Crowdsourcing Graphical Perception of Time-Series Visualization on Mobile Phones
Abstract
As the ubiquitous computing with mobile smart devices equipped with powerful hardware and software has become prevalent within the last decade, the demand for visual access to data has already become part of the life of many people, which requires employing the appropriate visual representation of data. Reports show that more than half of the internet traffic is through mobile, so that it is now more than only a supplemental way of desktop computers. Among many different types of data, time-series data is one of the most common types of data, such as in many news websites, personal health tracking applications, weather forecasting applications, and finance applications. Though there already exists a large body of literature on information visualization, the unique properties of mobile devices, such as the small size of the display and various context of use, make simply applying existing visualization techniques that were meant for large displays to mobile phone displays difficult. These are challenges against fully leveraging visual access to data using mobile phones. In this study, the performance with visualization on mobile phones is investigated. For this purpose, this study compares the performance of users using the two different visualization techniques to represent a collection of time-series data in limited space: line charts and horizon graphs. Methodologically, this study employs the crowdsourcing technique using Prolific (https://www.prolific.co).
Degree
M.Sc.
Advisors
Parsons, Purdue University.
Subject Area
Health sciences
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