I began this exercise by installing QGIS, which created some issues with my computer and was not the right tool for a person with no experience in mapping. I applied for both the Carto and a Tableau free student account, and began the free trial in Tableau desktop. In the end, I decided to use Tableau Public, because the interface was more simple and instructional videos were accessible to a novice like me. My original intention was to map a timeline for women’s right to vote globally, but given the difficulty in finding data (combined with my technical ignorance) I moved on to the area I am most comfortable with. I attempted to map out the timeline of women’s right to vote in Europe.

I wanted to move away from a simple timeline, which leaves out many important details and can be misleading about the real political and cultural position of women at the time. I wanted to keep the data as simple as possible, so I selected a list of countries with the dates when women were allowed to vote from womensuffrage.org. The list is not comprehensive, but it contains some detail on different forms of “voting rights.” I moved the data into a text file and attempted to find ways to incorporate details, such as the legal limitations based on education level, age, property that still prevented women from voting in many of these countries. One obvious error embedded in the data is the anachronism of listing Bosnia and Herzegovina in 1949; decades before the country existed.

I decided to leave this information in for the purposes of this assignment. Ultimately, I could not figure out how to include the additional data, and I ended up with different versions of the same timeline map I was trying to avoid. I would like to return to this project and figure out a way to incorporate the details I had to omit, such as which women could vote, or include places were women later lost the right to vote. I was thinking about Visualizing Sovereignty over the course of this experiment, not only because I faced many of the same problems, but because I think it would be interesting to map out how women’s right to vote in European powers interplays with women’s suffrage in the Caribbean. Here are a couple of visualizations from this map. 

My goal for this exercise was to build an understanding of visualization tools and the mapping process. I have wondered about the starting point for many of the visualizations we have seen in this course, especially those that require very large datasets. I felt completely lost about how to begin this project, but through the process of researching data and solving technical issues, l discovered a great volume of databases open and readily accessible online. There are tremendous governmental resources for spatial files, climate, economic, and demographic information readily available. I also found many technical resources, such as blogs with problem solving ideas, like easy ways to find latitude and longitude for a great number of data.

Map 2: NYC Poodles

I continued to explore what Tableau could do as practice, but also because I wanted to produce something lighthearted. It has been a very difficult and frightening time since RGB’s passing, and I was looking for some joy in this process. In my house a lot of happiness comes in the form of a 9 week old puppy (Kuma the sheepadoodle), so I decided to look for data on dogs in NYC. Years ago, I saw a map of the most popular dog breeds and names in NYC and I hoped to find different ways to present NYC canine data. While this process did not produce the political and interpretative challenges of the previous map, it taught me a lot about the technical issues, and reliability of data. 

I downloaded the NYC Dog Licensing Dataset from the NYC OpenData. I attempted to join a spatial file of NYC boroughs with the dog breed information, but failed every time. The troubleshooting process for this issue gave me the most in terms of technical understanding. I changed text to ‘string’, renamed and removed columns, learned to combine latitude and longitude to make a point, but there was one major issue with the dataset that was not workable: the zip codes were incorrect, or at least they were not exclusively NYC. This presents a challenge when trying to map out NYC dogs. See Map below. 

Ultimately I was able to visualize the data in a few ways. In the end I discovered that (if we’re to trust this viz) my zip code is tied for the largest number of poodle mixes in the city, although it is not the most popular breed. I could not figure out why the measure values show up this way, but the ratio of poodle mixes shown looks accurate.