is a geospatial map of the actual places, with one of the schools showing in a hover.
Admittedly taking baby steps, I’ve rendered a couple rudimentary visualizations. But I’d like to continue experimenting with Palladio to see what other mapping and deeper connections I could draw out.