My Quantified Self
Collecting data on myself for a week.
Create a visualization based on the synthesis of data collection regarding your weekly routine. The visualization must address gestalt, hierarchy, use of a grid, and typography to convey proper information design and architecture.
Where do I go and what do I search? This is the question that guided me when thinking about how to collect my dataset for this project. I thus explored my movement throughout the city of Savannah, GA over the course of 8 days. I wanted to understand if what I search is influenced by my location.
I used Google Maps on my phone to help me gather data, since my phone moves with me. The beauty of Google Maps is that if you allow it to track you, it will. I have been fascinated with the timeline feature ever since I learned about it a couple of years ago. I wanted to get an understanding of where I move, why I move, and what my motivations for moving may be.
For the poster, I wanted to combine the abstract and the literal. I vectorized the exact map locations of where I went all week, even if I searched nothing on my mobile while there. Searches are visualized by circles, with size corresponding to the amount of time I searched for a particular topic. I wanted to see which place outside of my house I visited the most. I wanted these locations to be the backbone of my searches, even if some searches were not born from the location. Does my environment influence my thoughts...how so? While 8 days of tracking has largely told me that I want food or ask “how to” questions, I wonder what an annual report of this type of tracking would reveal, and what other patterns I could discover about myself. Safari actually does not offer a timestamp for searches, so I had to be extra vigilant of the times I searched to correlate it with my location.
When looking around for examples of the way designers and researchers have synthesized and visualized raw data, I could not ignore the way Nicholas Feltron designed his day to day movement and interactions with such clarity, minimalism, and color for his annual Feltron Reports. The Feltron Reports captivated me. So much information, so beautifully synthesized.
Inspired by the way Feltron has visualized his movement among New York City through many forms of transportation, I looked at the variety of ways he showed his movement, and how each canvas tells its own story in its own style. From use of diagonals to color to shape, I immediately understood who Nicholas Feltron may be and why where he goes is a large part of that.
Ideation & Process
When thinking about the layout for my visualization, I wanted to portray movement between locations. I also wanted to create texture for the background that involves movement, so I ran over a piece of paper with my car. This tire texture can faintly be seen on my visualization. These sketches show my thought process surrounding my data visualization.
I was able to organize and cluster each of my Safari mobile searches from wherever I was into 10 categories.
I then assigned each of these places a bright color. A place’s color and circle represents what was searched around the location I was at. I searched for things on my phone through Safari 80 times over the course of 8 days.
What I Learned
While I like to think I “live in the moment,” and am conscious of every decision I make or every word I say, this project has taught me that when it comes to my interactions with my phone, many of my decisions are quick. I search things without even consciously realizing I do so. I open and close applications all the time, forgetting what I was going to look up in the first place, only to get distracted by another notification. I couldn’t believe that only 8 days told me so much...I’m so motivated by food! And I also shouldn’t search for it while driving.
Tracking myself also taught me about my specific movement patterns during a specific amount of time. Recording my searches allowed me to link what I was thinking with my location. Visualizing this unique dataset about myself resulted in a still shot of a small part of my life for 8 days. This first hand experience of tracking my own behavior unveiled the impact machine learning has for us, as this technology can easily gather large datasets faster and more accurately over time than manual collection.