Lectures
Summaries

[T10]: Evaluating Visualization Techniques

22 May 2020, 04:10 PM
Evaluating Visualization Techniques: Introduction; User Tasks ; User Characteristics ; Data Characteristics ; Visualization Characteristics ; Structures for Evaluating Visualizations; Benchmarking Procedures; An Example of Visualization Benchmarking.

Recommended Readings: (i) Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew O. Ward et all, 2015, pages 365 - 406 and pages 475 - 487.

To Know:
  • Understand the necessary ingredients to evaluate a Viz: tasks, characteristics of the data, user’s level of experience.
  • How to measure the degree of accuracy of the task accomplishment.
  • User Characteristics that are relavant.
  • Data Characteristics that we should look and consider
  • Be able to describe your data Viz in terms of standard Visualization Characteristics
  • Understand the 3 types Structures for Evaluating Visualizations. Be able to decide which is more appropriate in your case
  • Be able to define a Benchmarking Procedure

[T09]: Interaction Concepts.

15 May 2020, 04:10 PM
Interaction Concepts: Interaction Operators; Interaction Operands and Spaces; A Unified Framework.

Recommended Readings: (i) Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew O. Ward et all, 2015, pages 365 - 406 and pages 475 - 487.

To Know:
  • Understand the different Interactions. Be able to distinguish between them. Be able to recognize them.
  • Undertand the model of operators and spaces to describe the interactions
  • Understand and recognize the different spaces
  • For each operator be able to identify some ways of activating it
  • Be able to apply this model to the available interactions on Tableau

[T08]: Visualization Techniques for Time Oriented Data

08 May 2020, 04:10 PM
Motivation; Characterizing Time-Oriented Data; Visualizing Time-Oriented Data; TimeBench.

Recommended Readings: (i) Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew O. Ward et all, 2015, pages 253 - 284.
To Know:
  • Understand the motivation for the need of a special treatment of temporal dimension.
  • Understand the distinction between the physical dimension time and a model of time in information systems.
  • Understand the different scales for time: Ordinal, Discrete and Continuous. Which is the most common in IS.
  • Understand the scopes: instant and interval.
  • Understand the types of arrangement: linear versus cyclic.
  • Know the Time primitives: instant vs. interval vs. span.
  • Know and understand the Characteristics of Time-Oriented Data: Scale: quantitative vs. qualitative; Frame of reference: abstract vs. spatial; Kind of data: events vs. states: Events; Number of variables: univariate vs. multivariate.
  • Understand the concepts of Internal time and External time
  • For Visualizing Time-Oriented Data, understand the two different mapping of time: Mapping of time to space; Mapping of time to time.
  • Understand the role and importance of user task for the visualization techniques.
  • Know the goals of the discussed visualization techniques and their main features

[T07]: Visualization Techniques for GeoSpatial Data

28 Apr 2020, 04:10 PM
Visualizing Geospatial Data; Visualization of Point Data; Visualization of Line Data; Visualization of Area Data; Other Issues in Geospatial Data Visualization.

Recommended Readings: (i) Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew O. Ward et all, 2015, pages 221 - 253.
To Know:
  • Why geospatial data is different of other type of data (for Visualization).
  • The geo-coordinates and the main conventions for geo-localization.
  • The necessity and the concept of map projection. The properties in terms of angles, form, area and distance.
  • Some projections and when they are appropriate.
  • Visual Variables for Spatial Data
  • Common issues for spatial data mapping: class separation, normalization, and spatial aggregation.
  • The different types of phenomena represented by point data: discrete versus continous, smooth versus abrupt changes
  • Visual Variables for Point Data
  • Dot Maps and their issues. Variants
  • Pixel Maps: motivation and issues
  • Line Data: available visual Variables
  • Types of line data maps and their issues
  • Types of thematic maps (area maps) and their limitations. When each one is appropriate
  • Common issues in mapping: generalization and detail; labelling

[T06]: Visualization Techniques for Multivariated Data

24 Apr 2020, 04:10 PM
Point-Based Techniques; Projecting high-dimensional points into 2D or 3D display space; Line-Based Techniques; Region-Based Techniques; Combinations of Techniques. Parallel coorrdinates
To Know:
  • Mapping 1D data to screen is a coordinates transformation
  • Distinguish and be able to select between different forms of 2D visualizations
  • Strategies to deal with the visualization of multivariate 2D data
  • Principles of probing 2D and 3D data
  • Distinguish between explicit and implicit 3D surfaces
  • How to control the different direct volume visualization techniques to make the wanted data to stand out

[T05]: Semiology of Graphical Symbols and The Eight Visual Variables

17 Apr 2020, 04:10 PM
The Visualization Process in Detail; Semiology of Graphical Symbols; The Eight Visual Variables; Historical Perspective;
Recommended Readings: (i) Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew O. Ward et all, 2015, pages 139 - 180. (ii) Pag 42 - 64 from Visualization Analysis & Design, Tamara Munzner
To Know:
  • The fundamental role of the "Mapping for visualizations" step
  • The expressiveness and efficient in visualization
  • What is Semiology of Graphical Symbols. What are the tools
  • What the main relationships between the data and its visualization: pattern, pattern variations and order
  • Understand the x, y and z paradigm.
  • The Eight Visual Variables and their relative importance and role.
  • The impact of the screen resolution
  • The Effects of Visual Variables

General Rules for Exploratory Data Analysis
Recommended Readings: (i) Exploratory Data Analysis with R, by Roger D. Peng, Chapters 5, 6 and optionally Chapter 7.
To Know:
  • Principle 1: Show comparisons
  • Principle 2: Show causality, mechanism, explanation, systematic structure
  • Principle 3: Show multivariate data
  • Principle 4: Integration of evidence
  • Principle 4: Integration of evidence

[T04]: Perception in Visualization

03 Apr 2020, 04:10 PM
Perception in Visualization (Color; Texture; Motion; Memory issues); Metrics (Absolute Judgment of 1D Stimuli; Absolute Judgment of Multidimensional Stimuli; Relative Judgment ); Cognition

Recommended Readings: (i) Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew O. Ward et all, 2015, pages 118 - 136; (ii) Subtleties of Color; (iii) Color Models.
Recommended Activities: (i) check and try http://colorbrewer2.org.
To Know:
  • The difference between the light we see and the colors we perceive
  • The RBG and the CMY color models. Their relations. Why they are not appropriate for perception
  • The Munsell’s color model. The goals for the perceptual models
  • The CIE models
  • The notion of color map, and the different types of color maps
  • Color maps for sequential data; for divergent data, for categorial data
  • The importante of color blindness to choose a color map
  • The most important rules to choose or build a color map
  • How to use texture to convey information.
  • The stick-figure” icons. How to use.
  • What is our “channel capacity” when dealing with color, taste, smell, or any other of our senses.
  • What graphical entities can be accurately measured by humans
  • How many distinct entities can be used in a visualization without confusion
  • With what level of accuracy do we perceive various primitives
  • What is Absolute Judgment of 1D Stimuli
  • What is Absolute Judgment of Multidimensional Stimuli
  • What is Relative Judgment
  • What are the Weber’s and Stevens’s Laws
  • Strategies to expand our communication capabilities

[T03]: Physiology and Perceptual Processing

27 Mar 2020, 04:10 PM
What Is Perception? Physiology (Anatomy of the Visual System, Visual Processing and the Eye Movement ); Perceptual Processing
Recommended Readings: (i) Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew O. Ward et all, 2015, pages 81 - 117; (ii)
To Know:
  • What is perception.
  • The notion that the brain makes a lot of assumption in the process.
  • The role of measurements and theories in the study of perception.
  • The visible spectrum, its composition the relation with color and many forms of blindness.
  • The eye main components and their role in the human vision system
  • The retina photosensitive cells, their characteristics, their role, their distribution.
  • What is the blind spot. How to detect.
  • Type of eye movements
  • The concept of Preattentive Processing.
  • The major contributions of the theories of Preattentive Processing
  • “Preattentive” visual tasks
  • Postattentive Vision
  • Feature Hierarchy
  • Change Blindness

[T02]: Data Foundations

20 Mar 2020, 04:10 PM
Sources of data; Dataset; Dependent and independent variables; Data types; Structure within and between records; Data Preprocessing.
Recommended Readings: (i) Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew O. Ward et all, 2015, pages 51 - 76; (ii) Visualization Analysis & Design, Tamara Munzner, pages 20 - 40.
To Know:
  • The concept of variable or dimension and the diference between independent and dependent variables.
  • The various data types taxonomies and the impact of a data type in visualization.
  • The structural aspects of a data set
  • Some data processing techniques: the goal of each one and the most important approaches
  • The Tamara's view about data and the mapping to the concepts used by Matthew O. Ward et all

[T01]: Course overview

06 Mar 2020, 04:10 PM
What we mean by “Interactive Data Visualization”? What is Visualization? Why Visualization is important? Early Visualizations; Visualization today; Visualization and other fields. Visualization Process; The role of Perception.

Course Organization and Overview: Syllabus; Bibliography; Evaluation rules; important dates, etc..
Recommended Readings: (i) Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew O. Ward et all, 2010, pages 1 - 33.
Recommended Activities: (ii) Visit the various sections of this site; (iii) instal Tableau software on your computer. Follow the link http://www.tableau.com/academic/students.
To Know:
  • What is Visualization.
  • The main "applications" of Visualization.
  • Why Visualization is important.
  • Key aspects of today Visualizations.
  • Some important landmarks of early visualizations. For each one why is a landmark.
  • The relation between Visualization and computer graphics. The differences between them.
  • The relation of Visualization with other fields.
  • The general steps of a Visualization Process
  • The role of Perception.
  • The role and the importance of the user.