Accepted Tutorials

Here is the list of the accepted tutorials.

Color Scheming in Visualization

Sunday, October 16, 2022: 9:00 AM-12:00 PM CDT (UTC-5)

Theresa-Marie Rhyne, Visualization Consultant

This tutorial introduces the basics of color theory and steps though how to select and build color schemes for data visualization. It is intended for a broad audience of individuals interested in discovering the mysteries of color.

With a five stage colorization process, you learn how to build a color scheme with color harmony, incorporate the concepts of color models and address color deficiency. You explore the differences between mixing colors in the traditional painter color space and display color space. You discover online and mobile color suggestion apps to help with continued colorization. Along the way, color vision principles, perceptual uniformity with the the Hue Chroma Luminance (HCL) model as well as color gamut, spaces and systems are examined. Concepts like extending the fundamentals of the Bauhaus into digital media and overviews of color perception and appearance principals are covered.

NLP4Vis: Natural Language Processing for Information Visualization

Sunday, October 16, 2022: 9:00 AM-12:00 PM CDT (UTC-5)

Enamul Hoque, York University
Shafiq Joty, Nanyang Technological University

This tutorial will provide an introduction to natural language processing (NLP) to the interested researchers in the visualization (Vis) community. It will first motivate why NLP4Vis is an important area of research and provide an overview of research topics on combining NLP and Vis techniques. Then an overview of basic NLP techniques for text analysis followed by state-of-the-art deep learning models for NLP will be covered. In the final part, we will focus on various application tasks at the intersection of NLP and Vis. We will conclude with an interactive discussion of future challenges for NLP+Vis applications. The audience will include researchers interested in applying NLP for visualizations as well as others who focus more generally at the intersection of machine learning and visualization.

Visualization Analysis and Design

Sunday, October 16, 2022: 2:00 PM-5:00 PM CDT (UTC-5)

Tamara Munzner, University of British Columbia

This introductory tutorial will provide a broad foundation for thinking systematically about visualization systems, 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.

Sports Data Analysis and Visualization

Sunday, October 16, 2022: 2:00 PM-5:00 PM CDT (UTC-5)

Romain Vuillemot, Ecole Centrale de Lyon

In this tutorial, participant will learn recent techniques and tools to analyze sports players and teams from a data perspective. After the tutorial they will have an in-depth understanding of the state of the art of visualization for field-based sports (e.g., soccer, basket ball), performance sports (e.g., swimming, running) and opposition games (e.g., table tennis, badminton). For each of those sports, the instructor will introduce datasets, data pre-processing and structures, that participants will pick to work on a visualization design of their own, and code it. The instructor will also lead participants to calculate extra performance metrics for both players and team analysis they will include in their visualizations. Participants are expected to have intermediate knowledge of Tableau or coding skills in JavaScript and D3. The output of their work is a fully working, animated web application aimed at coaches or players to improve their tactical analysis. Participants will be encouraged to continue their work after the tutorial with advices on to collect their own sports data (e.g., from local university teams or their personal acticvity).

Topological Analysis of Ensemble Scalar Data with TTK, A Sequel

Monday, October 17, 2022: 9:00 AM-12:00 PM CDT (UTC-5)

Christoph Garth, Technische Universität Kaiserslautern
Charles Gueunet, Kitware SAS
Pierre Guillou, Sorbonne Université
Federico Iuricich, Clemson University
Joshua A Levine, University of Arizona
Jonas Lukasczyk, Technische Universität Kaiserslautern
Mathieu Pont, CNRS / Sorbonne Université
Julien Tierny, CNRS / Sorbonne Université
Jules Vidal, Sorbonne Université
Bei Wang, University of Utah
Florian Wetzels, Technische Universität Kaiserslautern

This tutorial presents topological methods for data analysis and visualization from a user’s perspective, with the Topology ToolKit (TTK), an open-source library for topological data analysis. In particular, similarly to 2021, this year’s tutorial has a special focus on ensemble data analysis with TTK, but with an updated content. Topological methods have gained in popularity and maturity over the last twenty years and success stories of established methods have been documented in a wide range of applications (combustion, chemistry, astrophysics, material sciences, etc.) with both acquired and simulated data, in both post-hoc and in-situ contexts. This tutorial provides a beginner’s introduction to topological methods for practitioners, researchers, students, and lecturers, with a special emphasis towards ensemble data analysis. In particular, instead of focusing on theoretical aspects and algorithmic details, this tutorial focuses on how topological methods can be used in practice to reduce ensemble datasets into concise yet meaningful topological data representations and how these representations can support advanced analysis. The tutorial describes in detail how to achieve these tasks with TTK. In contrast to the first iterations of this tutorial [13,14,16], this iteration focuses on the specific usage of TTK for ensemble data analysis, similarly to the 2021 edition [18], but with an updated content, including updated or additional features for ensemble data processing. First, we provide a general introduction to topological methods and their application in data analysis, and a brief overview of TTK’s main entry point for end users, namely ParaView, will be presented. Second, we detail TTK’s software infrastructure for ensemble data analysis, including TTK’s Docker support (to facilitate its deployment on computing servers), a tour of the topological data representations supported by TTK, and lastly TTK’s cinema support (to manipulate ensemble of topological data representations with a database formalism). Third, we will present concrete use cases of ensemble data analysis and visualization, using contour tree alignment as well as ensemble clustering and summarization with persistence diagrams and merge trees. Presenters of this tutorial include experts in topological methods, core authors of TTK as well as active users, coming from academia and industry. This tutorial mostly targets students, practitioners and researchers who are not necessarily experts in topological methods but who are interested in using them in their daily tasks. We also target researchers already familiar to topological methods and who are interested in using or contributing to TTK. We kindly ask potential attendees to optionally pre-register at the following address, in order for us to reach out to them ahead of the tutorial with information updates (for instance, last minute updates, instructions for the download of the tutorial material package, etc.): https://forms.gle/9b7TTERsjMs49g9m8

Tutorial web page (including all material, TTK pre-installs in virtual machines, code, data, demos, video tutorials, slides, etc): https://topology-tool-kit.github.io/ieeeVisTutorial.html

Riemannian Geometry for Scientific Visualization

Monday, October 17, 2022: 9:00 AM-12:00 PM CDT (UTC-5)

Markus Hadwiger, KAUST
Thomas Theussl, KAUST
Peter Rautek, KAUST

This tutorial introduces the most important basics of Riemannian geometry and related concepts with a specific focus on applications in scientific visualization. The tutorial will be an improved version of our tutorial of the same name at VIS 2021. Building on and extending the detailed VIS 2021 tutorial notes (see vccvisualization.org/RiemannianGeometryTutorial/), we will refer some detailed mathematical material from the VIS 2021 tutorial talks to the notes, and instead use the time in the tutorial talks to extend the discussion of various applications in visualization. We will also extend the notes with more applications.

The main concept in Riemannian geometry is the presence of a Riemannian metric on a differentiable manifold, comprising a second-order tensor field that defines an inner product in each tangent space that varies smoothly from point to point. Technically, the metric allows defining and computing distances and angles in a coordinate-independent manner. However, even more importantly, it in a sense is the major structure that defines the space where scientific data, such as scalar, vector, and tensor fields live.

However, the concept of a metric, and crucial related concepts such as connections and covariant derivatives, are not often used explicitly in visualization. In contrast to concepts of differential topology, which have been used extensively in visualization, for example in scalar and vector field topology, we believe that concepts from Riemannian geometry have been underrepresented in the visualization literature. One reason for this might be that most visualization techniques are developed for scalar, vector, or tensor fields given in Euclidean space R2 or R3, and data given on curved surfaces are usually treated explicitly through their embedding in R3. However, the presence of a Riemannian metric on a manifold has very important implications even for data given in Euclidean space, for example regarding the physical meaning of visualizations as well as for the use of non-Cartesian coordinates. Therefore, considering the metric tensor field explicitly provides several important benefits.

In this tutorial, we will build on our previous VIS 2021 tutorial, and in particular extend highlighting the additional insight that can be gained from employing concepts from Riemannian geometry in scientific visualization. Although we believe that insight is the most important benefit to be gained from using these concepts, we will also discuss computational advantages. In addition to Riemannian metrics, we will also introduce the most important related concepts from modern, coordinate-free differential geometry, in particular general (non-Cartesian) tensor fields and differential forms, smooth mappings between manifolds, Lie derivatives, and Lie groups and Lie algebras. Throughout the tutorial, we will use several examples from the scientific visualization literature, dealing with scalar, vector, or tensor fields, respectively, and highlight their implicit or explicit connections to Riemannian geometry.

Visualization in Bayesian Workflow

Monday, October 17, 2022: 9:00 AM-12:00 PM CDT (UTC-5)

Clinton Brownley, UC Berkeley

Visualization can be a powerful tool to help you build better statistical models. In this tutorial, you will learn how to create and interpret visualizations that are useful in each step of a Bayesian workflow for three common regression models, linear, logistic, and multilevel. A Bayesian workflow includes the three steps of (1) model building, (2) model interpretation, and (3) model checking/improvement, along with comparisons to other models. Visualization is helpful in each of these steps – generating graphical representations of the model and plotting prior distributions aid modeling building, visualizing MCMC diagnostics and plotting posterior distributions aid interpretation, and plotting posterior predictive, counterfactual, and model comparisons aid model checking/improvement.

Analyze and Visualize Large Scale Geospatial Data with H3 and HexTile

Monday, October 17, 2022: 2:00 PM-5:00 PM CDT (UTC-5)

Shan He, Foursquare
Nick Rabinowitz, Foursquare

Geospatial analysis can be challenging and time-consuming – from preparing data of different shapes, forms, sizes, to processing and visualizing large datasets at scale. In recent years, geospatial visualization has increasingly shifted from legacy GIS tools to web-based systems leveraging WebGL and other modern browser-based technologies to enable fast exploratory data analysis for a broader audience.

Using the Unfolded Studio platform, Shan He and Nick Rabinowitz will present an overview of common geospatial visualization approaches and techniques. We will then offer a deep dive into H3, an open source discrete global grid system developed to support a wide variety of geospatial use cases. Building on our discussion of H3 and existing spatial tiling systems, we will introduce Hex Tiles, a spatial tiling system based on H3 for fast visualization and analysis of large spatial datasets, and offer a hands-on tutorial demonstrating how Hex Tiles can be used for visualization and analysis in the Unfolded platform.

VTK-m – A ToolKit for Scientific Visualization on Many-Core Processors

Monday, October 17, 2022: 2:00 PM-5:00 PM CDT (UTC-5)

Dr. Tushar M. Athawale, Oak Ridge National Laboratory
Kenneth Moreland, Oak Ridge National Laboratory
David Pugmire, Oak Ridge National Laboratory
Silvio Rizzi, Argonne National Laboratory
Mark Bolstad, Sandia National Laboratories

In this tutorial, our goal is to familiarize the audience with the VTK-m library, an open-source toolkit for visualization and analysis on many-core devices. The visualization community can largely benefit from the VTK-m library via: 1) application of its rich set of high-performance portable visualization algorithms for accelerating visualization research and 2) deployment of new visualization algorithms on different high-performance architectures with VTK-m. The tutorial will cover the usage of VTK-m library for scientific visualization and development with VTK-m. Tutorial webpage (contains the slack channel link, tutorial materials, and VTK-m installation instructions): https://tinyurl.com/vtkm-tut-2022