Blog: Using Personal Informatics to Create Information Visualization and in Motivating Behavior Change
Objective: Using Personal Informatics to Create Information Visualization and in Motivating Behavior Change
I have collected my food intake through an iPhone application from Feb 10 – 28, 2011, which accounts for three full weeks. This was done through an iPhone application called “dietSNAPS,” which lets users to take pictures of every meal that they ate.
I delivered entry type, time-stamp, frequencies, and description in my visualization. In addition, actual photos of the meal will be used to be more visually-engaging and to be more aesthetically interesting to look at as well. Based on Yau and Schneider (2009) article “Self-Surveillance,” they have introduced an important concept of how collecting personal data about one’s behavior can help create a sense of awareness and motivation for behavior change (24). However, a question that comes to mind is the issue of the visualization of data. As noted, different forms of data presentation can differ in how information is highlighted. Moreover, artistic approaches have been examined in representing visualizations, most notably due to the emotional value that art and design can bring. As further exemplified in Holmes’ (2007) work on eco-visualization in the effort to reduce energy consumption; she has provided an excellent example of how visualizations can effectively convey and raise awareness.
Based on the understanding of how visualizations can reveal unexpected patterns, I have consistently tracked three weeks of data on my food intake. The time frame of the data collection was from February 10 to the 28, 2011. A total of 85 entries were recorded throughout the 21 days, of which 71 entries have corresponding pictures. I have recorded this data with the help of an iPhone application called “dietSNAPS,” which lets users to take pictures of every meal they have eaten. Each log is further documented with information on entry type (breakfast, lunch, dinner, snacks and drinks), a time stamp, and a short text-field for users to input any descriptive text that describes the food. I have further export this data via E-mail to create my visualization.
The visualization that I constructed incorporates the temporal dimension (time of the day) on the x-axis, with the frequencies on the y-axis. Each data point is an actual photograph of the food I ate, with a corresponding border color that indicates the meal type. I also experimented with other approaches. One method that I tried was to plot actual dates on the right-axis. However, after a pilot version of this visualization, I discovered that this information was not intuitive to read. Although this set of information could have provided for a better understanding as to how eating patterns could have changed from each day; this visualization will be more effective in capturing a larger data set. The biggest finding that I found was that I do not have a very consistent food intake throughout the day. Amongst the 21 days, I had only eaten five breakfasts and 8 lunches. As a result, snacking takes place throughout the day from the mid-mornings to mid-night. Therefore, I would eat huge dinners to compensate for the lack of caloric intake in the day. Moreover, my dinner tend to spam later in the evening from 8 to 11pm. Another interesting finding was that I only drink caffeine-contained liquids, such as coffee, coke and tea.
In sum, the design of this visualization allows people to track entry types and frequencies in relationship to time. Although pictures provided for an interesting presentation manner, the size of the picture will restrict the actual processing of information. Therefore, a limitation of this design is the lack of descriptive texts that illustrate the type of food that is eaten. This is compensated by a tag cloud of all of the description that was entered previously in “dietSNAPS.” This design-based visualization can help convey other types of information that a system-based visualization cannot, such as the emotional value of a picture in comparison with numbers or data points on a data.
1. Yau, N., & Schneider, J. (2009). Self-Surveillance. Bulletin of the American Society for Information Science and Technology, 35 (5), 24 – 30.
2. Homes, T.G.(2007). Eco-visualization: combining art and technology to reduce energy consumption. In Proceedings of the 6 ACM SIGCHI conference on Creativity & Cognition (p. 162).