View the online visualization here.
Design Statement
Data: I wanted to explore my sleeping patterns over this semester and so I decided to track that alongside with my structured obligations (lecture hours and assorted work obligations: team meetings for web development, budget/all staff/production hours for newspaper, all-staff weekly meetings and office hours for mentoring). I tracked hours slept and hours in structured obligations to see if there was any relationship between the amount I worked/was in class, and the amount I slept.
I track these structured hours pretty religiously in my Google Calendars, and so it was easy to trace back those work hours, though it doesn’t count additional free hours I might have had to work on editing an article, a story might have had to cover, time mentoring students outside of office hours, time I spent working on homework. I kept track of my sleep hours in my paper planner since I used it anyway for more of my assignment tracking.
I initially intended on tracking mood and social media usage as well, but that proved trickier to do as I wasn’t sure how to quantify some pretty open-ended variables (I found my mood would fluctuate pretty often within a day, other days, I didn’t feel like what I was feeling fit on a linear scale of happy/sad, so I decided to scrap the variable altogether.
The Visualizations: I decided to use a bar graph layered with a line graph to juxtapose two pieces of data I had collected over the 70 day interval, because I found side-by-side bars to be too clunky and busy for 70 days of data. I stuck to color blind-friendly colors blue and grey to make my visualizations easier on the eyes, and to also make it simple to view and understand.
The “far-view” charts were emphasized through the hierarchy of my design; I made the 70-day charts twice as large as the more granular “near-view” charts dealing with monthly and/or weekly data. On the 70-day chart of the static graphic I created, I also marked the different months with a thin line to allow viewers to better understand the monthly average graphic in the context of the larger 70-day graphic, and hopefully more intuitively see where the weekly average came from as well.
The long-term data was the most interesting to me. Seeing how busy this semester was for me and how late I would be going home on a weekly basis (11pm/midnight at least 3 times a week), I only had a general sense of how much time I slept relative to the other days of the week. I noticed there were nights in between really busy days (Tu/W), yet I’d sleep 10+ hours, likely from a combination of exhaustion and eventual stress. What was interesting was the subtle increase and decreases in hours slept on a week to week basis, probably from a combination of burnout and pending exams —- which goes to show how difficult it is for myself (and I’m sure for many other people) to consistently be sleep deprived.
Insights: I learned about the relationship between sleep and work for myself; though I suspected it, I was able to see just how much I worked a week as well as the distribution of the hours I worked a week, and how that affected the respective distribution of sleep over a week. I broke down my data of 70 days into even more specific views of days of week/ days of month/month.
I found that I slept the least and was the busiest Tuesdays, Wednesdays and Sundays when I worked production nights, and surprisingly, that when looking at monthly averages, I tended to sleep less (lows were lower) during the beginnings and ends of months, and sleep more (highs were higher) in the middles of the month. This could have to do with the weeks leading up to my exams, letting myself rest the week after, then progressively sleeping less and less for the next set of exams.
In the future, I would try to keep track of multi-dimensional mood data and have a more structured approach to collecting social media browsing activity — I found that I didn’t post enough on social media to use it as a metric of social media activity, and I would have instead had a tracking app on my phone to keep track of how much time I spend simply browsing.
Questions I’m left wanting to further explore is how I cope with stress, and seeing how my productivity is affected (looking exclusively at how often I turn in things late).