Personal Analytical Calendar




Tavakkol, Sanaz

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Data is all around us, everywhere we go and in every activity we do. It exists in all aspects of our everyday personal life. Making sense of these personal daily data, which leads to more self-awareness is becoming remarkably important as we can learn more about our habits and behavior and therefore we can reflect upon this extended self-knowledge. Particularly, these data can assist people to learn more about themselves, uncover existing patterns in their behaviors or habits and help them to take action towards newly developed goals. Accordingly, they can either try to improve their behaviors to gain better results and trends or to maintain existing ones. Through the interviews that I conducted, I learned that “Productivity” is one of the most important personal attributes that people are very interested to monitor, track and improve in their daily lives. People are interested to learn more about the supportive or preventive causes that effect their daily productivity, which eventually can help them to improve their time-management and self-management. In this thesis, I focus on two research questions: (1) How can we design a visualization tool to help people be more engaged in understanding their daily productivity? In order for people to learn more about themselves, they need context about their living habits and activities. So I chose digital calendars as a platform to integrate productivity related information as they provide beneficial contextual information, supporting many of the questions that people ask themselves about their personal data. As the next step, I had to find an effective way of representing influential factors on productivity on the calendar. This led to define my second research question: (2) What combination of visual encodings will enable people to most easily identify a relationship between two different pieces of daily information rendered on a calendar? For finding the best visual encoding, I considered encoding Numeric data using Saturation and Length encodings, and Nominal data using Shape encoding. I designed two types of questions: Calendar related questions, to investigate the interference level of visualizations in calendar related tasks, and Visualization related questions to identify which visualization is faster and leads to more accurate results and better user ratings. I compared the combination of Numeric x Numeric (Saturation x Saturation, Saturation x Length, Length x Length) and Numeric x Nominal (Shape x Length, Shape x Saturation) data encodings. My results demonstrated the following: for Calendar Task questions and in Numeric x Numeric category, Length x Length had the overall best results. For the same task set and in Numeric x Nominal category, Shape x Length was rated the best. For Visualization Task questions and in Numeric x Numeric category, Saturation x Saturation had the better performance overall in most of the cases and for same task set and in Numeric x Nominal category, Shape x Saturation was the fastest while Shape x Length was the most accurate. These findings along with interviews provided me with useful information for refining the visualization designs to more accurate, more user-friendly and faster visualizations which assist people in monitoring goals, trends, status, contexts, influencing factors and differences in their productivity related personal daily data and brings them more insight awareness and possibly self-reflection.



analytics, personal analytics, PVA, visualization, calendar