Visualisation using Empirical Modelling principles and tools

Over the past thirty years, the visualization community has developed theories and models to explain visualization as a technology that augments human cognition by enabling the efficient, accurate, and timely discovery of meaningful information in data. Along the way, practitioners have also debated theories and practices for visualization evaluation: How do we generate durable, reliable evidence that a visualization is effective? Interestingly, there is still no consensus in the visualization research community how to evaluate visualization methods. The goal of this chapter is to rise awareness of still open issues in the visualization evaluation, and to discuss appropriate evaluations suitable for different visualization approaches. This includes user studies and best practices to conduct them but also other approaches for suitable evaluation of visualization. The chapter is structured as a moderated dialogue of two visualization experts.

2011

Numeracy, 2019

Claus O. Wilke has authored an excellent reference about producing and understanding static figures, figures used online, in print, and for presentations. His book is neither a statistics nor programming text, but familiarity with basic statistical concepts is helpful. Written in three parts, the book presents both the math and artistic design aspects of telling a story through figures. Wilke makes extensive use of examples, labels them good, bad, wrong or ugly, and explains why he deems them so. He includes chapters that serve as a directory of visualizations, and the reader will encounter familiar figure types as well as more esoteric ones. He discusses aesthetic considerations, including color, that make figures more compelling, and provides tips to connect with the audience to tell a story. Wilke intends the book for those producing figures, but consumers of graphical content will also find it helpful as his clear and concise style-and his effective use of exampleswill help them evaluate the worth of figures they encounter.

This paper provides an overview of data visualization, tracing its historical evolution from ancient visual communication to modern digital applications. It outlines the core principles of effective data visualization, emphasizing clarity, accuracy, and simplicity. The paper explores various types of data visualizations, including statistical, dynamic, and interactive formats, and examines the spectrum of tools available, from open-source platforms and commercial software to programming libraries. It also addresses critical ethical considerations in data visualization, such as honesty, avoiding bias, and ensuring privacy. Furthermore, the paper discusses the inherent challenges in creating meaningful visualizations, including data overload and the selection of appropriate visual types. Finally, it examines emerging trends shaping the future of data visualization, highlighting the transformative roles of artificial intelligence, augmented reality, and enhanced interactivity in facilitating deeper data understanding and informed decision-making.

Handbook of Human Centric Visualization, 2014

This paper explores the extent to which a scientific framework for visualization might be possible. It presents several potential parts of a framework, illustrated by application to the visualization of correlation in scatterplots. The first is an extended-vision thesis, which posits that a viewer and visualization system can be usefully considered as a single system that perceives structure in a dataset, much like "basic" vision perceives structure in the world. This characterization is then used to suggest approaches to evaluation that take advantage of techniques used in vision science. Next, an optimal-reduction thesis is presented, which posits that an optimal visualization enables the given task to be reduced to the most suitable operations in the extended system. A systematic comparison of alternative designs is then proposed, guided by what is known about perceptual mechanisms. It is shown that these elements can be extended in various ways—some even overlapping with parts of vision science. As such, a science of some kind appears possible for at least some parts of visualization. It would remain distinct from design practice, but could nevertheless assist with the design of visualizations that better engage human perception and cognition.

InfoDesign, 2021

Animation can have a range of purposes when applied to data visualization. It can be used to enhance a chart's readability, make it more appealing, or aid the narrative storytelling. The objective of this paper is to understand the different ways researchers have found to describe the use of animation as a tool for data visualization, and how these approaches interact with one another. Thus, an analysis protocol was established-based on definitions by Munzner, Heer & Robertson, Schwabish, and other researchers in the field-and it was applied to real examples in order to test its ability to identify and categorize types of animation and their uses. It is expected that this analysis protocol could be used as a resource for data visualization professionals and enthusiasts, students, and other interested parties who could benefit from a deeper examination of the theme.

2006

This paper describes an action research project based on the process of designing the visualisation of heart and breath rate data for the interactive artwork Cardiomorphologies. The project aimed to realise the affective goals of the artist as closely as possible by studying the audience experience of the visualisations and incorporating the findings into an iterative design process. The qualities of richness and ambiguity were found to contribute to the achievement of the artists' two major aims: 1) to create a sense of integrated physical and mental engagement with the work and 2) to create a reflective state in which participants consider correlations between their thoughts and specific physiological states

2002

Abstract As the field of Visualization matures, there'sa trend to move away from the use of ad-hoc graphics techniques and toward algorithms that are based on a formal understanding of how people perceive visual representations of information. Psychometric methods from experimental psychology can be used to quantify the relationships between the properties of images and what people perceive.