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Interactive Data Visualization

Objectives

Knowledge

  • What is Information Visualization, Data Visualization (DV) and the different purposes of DV.
  • The role of interactivity in DV and the general interaction patterns.
  • The concept of Visual Variable and the practical consequence in the design of Interactive Data Visualization (IDV).
  • The classification of data for DV purposes and the impact on IDV.
  • For each type of data the most relevant available techniques.
  • Due to its wide applicability, some deep understanding on Visualization Techniques for multivariate Data time oriented data and Geospatial Data.
  • The main components of general IDV systems and the principal characteristics required on modern IDV systems.
  • The available approaches to Compare and Evaluate Visualization Techniques and Systems.
  • The actual trends in IDV and their role in more general systems and applications.

Application

  • Choose the visual variables and visualization techniques for a given data set and purposes.
  • Use a given an existing IDV system to explore one or more data sets.
  • Based on existing frameworks and platforms, design and build an IDV system appropriate for a class of data sets and a class of exploration and visualization tasks.
  • Setup an experimental environment to evaluate a DV technique. Analyze the data gathered in the experimentation.

Soft-Skills

  • Understand the multidisciplinary nature of the area and the relationship with other areas.
  • Explore the experimental nature for design IDV systems.

General characterization

Code

11565

Credits

6.0

Responsible teacher

João Carlos Gomes Moura Pires, Nuno Manuel Robalo Correia

Hours

Weekly - 4

Total - 60

Teaching language

Português

Prerequisites

General progamming skills

Bibliography

Main Bibliography:

  • Visualization Analysis & Design, Tamara Munzner, 2015, ISBN: 9781466508910
    ISBN (e-Book): 9781498707763

  • Interactive Data Visualization: Foundations, Techniques, and Applications, Second Edition. Matthew O. Ward, Georges Grinstein, Daniel Keim, 2015, ISBN 9781482257373

  • How Charts Lie: Getting Smarter about Visual Information. Alberto Cairo, 2019
    ISBN-13: 978-1324001560

Teaching method

In the lectures the course content is presented, illustrated with application examples and references to related work. The laboratory classes are intended for specification, development and presentation of the project that deals with topics presented during the lectures.

The evaluation of the course consists of five elements: two mid-term written individual tests and one project, with several phases (specification, state of the art and code/interface) that together account for a project to develop a interactive data visualization application, using real data.

Evaluation method

The evaluation of the course includes 3 elements: two individual tests carried out throughout the semester and 1 project, with several phases (specification, article, code / interface and oral), which correspond to a project of an interactive data visualization solution.

Calculation formula of the final classification (rounded to the nearest integer):
Final classification = 25% Test1 + 25% Test2 + 50% Project

where the Tests and the Project grades are rounded to one decimal place.

Course approval requires the following classifications:

(average (Test1; Test2)> = 10) and Project> = 10

Students who obtained Project > = 10 and did not pass the tests will be able to take an exam, the grade of which will replace the tests grade in the calculation of the final grade.

The tests and exam will be carried out preferably in person and will be without consultation.

Subject matter

Introduction to Data Visualization

            What Is Visualization?

            Relationship between Visualization and Other Fields.

            The Visualization Process.

            Data Foundations.

            Human Perception and Information Processing.

            Semiology of Graphical Symbols.

            The Visual Variables. 

Visualization Techniques

            Visualization Techniques for Spatial Data

            Visualization Techniques for Geospatial Data

            Visualization Techniques for Time-Oriented Data                     

            Visualization Techniques for Multivariate Data

            Visualization Techniques for Trees, Graphs, and Networks

            Text and Document Visualization           

Interaction Concepts and Techniques

            Interaction Operators, Operands and Spaces (screen, object, data, attributes)

            Visualization Structure Space (Components of the Data Visualization)

            Animating Transformations

            Interaction Control

            Designing Effective Visualizations

            Comparing and Evaluating Visualization Techniques                                                                          

Visualization Systems

            Systems Based on Data Type

            Systems Based on Analysis Type

            Text Analysis and Visualization

            Modern Integrated Visualization Systems

            Toolkits

Research Directions in Visualization