One of our first events for Love Data Month this year was a pair of hands-on data visualization activities: The Art of Every Day Data and the Mobile MapRoom. I can’t speak to the details of the MapRoom beyond “it is awesome and Girmaye Misgna does cool stuff with it” but I will talk about The Art of Every Day Data and what the goal was for this activity.

The idea for The Art of Every Day Data came from the very cool Dear Data Project. In Dear Data, two friends, one in England, the other in the US, collected data about their lives for a week (things like How Many Times Did I Look at a Clock? or How Many Times Did I Say Thank You?) and then make hand-drawn visualizations of the data to send on postcards to each other.

I fell in love with this project because it demonstrates that working with data, even making data visualizations, doesn’t have to be a computationally difficult task. Anyone can collect data, about anything, and create a beautiful interpretation of it. This is what I wanted to bring to Penn Libraries and Love Data Month with this event.

The Dear Data Project was published as book in 2016. The book not only includes each postcard from the project, but also some guidance on how to do these things. The steps they list are:

  • Begin with a question
  • Gather the data
  • Spend time with the data
  • Organize and categorize
  • Find the main story
  • Sketch and experiment with first ideas
  • Draw the final picture
  • Draw the legend

To give attendees of AoEDD a chance to actually experiment with making these hand-drawn visualizations, I’d need a dataset for them to work with, doing the first few steps for them. I also wanted to create some examples to give them ideas so I would, in fact, be going through each step myself.

I thought long and hard about what kind of data to collect. Sitting close to our office kitchenette gave me the idea to put up a sheet on our cabinets and ask my colleagues to track what they went into the kitchen for. My colleagues are wonderful sports and did a great job tracking why they were in the kitchen for a week. Here is our final collection sheet:

Data collection sheet with tallies of many colors titled "Why are you in the kitchen?"
Collected data. Yup, our copier is in the kitchen.

At the end of the week, I took the data sheet and added up all our data into a more easy to read format:

Data sheet with total numbers for each day of the week for the categories in previous image
Transcribed data

I took this data and spent a long time with it. I noticed things like the consistency in the number of times the microwave was used in a day, how many dishes we wash (a LOT!), and how many people besides me used our office tea. I was surprised how such a mundane-seeming dataset could have so many stories to tell. I thought a lot on what I wanted to show about this data. I landed on wanting to make a visualization to show how our use of the kitchen changed throughout the week.

I looked at the Dear Data book to get ideas about how I might represent our data and tried a lot of different things before finding something that seemed to work.

A test visualization
A test data visualization

The final visualization I ended up with is here:

hand drawn data visualization of the data collected in previous images
Final visualization

Just looking at the visualization doesn’t tell you too much, but with the legend (below) it becomes clearer. The data goes left to right, Monday to Friday. Each arm on the stars represents one instance of an activity. The colors represent a category of activity and if there’s a shape on the end of the arm, it’s giving more specific information about the instance.

My favorite thing in the dataset is the explosion of kitchen visits for food on Friday. As we collected the data, we noticed that we hadn’t had many snacks brought in for everyone throughout the week and on Friday five of us brought food to share. Did I mention how great my colleagues are?

legend for the visualization
The legend for understanding the visualization

I learned a lot going through these steps with the data and made me think in new ways about data throughout the process. While our activity didn’t get many participants, I think this is a useful way to get more people excited about data. There is all kinds of data to collect and many ways to interpret data to tell our stories.

 

 

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