Visualization Design and Evaluation Studies Across Application Domains: Applying and Enhancing Design Study Methodologies through Reflection on Collaborator Involvement and Domain Characteristics

Abstract

As data complexity increases across various disciplines, the demand for effective visualization and visual analytic tools has escalated, highlighting the need for methodologies that guide cross-disciplinary collaborative visualization projects. Over the past two decades, the targeted application of visualization and visual analytics has captured increasing attention, emphasizing the essential role of guiding methodologies in addressing specific domain challenges. This thesis explores established design study methodologies within the visualization domain, focusing on their adaptation and refinement across different application domains through both theoretical exploration and practical application. Specifically, this thesis initiates by scrutinizing existing design study methodologies through a literature review, comparing and synthesizing various approaches. To gain deeper insights into the practices of design studies across different domains, an interview study was conducted.

This study collected detailed experiences and reflections from a diverse group of participants, including domain experts, visualization researchers, and software engineers. The findings help to highlight both the commonalities and variations in collaborative design processes across various roles and domain backgrounds. Additionally, a systematic literature review was undertaken to assess evaluation practices within the field, exploring how domain-specific differences and emerging trends influence these methodologies. In practical terms, we tailored and applied the ‘Nine-Stage Framework’ to conduct detailed design studies in collaboration with experts from several fields.

Notable projects included partnerships with historians to analyze historical book trade data and collaborations with bioinformaticians to utilize visual analytics for single-cell RNA sequencing data analysis. Insights from each study were thoroughly documented, revealing unique challenges and innovative solutions. The thesis further extends to additional design studies across domains such as artificial intelligence, political history, and epidemiology, demonstrating how methods are customized to fit varying domain contexts. The findings from these groundwork explorations and diverse practical applications underscore the necessity for adaptations to meet specific domain needs. Our research highlights how differences in domain characteristics and experts’ involvement influence the execution of design studies, including practices in preconditioning, collaborative design, development, and evaluation activities. The aim is to enhance methodological frameworks to be more adaptable and comprehensive, thereby better guiding design studies to align with the complexities and demands of the real world.

Date of Award	1 Apr 2025
Original language	English
Awarding Institution	King's College London
Supervisor	Alfie Abdul-Rahman (Supervisor) & Rita Borgo (Supervisor)