A personal data visualization project mapping ten days of gratitude, kindness, and mood by time and location.
This project set out to transform ten days of personal data into a meaningful visual representation — tracking gratitude, acts of kindness, and mood to find patterns most people never think to look for in their own daily routines.
I logged my data through a mobile Google Sheet each night for ten days, recording up to three moments of gratitude, up to three acts of kindness toward others or myself, and a mood rating for each. Missed entries were filled in the next morning to avoid losing accuracy. Once collected, I used Miro's AI features to categorize the data, then cleaned it further in Excel — renaming a confusing "motivational level" field to "mood" after realizing it needed to be logged in the moment, not at the end of the day.
Early sketches tried to show every dimension of the data in a single PDF, and none of them worked — the first vectorized version technically showed everything but looked nothing like what I wanted.
After talking with my professor and getting group critique, I realized I was trying to show too much at once and that visualizing location wasn't serving the point I actually wanted to make. I shifted focus from location to time, mapping all 24 hours and building a binary graphic showing when each activity happened most, then reworked the symbol system to match.
The final graphic maps gratitude, kindness, and mood across a 24-hour cycle, color-coded by location (campus, home, and two workplaces), with a simple symbol system: a sun for gratitude, a cross for self-directed kindness, and two linked circles for kindness shown to others. A separate location-inclusive version was explored but not submitted due to time constraints.
This was a self-directed data set, not a broader study — the patterns reflect one person's repetitive school and work schedule rather than generalizable behavior. I originally hoped to make the visualization interactive and may revisit that idea later.