project 4 — data visualization
Early on in this period of isolation, I became much more aware of the weather’s impacts on my mental space. With a goal to visualize weather’s effects on my life during the month of March, I recorded my wake-up time and sleeping time, as well as perceived mood for each day. Although mood and productivity are definitely correlated with weather and sky coverage, it must be noted that there may have been a variety of extraneous factors such as long weekends, deadlines, and simple break days. And also I am just an erratic sleeper.
From here, I developed a few elementary sketches, many of which did not work as I had hoped:
In some of these preliminary ideas, I had overlaid some information on the same axes, which proved to be sometimes confusing.
Another example of overlaying wakeup time and temperature, but this time with the axis for mood inverted to show the correlation more spatially.
Out of my first few ideas, this one was the most effective in communicating both weather and time. However, this visualization was missing the data on mood. The straight bold lines denoting individual days also seemed a bit visually jarring.
I decided to expand on the most effective iteration, adding additional weeks of data on the side to represent the whole month of March. From feedback I received, I realized that the color palette was less effective in communicating the weatherspace in Pittsburgh. The arcs I had created also does not really add too much to the visual.
From here, I tried to draw from the individual qualities which worked the most effectively. With the more grayscale color palette and small accents to denote both weather and mood, the visual becomes much easier on the eyes while expressing everything I sought to. Because of the better contrast in tones in this visualization, I feel like it is easier to understand how my quality of sleep and mood (represented with branches) vary depending on the day’s weather.
I think if I were to do this data visualization again, I would try to record more interesting or ground-breaking data, as I felt like the data set I used was quite boring and simple. Besides that, I am satisfied with the outcome.
