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. / Graduate / 0984 / tavakkol@uvic.ca
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/5361 |
| Date | 02 May 2014 |
| Creators | Tavakkol, Sanaz |
| Contributors | Tory, Melanie |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web, http://creativecommons.org/publicdomain/zero/1.0/ |
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