SUNDAY, OCTOBER 23
8:30AM-12:10PM
Location: Johnson A+B
Min Chen (University of Oxford), Mateu Sbert (University of Girona, Tianjin University), Anton Bardera (University of Girona), Han-Wei Shen (The Ohio State University), Miquel Feixas (University of Girona), Ivan Viola (TU Wien)
In this half-day tutorial (intermediate to advanced), we review a variety of applications of information theory in visualization. The holistic nature of information-theoretic reasoning has enabled many such applications, ranging from light placement to view selection, from feature highlighting to transfer function design, from data fusion to visual multiplexing, and so on. Perhaps a particularly exciting application is the potential for information theory to underpin the discipline of visualization, for example, mathematically confirming the benefit of visualization in data intelligence.
SUNDAY, OCTOBER 23
8:30AM-12:10PM
Location: Ruth
Jun Tao (University of Notre Dame), Hanqi Guo (Argonne National Laboratory), Bei Wang (University of Utah), Christoph Garth (University of Kaiserslautern), Tino Weinkauf (KTH Stockholm)
Tutorial website
Flow visualization has been a central topic in scientific visualization for many years, which can be explained by the ubiquity of vector fields in various kinds of scientific, engineering, medical researches. In all these domains, with today’s ever-growing computation power, numer- ical simulations produce large, time-varying and highly complex vector fields. Preserving the rich information in these large and complex vector fields and presenting concise visualizations for clarity are two desired goals, but they are often conflicting. Striking a balance be- tween them is challenging, which requires us to better distinguish the features from the contexts. Understanding and extracting features be- come critical to obtain insights from the vector fields with growing sizes and complexities. In this tutorial, we cover different topics centered at the feature- based flow visualization and analysis: (a) interactive techniques that allow users to discover their features of interest; (b) spatio-temporal flow analysis that considers n-dimensional unsteady flows as (n+1)- dimensional steady flows; (c) feature extraction, tracking and simpli- fication with robustness that captures structural stability of the data; d) vector field techniques for large-scale time-varying data, especially the parallel algorithms and in-situ techniques; and (f) theories and scalability issues in ensemble and uncertain flow. This tutorial aims at providing information of the state-of-the-art techniques for feature-based flow visualization in different aspects, including interac- tive exploration, large-scale time-varying data, topological robustness and ensemble data.
SUNDAY, OCTOBER 23
2:00PM-5:55PM
Location: Holiday 6
Tamara Munzner (University of British Columbia)
This introductory tutorial will provide a broad foundation for thinking systematically about visualization sys- tems, built around the idea that becoming familiar with analyzing existing systems is a good springboard for designing new ones. The major data types of concern in visual analytics, information visualization, and scientific visualization will all be covered: tables, networks, and sampled spatial data. This tutorial is focused on data and task abstractions, and the design choices for visual encoding and interaction; it will not cover algorithms. No background in computer science or visualization is assumed.
SUNDAY, OCTOBER 23
2:00PM-5:55PM
Location: Ruth
Bernice E. Rogowitz (Visual Perspectives Research and Consulting)
This course builds upon the foundation set by the IEEE Vis course “Human Vision and Cogniton for Emerging Technologyies,” presented in 2012. The course involves a review of key principles outlined earlier, focusing on human spatial, luminance and color perception, interpreted for data visualization and visual analytics tasks, with many examples from real-world projects in a wide range of industries. The course includes many new topics, such as multisensory interactions, and provides fundamental insights into the design of experiments involving human observers.
SUNDAY, OCTOBER 23
2:00PM-5:55PM
Location: Latrobe
Jonathan C. Roberts (Bangor University), Christopher Headleand (Bangor University), Panagiotis Ritsos (University of Chester)
When developing visualization software, developers need to plan what they are going to build. They need to make plans of how the data can be visualized using a computer interface. Low fidelity methods, such as sketching, have been used before, however they are ad hoc. This tutorial leads the attendees through sketching designs, considering design alternatives using the Five Design-Sheet methodology.
MONDAY, OCTOBER 24
8:30AM-12:10PM
Location: Peale A+B+C
Klaus Mueller (Stony Brook University), Shenghui Cheng (Stony Brook University)
Analyzing high-dimensional data and finding hidden patterns in them is a difficult problem and has attracted numerous research efforts in the visualization community and beyond. Gaining insight into high dimensional data is at the core of big data analysis and data science. Automated methods can be useful to some extent but bringing the data analyst into the loop via interactive visual tools can help the discovery process tremendously. All of these visual tools use some kind of projection strategy to convey the high dimensional space within the confines of the two screen dimensions. Since this projection is an inherently ill-posed problem in all but the most trivial cases, all methods will bear certain trade-offs. Knowing the strengths and weaknesses of the various paradigms existing in the field can inform the design of the most appropriate visualization strategy for the task at hand. It can help practitioners in selecting the best among the many tools available, and it can help researchers in devising new tools to advance the state of the art. This tutorial aims to serve both of these factions of the visualization community.
MONDAY, OCTOBER 24
8:30AM-12:10PM
Location: Ruth
Theresa-Marie Rhyne (Visualization Consultant)
We examine the foundations of color theory & how these methods apply to building effective visualizations. We define color harmony & demonstrate the application of color harmony to case studies. Case studies include ensemble scientific visualizations, historic & new infographics, correlation in biological data, rainbow color deficiency safe examples, & time series animations. The Pantone Matching System, Munsell Color System and other hue systems are reviewed. The features of ColorBrewer, Adobe’s Capture CC app, & Josef Albers “Interaction of Color” app are examined. We also introduce “Gamut Mask” & “Color Proportions of an Image” analysis tools. Our tutorial concludes with a hands on session that teaches how to use online and mobile apps to successfully capture, analyze and store color schemes for future use in visual analytics. This includes evaluations for color deficiencies using Vizcheck & Coblis. These color suggestion tools are available online for your continued use in creating new visualizations. Please bring small JPEG examples of your visualizations for performing color analyses during the hands on session.
MONDAY, OCTOBER 24
8:30AM-12:10PM
Location: Latrobe
Camilla Forsell (Linköping University), Matthew Cooper (Linköping University)
User-centred evaluation has repeatedly been identified as an aspect of development in visualization that is both vitally important and frequently quite poorly carried out within the field. The objective of this half-day introductory tutorial is to introduce the topic, provide knowledge and clear guidelines about what is important to consider and what resources are available to support further study in this area. Participants will also learn to better judge the relevance and quality of a publication presenting an evaluation when reviewing such work since similar rules apply.
MONDAY, OCTOBER 24
2:00PM-5:55PM
Location: Ruth
Rafael Ballester-Ripoll (University of Zürich), Renato Pajarola (University of Zürich)
Initially proposed as an extension of the concept of matrix decomposition for three and more dimensions, tensor decompositions have found numerous applications in visualization and visual computing. They constitute a powerful mathematical framework for compactly representing and manipulating dense data fields, especially in many dimensions. This course will introduce the most popular decomposition models and showcase emerging tensor methods for compression, interactive visualization, texture synthesis, denoising, and multidimensional inpainting. Multidimensional visual data types of interest include image and geometry ensembles, hyperspectral images, volumes and corresponding time-varying data.
MONDAY, OCTOBER 24
2:00PM-5:55PM
Location: Latrobe
Sheelagh Carpendale (University of Calgary), Uta Hinrichs (University of St. Andrews), Trevor Hogan (Cork Institute of Technology), Alice Thudt (University of Calgary), Melanie Tory (Tableau), Jo Vermeulen (University of Calgary), Jagoda Walny (University of Calgary)
Tutorial website
Evaluation is increasingly recognized as an essential component of visualization research. However, evaluation itself is a changing area of research. New methods to extend and validate our research continue to emerge. This 1/2-day tutorial is designed for beginning to intermediate audiences. We will focus on qualitative research methods using a mixture of talks and hands-on activities. After completing this tutorial, people will have a richer understanding of the benefits and challenges of qualitative empirical research.
