Visualizing Textual and Qualitative Data
November 2021
In this third exercise of the Visualize series, we were tasked with creating a data self portrait aka a visualization that presents insights from a dataset about myself. We were allowed to use pre-existing data (email archive, fitness tracker data, etc.) or collect a new dataset about an aspect of your life. This exercise felt very similar to one of my personal project, Our Year in Movies 2020, where I was inspired by the Dear Data project and Giorgia Lupi’s stylistic approach to data humanism.
Type: Static data visualization
Tools: Excel, colormind.io, Figma
The Process
Over the last few years, I’ve been using Letterboxd to track every movie and special TV series that I watch. I thought this would be really interesting to look at my 2021 watching history for this qualitative exercise. In my sketch, I listed out four variables to potentially look at: movie genre, date watched, personal rating, and the color palette of the movie poster that Letterboxd uses from IMDB. At first, I was inspired from Dear Data and sketched out a visualization that could use all four variables where colors could represent the palette and genre, lines and/or dots could represent my personal rating, and the way each data point was organized could be in chronological order.
I began prototyping in Figma. I created each movie’s color palette using colormind.io, organized them in chronological order from left to right. and top to bottom, then gave each movie its border color based on the primary genre in IMDB. When I started adding my personal rating to each movie, I realized that it didn’t provide any more additional insight than what was already visualized. With Ashley’s very helpful feedback :), since there was already a rainbow of colors, I decided to try using different brush strokes as a guide to visualize the different genres of each movie. I also thought it would be interesting to bring in my personal ratings by changing the height of each movie’s palette bar. What you’re seeing is the final product after removing my personal rating, which I believe gives a rich visual tapestry into my movie/TV watching habits.
What I Learned
This might have been the hardest exercise in the Visualize series for me. My goal with the Our Year in Movies project is to make it an annual challenge similar to the Feltron Annual Reports. To be quite honest, I’m not a huge fan of how I visualized the genre with brushstrokes and I feel like it’s visually very busy with the color palette bars being so close together. I feel like with more time and thought, this could improve even more and the color palette would be the main focus without too many distractions or the data points would be organized by genre. It might even have the beginnings of a data art piece where the x- and y-coordinates aren’t visible in the piece itself. Maybe something to consider for Our Year in Movies 2021 🤔