This week, I encountered difficulties with my “Highest Earners of Each Nation” chart. I was struggling to figure out how I wanted to visualize the pence field, but I knew I still wanted to focus on how Fonda paid each trader and how any discrepancies could possibly reflect a power dynamic between traders of different nations, or between native and non-native traders. After messing around with it for awhile, I decided to focus on the maximum number of pence paid to each trader and visualize this by bars representing the individual trader who received the most pence during any given visit. I like this better because instead of looking at the sum of all pence earned for the top individuals (which was the chart’s previous purpose), I can now look at the the top three earners for different nations based on single events. This way, the chart is not so heavily skewed towards the Mohawk, and men are not solely represented here, which I found interesting. Men may have been the top traders for their nation overall, but some women received higher payments than men in specific exchanges, which could allude to other factors mentioned in some of my secondary sources.
I focused on my visualizations more by creating a formatting scheme and style guide. The two biggest changes I made were eliminating grid lines (that extra ink is super unnecessary) and changing the fonts. I went with Lucida Calligraphy (black) for the title fonts to reflect the cursive handwriting of my primary sources. I chose to stick to the Purple-Pink-Gray color palette for all charts, since purple is the color of the Haudenosaunee. I assigned the gray colors to represent non-native traders. I was thinking of using orange to represent non-native traders, since orange is commonly used to indicate Dutch, but that would be misleading because it would suggest that all Anglo-American traders featured in these datasets were all Dutch, which is unlikely. For the treemap, I could only use the purple palette that ascends in lightness, but it goes well with the other palette.
Besides deciding on the look, I also worked on matching up the field names and aliases this week. For example, I adjusted the “Tribe” field to “Native” so that it this category was consistent across all charts. I had to change some aliases to be a bit more clearer in the visualization, like the trader David Hoarse (son of David). It originally said “Davids son David Hoarse” but I changed it in OpenRefine. Some of the titles had to be revised as well to sound more concise and flow better. I also went back and cleaned up the “Unidentified Dutch Trader” Item field. I was able to narrow the field down to just 34 choices from over a 100, so now the pattern I am trying to highlight in the frequency of trade items included with beaver furs is much clearer. Lastly, I finished my secondary research so I could finalize my text for my StoryMap. While this is still a draft and by no means a finished product, I feel much better having a concise beginning/end and fully laid out argument.
My remaining to-do items are as follows: 1) build more interactions into each visualization, 2) decide on final maps and images to add to the StoryMap and crop/edit them as needed, and 3) revise StoryMap text to thoroughly outline my argument. I think my argument still needs a lot of work, so I’m going to focus on what exactly I want to say, how I want to say it, and why I think it’s important. This is where my secondary sources come in. I also realize that my visualizations are kind of boring and not very interactive at all. That is another focus of mine for the next few days – to rewatch the Tableau videos and add more features. I’m thinking of adding another filter somewhere (like the Year slider I already have), highlights, and annotations to make my argument really pop out.
Here is the current status of each visualization: