This is one of, if not the, hardest digital scholarship tool I have used. It made me a little insane. It made me lose sleep. It gave me a headache. To give Flourish a little bit of credit, I am not a math girly or a data science girly, so some of this took a bit of finagling (thank you Jen). It was easy to upload the CSV file to Flourish, and it was fun to look at the different types of grafts/data visualizations that one could create, but trying to pick the right one for the data that I had from the Omaha Reservation in Iowa took some finaling (thank you Jen part 2). It took me back to freshman year of high school in Algebra 2 (when we did our brief foray into stats) and having to like scroll through the data and find the outliers, see if they were necessary pieces of information to the data set, and if we removed them what effect that would have on our data. So for this one, I ended up removing a lot of the bigger numbers (like medians, etc) that weren’t actually necessary because we had all of the other pieces of data that allowed one to extrapolate a median or some other conclusion with the data visualization as a basis. I ended up going with a donut chart breakdown after consulting with Jen because the others lacked something every time – either it was missing labels, the differences in data wasn’t clear, it was monochrome, or it didn’t communicate the data, plain and simple. I quite like the little donut charts, especially with all the colors that make it so clear what corresponds with what, and what impacts that has on the comprehensibility of the information being communicated. In terms of the data, the visualization I created displays the census data I found regarding the Omaha Reservation in Iowa in terms of age, gender, race, economics, education, and housing. I think it’s important to look at this kind of data (especially with the rise in visibility, discussion, and court cases against the Indigenous nations within the US and around the world) because it alters the perception lots of people have of Indigenous people and what they look like (in essence). People tend to have a monolithic perception of Indigenous people, that is generally damaging or harmful to Indigenous people, or remove their humanity and make them into some sort of lost vision of humanity, and it’s important to reframe that and highlight that they are real people that really do exist.
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Annemarie– your experience with Flourish (from all the highs to lows) are something that I feel every time is use this tool! I’ve used Flourish for several years, and while at face value it seems “easy” because it generates visuals for whatever data you upload, it is actually very difficult to make adjustments so that your data is represented “well” and is actually meaningful for research. Somehow, I always become frustrated when using Flourish because inevitably something breaks, something isn’t showing up, or something in the data isn’t right. As you describe from your experience, there is only so much you can do in Flourish. Often, “fixing” the problem means looking more closely at the data itself so that it can become more “readable” to software like Flourish.
I really enjoyed your thoughts about the categories themselves and how we need to critically engage their complexity and how they shape one’s holistic experience. What is so interesting about this data is the array of topics in the legend– not only does this make the donut charts colorful and interesting, but it visualizes the density of statistics. I am always a little cautious when working with quantitative data, because while it can give us a sense of reality, it can also limit our understanding of one’s real identity and their lived experience. My realization out of this week is the necessity of corroborating quantitative data with qualitative data.
Thank you for these visualizations and insights!