Topic outline

  • General

    Welcome to Demo Module: VizInfo
    Teachers: Riccardo Mazza, Dominique Brodbeck, Michele Lanza & Richard Wettel

    Riccardo & Dominique

    General information & tools
    In this module you will get an introduction to the main concepts and techniques on Information Visualization (infovis). You will see how a huge amount of data can be represented in a visual format that allows people to gain insights that might lead to significant discoveries.

    In particular, you will learn:

    • how to use graphics to illustrate data and relationships between data elements
    • the basic principles of infovis
    • the different usage of infovis (presentation, explorative analysis, confirmative analysis)
    • strategies and techniques that help us transform large multidimensional datasets into structures that are conducive to visualization.
    • how different data structures (plain, hierarchical, network) can be visualized
    • how to create your own visualization solutions for concreete practical problems. You will have the opportunity to come up with your own visualization ideas and apply them to some interesting problems.
    • concepts of software visualization which will facilitate both the human understanding and effective use of computer software

  • Information Graphics

    from Module 3: Information Visualization

  • Introduction to Information Visualization

    from Module 3: Information Visualization 

    • minard-map 
    • Information Visualization can be defined as the use of computer-supported methods to interactively explore and derive new insights through the visualization of large sets of information.
    • In this section you will get a basic introduction on the theories and concepts of Information Visualization, a discipline which is branch of Human-Computer Interaction field with origins in late '80. You will see the main definition of the terms, how visualizations amplifying cognition, and some design principles defined by outstanding scholars in the field

  • Introduction to Software Visualization Principles

    from Module 10: Software Visualization

    system complexity

  • Multi-dimensional Data

    from Module 6: Multidimensional Visualization

    The problem with multi-dimensional data is that we only have three spatial dimensions available onto which to map the attributes. In practice we are even limited to two dimensions, as three dimensional visualization is tricky and usually only works well for data that has an intrinsic spatial structure. One common strategy to deal with that problem is parallelization.

    • The general idea of paralellization is to subdivide the (two-dimensional) space into an appropriate number of sub-spaces. Each of the sub-spaces is then used to show a two-dimensional representation of a selected aspect of the data. By showing all these sub-spaces in parallel and at the same time, correlations and patterns become evident that go beyond two dimensions. In the following we present two examples for the parallelization strategy:


      Scatterplot Matrix

      scatterplot matrices

      This technique constructs a matrix of small scatter plots with all possible combinations of pairs of attributes. The scatterplot matrix can also be regarded as an instance of the rule of small multiples that we know from Information Design.


      Parallel Coordinates

      parallel coordinates

      This technique breaks the dilemma of flatland by using one axis for each attribute, but arranging them in parallel in the plane, instead of orthogonal to each other as in the Cartesian way. An object in this parallel coordinate system is then represented not by a point, but a polygonal line that is constructed by connecting the values for all the attributes by a line between neighboring axes.

  • The Hierarchy

     from Module 6: Multidimensional Visualization

    Russian dolls

    Hierarchies are a common strategy to structure large amounts of information into nested manageable chunks. We find them everywhere, for example in taxonomies, tables of contents, file directories, or corporate organization charts.

    In this section we will first have a look at different ways to viually represent hierarchies.

    This is followed by a case study. We will look at a visualization method called the Parallel Coordinate Tree that combines multidimensional analysis with a tree structure representation. Distortion-oriented focus+context techniques are used to facilitate interaction with the visualization. It is a design study of a commercial application that was built, using this method to analyze and communicate results from large-scale customer satisfaction surveys.

    Finally, the exercise puts things back into the big picture.

  • Understanding Complex Data

    From Module 9: Data Visualization

    Understanding Complex Data

    In the basic modules we have learned about the fundamental principles and techniques of information visualization. In the application modules we have then seen how these can be integrated and applied to real world problems.

    The goal of this module is now to learn how to create your own visualization solutions for concrete practical problems. You will have the opportunity to come up with your own visualization ideas and apply them to some interesting problems. Some interesting data sets that you could use as a starting point are provided, but you can also identify a complex dataset yourself. Maybe from your professional context, or something you find on the Internet.

    The work will be performed in groups and the results presented in class, to emphasize the social and communicative aspects of visualization.

    The Task

    The task consists of the following steps:

    1. Look for an interesting data set
    2. Formulate the questions that you want to have answered about this data
    3. Think about what kind of visualization and interaction techniques are needed to provide these answers
    4. Develop the visualization by either using existing tools, or by creating a mock-up
    5. Create a presentation to communicate your findings
    6. Upload it to the VizHall
    7. Rate and discuss the work of your peers

    Criteria for the data set

    The dataset must be reasonably complex, i.e. it should not be possible to answer your questions with a few simple Excel manipulations. It can also consist partly or even wholy of non-numerical data. If in doubt, then plase ask the instructor for an assessment.

    • Data Sources

      From Module 9: Data Visualization

      Data Sources and Tools

      The following links contain a (non-comprehensive) collection of sources for interesting data sets that might inspire you:

      Data Sources

      Further resources:

      • Tools

        From Module 9: Data Visualization

        tools

        The following links contain a (non-comprehensive) collection of freely available tools that might inspire you:

        • Mondrian: Mondrian is a general purpose statistical data-visualization system written in JAVA. It features outstanding visualization techniques for Categorical Data, Geographical Data and LARGE Data.
        • Visulab: VisuLab (short for visualisation laboratory) is an experimental software package for the comparative visualisation of multivariate data. The latest Release of this software is designed as an Add-In to work in Microsoft Excel 2000, XP, 2003 and up.

        Further resources: