I worked with Tableau to produce maps visualizing World Health Organization data on literacy and fertility rates in different countries. I submitted the documentation for a student license for Tableau and was denied twice, so I worked with the 14-day trial, which did not permit saving the project. As a result, screenshots were the only way to document my work. I will contact Tableau to ask what was missing from my submission, which met all of the requirements stated on the website.
This was my first experience working with a mapping software and several realities set in very quickly as I began experimenting with the different programs. My choices were narrowed down by practical constraints. ArcGis Desktop required a PC, QGIS would not run on my computer because it could not confirm it was not Malware, and Leaflet requires programming knowledge. I did watch several videos on Python and begin the process of moving past skepticism about my ability to learn a programming language. I ultimately chose Tableau because:
(1) It was the one I could open on the computer I have available right now.
(2) It appeared user-friendly at first walk-through.
(3) Video tutorials were readily available in the absence of someone to help directly.
My experimentation began with attempting to work with data from the NYC Department of Education and the National Center for Education Statistics. I was interested in seeing what I could visually map in terms of relationships between “school quality” (a problematic term) and other data points such as attendance/truancy, state testing, and a variety of other categories. I was driven by a question I cared about but quickly recognized that it may not be best suited for a mapping project. This served as a reminder of the importance of asking whether the capacities of the tool and the question being asked really elevate one another. At this stage, I also do not have the knowledge of Tableau to figure out how to build maps specifically of NYC. Moreover, the importance of really knowing your data set in order to understand how to maximize the potential of the mapping software was very clear. Finally, as we have discussed in many of our readings problematizing the apparent objectivity or fixed nature of data, I sensed that mapping and visualizing the data somehow solidified the “truth” implied in their categories. In fact, every category of data, from the racial and ethnic terms used, to what defines school quality requires interrogation. Visualizing them as they stand produced enormous discomfort.
Typically, through experimentation and simple use, I am able to become proficient in most new systems or programs I encounter. With Tableau, it was quickly apparent that focused attention to the tutorials – and attending some of the workshops being offered – would be critical to actually learning how to use the tools well and understand their power. Time, focus, and specialized knowledge are all required, as is seeking out those who have more experience before returning to the program myself to learn through experimentation once again.
After accepting that I would not be figuring out how to map NYC anytime soon, I stumbled upon the world mapping feature in Tableau.
My mind immediately went to our reading on how maps can lie. A basic unease with the form of the map presented in Tableau ran through my entire time with the program. I did not investigate whether there is an option to visualize the data on a different form of the world map, but as I write this reflection, I realize that this is something I need to investigate. I experimented with the formatting tools – size, color, outline, and found this useful, though not the most relevant to spend a lot of time on at this stage in my own learning of the program.
Next, I thought about where I could pull data for all of the countries of the world. This brought me to the World Health Organization and statistics on many things, but the two I was most interested in were fertility rates and literacy rates. I know that this is a topic with vast fields of research on it, but I wanted to see if I could visualize relationships between them here. I did not manage to put both on the map, but I did find the ability to produce what I would describe as a spreadsheet with visuals. And from the overlay of categories, I got a glimpse into how you can start to analyze more complex relationships between different data points and I now know what types of “how-to” questions I would ask in a more specialized workshop setting on Tableau.
Other lessons I learned included that to do this work at a high level, I would need to pay attention to many very practical factors which may not matter as much in other fields. First, in order to spend long periods with the program, a physically comfortable workspace would be key – a mouse, desk, and a larger screen (possibly even two screens). My computer also labored with downloading and opening big data sets. The cumulative time lost doing this regularly would be enormous. My knowledge of Excel is also decent but extends exactly as far as my work demands have required me to bring it – in short, I have learned everything I needed to when I needed to. For the purposes of maximizing DH programs as a “tool of inquiry,” building out my knowledge of Excel is probably a very basic requirement.
One of the most profound lessons I learned through this project was that some of the most interesting questions are those I did not have at the outset of my inquiry. In this way, the generative power of these tools is remarkable and inspires continued curiosity as you work. The act of building produced new, more interesting questions to investigate. I can also imagine how this would make it easy to get distracted or pulled in many different directions and lose the original thread worth pursuing. An awareness of this duality may be important.
Lastly, a major lesson I have failed to learn many times over in my life: save often. Absolutely everything I do at work autosaves, so this is a very simple habit I do not have right now. Tableau froze at least twice, and I had to force quit both. In part because I was immersed in what I was doing, I had not taken a screenshot. This meant I started over several times. On the other hand, from an experiential perspective, it was useful to attempt to recreate what I had just done – and see whether I could or not. Still, save often!