SFNCS - a framework for assessment of spatio-temporal visualization methods

• Main Author: Allain, Kevin Tudal
• Format: Dissertation
• Language: English
• Published: City, University of London 2022
• Subjects: QA75 Electronic computers. Computer science; ZA4050 Electronic information resources

Movement analysis is complex due to many different factors: different forms of data, different levels of precision, strongly influenced by context, for which diverse sets of tasks require different visualizations and algorithmic approaches. There is a vast scope of previous work that researches, for diverse tasks, several approaches to visualization designs and data processing methods. The scope of tasks, potential visualization methods, and data processing that is yet to research is vast. To help reach a higher precision when describing contributions of researchers, we define a framework that characterizes information, from its recording into data to the way it is presented to the user and the terms used to communicate about it for evaluations. Within this thesis, we explain how our original research scope directed us from establishing the current state of the art for visualization methods for movement analysis while accounting for context into a characterization of visualizations, data processing methods, and communication approaches. This results in the framework that is the main contribution of our thesis. This thesis also presents several studies that refine our understanding of the impact of data complexity over diverse tasks, using precise terms. We also discuss how our system can be used to set up and analyze studies based on vague terms. Furthermore, we discuss the strength and weaknesses of existing designs for exploration tasks of contextually rich data movement, and potential design approaches to investigate in future work. These discussions include the tasks for which the designs could be most useful and how they fit within different characterizations of information and data.