Dear Data is a project by two information designers, Giorgia Lupi and Stefanie Posavec, involving the collection of data from their daily lives on postcards and mailing them to one another. On the front of each postcard is their chosen data visualization, and the back features a key detailing how to read the data. Much like popular mood and habit trackers, the postcards feature unique visualizations. These visualizations are comprised of custom symbols, color variation, shape, thickness and length of lines, and the use of left and right, all defined by the designer behind the data.
To kick off my own Dear Data adventure, I started by identifying a topic and supporting subquestions to guide my data collection:
- What are my eating habits?
- What have I eaten? (category: snack, fruits, meal, etc.)
- What did I eat? (specifics)
- When did I eat? (date, time of day, etc.)
- What was I doing while eating? (working, watching a show, at dinner, etc.)
- What were the portions? (mini, small, mid, large, surplus, etc.)
- Was it home-cooked? If so, who cooked?
- Was it fast food or carry-out? If so, from where? How much did it cost?
- Did I visit a restaurant? If so, what restaurant? With whom? How much?
- Why did I eat? (hungry, craving, bored, snacking, offered to me, just because, etc.)
- What was my hunger level? (low, average, high, starving)
- Was it satisfying? (satisfied, neutral, dissatisfied)
Next, I created a simple table in my sketchbook to track everything I ate for an entire week. My subquestions became each column within the table, and the rows were the meals, organized by date and time.
As I collected, I started noticing trends within the data. First off, the week in which I tracked my eating habits was a peculiar week to do so. It was an extremely busy week that consisted of completing design work all day, having late-night work sessions, attending multiple meetings and class sessions, and commuting for various appointments and errands. The main trend I noticed was that I ate out every single weekday. My reasons for purchasing food instead of eating at home or cooking were primarily out of convenience, and to save time. I also think eating things I knew I would be satisfied with offered a sense of comfort and peace of mind in contrast with the chaos of my work week. Another trend I noticed– I tend to purchase decent portions of food and span my consumption of those meals across a couple of days. I also do not have a routine for my meals; I just squeeze in food where I can and often wait until I’m hungry (with an average or high hunger level) to eat.
Once all of my data was collected for the week, I began exploring possible ways to refine and visualize all my findings. I started by reviewing each column and deciding what was most important to display in my final visualization. I eliminated insignificant columns and details to simplify things, including dish name, fast food/carryout, restaurant, and why I ate. Some of this information resembled other existing information closely, while other information was irrelevant to the story I wanted to focus on. This left me with simplified versions of the columns that would eventually define my key: the day, time of day, level of activity, portion size, hunger level, satisfaction level, meal category, the reason for eating, and whether or not the food was home-cooked.
Here’s what I ended up with:
Overall, I learned a lot about myself through this exercise. For starters, the majority of the times that I ate in one week, I was trying to multitask and accomplish something else while eating. The frequency at which I ate each day was inconsistent from day to day because I squeezed in meals where I could. I also only ate when I had an average or high hunger level. Finally, 1/9 of the times I ate was a home-cooked meal– and I was not the chef!
Sola's Thoughts 