Loranne's Adventures in Librarying
For this semester’s Information Visualization class, we were assigned the following:
“Create a visualization that depicts the various families, factions, and allegiances in the Game of Thrones series. This visualization should go beyond being a family tree to include visual encoding(s) that will allow us to see alignments and other types of associations.”
I wanted to try to do something more computational with rather subjective data, and I kind of lucked out in that my first idea worked (for once). I created a spreadsheet, with my top 50 most memorable characters (based on only the knowledge I have from watching the two seasons of the HBO series). I later cut it down to 47, due to three of those characters (Robin Arryn, for instance) being children or less prominent
I placed the character names on both axes, and created my scale, of -5 to 5. For example, Bran Stark and Theon Greyjoy hate each other -5 ( I refuse to offer spoilers as to why), whereas Cersei and Jaime Lannister love each other +5 (do they ever!). It was interesting to have to think of such dynamic relationships this way: reduced to a number.
The end result is something like a heatmap. By creating a spreadsheet this way, I hoped to see patterns develop, and I did. There are interesting clusters where people who don’t even really interact ever (thus far) are all centered around a common friend (or enemy). Sadly, I wasn’t able to execute the interactive chart I’d initially envisioned. I’d like to be able to edit the sort criteria at any time. However, I can still do this in Excel, which is kind of fun.
Classmate and rockstar Ben Chartoff has created an interactive version of my heatmap using Processing. It’s pretty great (and neater, too).
I reviewed my data, and, rather than mirroring relationships (for example, Sansa’s row intersecting with The Hound’s column was previously set to the same value as The Hound’s row intersecting with Sansa’s column), rows now represent how the name on the row feels about the name in the column. This allows for more accurate representation of unequal relationships, where before I used an average for uneven affections.