Interactive Data Visualization
- 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.
- 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.
- Understand the multidisciplinary nature of the area and the relationship with other areas.
- Explore the experimental nature for design IDV systems.
João Carlos Gomes Moura Pires, Nuno Manuel Robalo Correia
Weekly - 4
Total - 60
General progamming skills
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
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.
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.
Introduction to Data Visualization
What Is Visualization?
Relationship between Visualization and Other Fields.
The Visualization Process.
Human Perception and Information Processing.
Semiology of Graphical Symbols.
The Visual Variables.
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)
Designing Effective Visualizations
Comparing and Evaluating Visualization Techniques
Systems Based on Data Type
Systems Based on Analysis Type
Text Analysis and Visualization
Modern Integrated Visualization Systems
Research Directions in Visualization