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CS525D - Data Visualization - Spring, 2014

Prof. Matthew Ward
FL231, 831-5671,
Office Hours: Tuesday at 2, Thursday at 1, Friday at 10, others by appointment

Overview: Visualization is the graphical communication of data and information for the purposes of presentation, confirmation, and exploration. For thousands of years, images have been used to convey numbers, concepts, and relationships using techniques such as maps, icons, graphs, and other visual forms. In the past 2 decades, visualization has evolved into a discipline, drawing from such fields as graphics, human-computer interaction, perceptual psychology, and art.

The goal of this course is to expose students to the field of data visualization and familiarize them with the stages of the visualization pipeline, including data modeling, mapping data attributes to graphical attributes, existing visualization techniques, tools, and paradigms, perceptual issues, and evaluating the effectiveness of visualizations for specific data, task, and user types.

Textbook: Interactive Data Visualization: Foundations, Techniques, and Applications, by M. Ward, G. Grinstein, and D. Keim. ISBN 9781568814735.

Additional Resources: All documents for the course will be made available at the website Also, I have an extensive library of books and conference proceedings on visualization (see the list below). If there is any topic that you'd like to delve deeper into, or look for clarification or alternate viewpoints, feel free to borrow any of my collection for a week or two.

Assignments: Each week the assignment will consist of several components, each with an expected amount of time you should dedicate:

Exams: There will be no exams given for this course.

Term Project: The steps of the programming project are as follows:

  1. Select some socially relevant data set or information source as a focus for visual analysis. Confirm your topic with Prof. Ward.
  2. locate 1 to 3 papers that present methods for visualizing this kind of data. Summarize and include references to them in your project report.
  3. Design or extend a visualization to allow exploration of your data/information. You are not allowed to just use Excel! There should be some programming involved.
  4. Explore your dataset and identify a modest number of "interesting" features in the data.
  5. Write a short (between 5 and 10 pages, single spaced) paper describing the data, the papers you read related to visualizing this type of data, the process you followed in developing your visualization, the methods used for exploration, and the things you discovered. Include screen shots and relevant references.
  6. Create a poster describing your data and how you visualized it. Show more than one view and, if possible, more than one data set.

This project is due by the start of our last class. We will hold a poster session for all to see what everyone has been doing.

Grading: Your grade will be roughly computed as follows:

For each part, I will assign a letter grade, and your final grade will be a weighted average of these grades. The rough grading will be as follows: check/B = met expectations, check+/A = exceeded expectations, check-/C = did not meet expectations, X/F insufficient to earn passing grade. Late assignments without prior permission may have a negative impact on the grade. I will grade the programming project based on all aspects of the work, including the progress reports (presentations), report, and poster.

Academic Honesty: Copying the work of others and turning it in as your own is considered academic dishonesty, and is strictly forbidden in this class. Violators of this policy will receive a 0 grade for the assignment, and the incident will be reported to the department chair and the Dean of Students' Office.

Facilities: You can use whatever computer you have at your disposal, as long as your programs can be demonstrated on a machine on campus.

Software Resources: OpenGL, Java2D, Java3D, Processing, or X can be used for software development. Basically whatever language you used in your graphics course will do. In most cases, you can get by with 2-dimensional graphics, though for some types of visualization, 3-D is essential. When you turn in your assignments, please include instructions for compiling and executing the program. I may decide to instead have you demonstrate the programs in action if it is too time-consuming for me to figure out how to build and run them.

It may also be possible to build your assignments using an existing visualization tool as a base (you are expected to add code to these). Some visualization tools that you can download and test include:

  1. XmdvTool -
  2. SpiralGlyphics -
  3. OpenDX -
  4. Prefuse -
  5. DeVise -
  6. VTK -
  7. CViz -
  8. VolVis -\_home.html
  9. extra points for finding others (other than WPI-developed)

Books Available from Prof. Ward:

  1. Bartz, Dirk, Visualization in Scientific Computing '98, Springer, 1998.
  2. Bederson, Ben, and Shneiderman, Ben. The Craft of Information Visualization, Morgan Kaufman, 2002.
  3. Berthold, Michael, and Hand, David, Intelligent Data Analysis (2nd edition), Springer, 2003.
  4. Brown, Judith. et al., Visualization: Using computer graphics to explore data and present information, Wiley and Sons, 1995.
  5. Card, Stuart, et al.. Readings in Information Visualization, 1999.
  6. Chen, Chaomei. Information Visualization and Virtual Environments. Springer, 1999.
  7. Chen, Chaomei et al., Handbook of Data Visualization, Springer, 2008.
  8. Cleveland, William, Visualizing Data, Hobart Press, 1993.
  9. Di Battista, Giuseppe et al., Graph Drawing, Prentice Hall, 1999.
  10. Diehl, Stephan, Software Visualization, Springer, 2007.
  11. Fayyad, Usama, e. al.. Information Visualization in Data Mining and Knowledge Discovery. Morgan-Kaufmann, 2002.
  12. Few, Stephen, Show Me the Numbers, Analytics Press, 2004.
  13. Friendly, Michael, Visualizing Categorical Data, SAS Publishing, 2000.
  14. Grave, Michael, et al., Visualization in Scientific Computing, Springer-Verlag, 1994.
  15. Hagen, Hans, et al., Scientific Visualization - Dagstuhl '97, IEEE CS Press, 2000.
  16. Harris, Robert. Information Graphics, a Comprehensive Illustrated Reference, Oxford University Press, 1999.
  17. Keller, Peter, and Keller, Mary. Visual Cues: Practical Data Visualization. IEEE Press, 1993.
  18. Kerren, Andreas, et al.. Information Visualization: Human-Centered Issues and Perspectives, Springer, 2008.
  19. Kosslyn, Stephen. Elements of Graph Design, W.H. Freeman, 1994.
  20. Lichtenbelt, Barthold, et al. Introduction to Volume Rendering. Prentice-Hall, 1998.
  21. Mullet, Kevin, and Darrell Sano, Designing Visual Interfaces, Prentice Hall, 1995.
  22. Nelson, Gregory, et al.. Visualization in Scientific Computing. IEEE CS Press, 1990.
  23. Nelson, Gregory, et al.. Scientific Visualization: Overviews, Methodologies, Techniques. IEEE CS Press, 1997.
  24. Post, Fritz et al., Data Visualization: the state of the art, Kluwer, 2003.
  25. Schroeder, Will, et al.. The Visualization Toolkit (2nd edition). Prentice-Hall, 1998.
  26. Soukup, Tom, and Davidson, Ian, Visual Data Mining, Wiley, 2002.
  27. Spence, Robert. Information Visualization. Addison-Wesley, 2001.
  28. Stasko, John, et al., Software Visualization, MIT Press, 1998.
  29. Telea, Alexandru, Data Visualization Principles and Practice, AK Peters, 2008.
  30. Thalmann, Daniel, Scientific Visualization and Graphics Simulation, Wiley, 1990.
  31. Thomas, James, and Cook, Kristin. Illuminating the Path: the Research and Development Agenda for Visual Analytics, IEEE CS Press, 2005.
  32. Tufte, Edward. The Visual Display of Quantitative Information. Graphics Press, 1983.
  33. Tufte, Edward. Envisioning Information. Graphics Press, 1990.
  34. Tufte, Edward. Visual Explanations. Graphics Press, 1997.
  35. Tufte, Edward. Beautiful Evidence, Graphics Press, 2006.
  36. Ware, Colin. Information Visualization: Perception for Design. Morgan-Kaufmann, 1999.
  37. Wilkinson, Leland, The grammar of graphics (2nd edition), Springer, 2005.
  38. Woolman, Matt. Digital Information Graphics, Watson Guptill Publishers, 2002.
  39. Proceedings of IEEE Visualization Conference. 1990 - present.
  40. Proceedings of IEEE Symposium on Information Visualization. 1995 - present.
  41. Proceedings of IEEE Symposium on Visual Analytics Science and Technology, 2006-present.
  42. Proceedings of International Conference on Information Visualization. 1999, 2005.
  43. Proceedings of the Eurographics Visualization Symposium. 2003, 2004.
  44. Proceedings of Volume Visualization and Graphics Symposium. 1998, 2000, 2002.
  45. Proceedings of Parallel Visualization and Graphics Symposium. 1999.
  46. Proceedings of Parallel and Large-Data Visualization and Graphics Symposium. 2001.

Tentative Schedule:

January 21:
introduction and foundations
January 28:
data models and preprocessing
February 4:
perceptual issues (Data Sets Chosen and Approved)
February 11:
visualization frameworks and taxonomies (Research 1)
February 18:
spatial data visualization techniques (Projects A)
February 25:
geovisualization techniques (Projects B)
March 4:
non-spatial data visualization techniques (Research 2)
March 11:
Term Break, no class
March 18:
trees and graphs (Projects A)
March 25:
text visualization techniques (Projects B)
April 1:
interaction concepts (Research 3)
April 8:
interaction techniques (Projects A)
April 15:
designing effective visualizations (Projects B)
April 22:
evaluating visualizations (Research 4)
April 29:
future directions (Posters)
May 6:
Snow Day

Every programming project group should decide on their datasets and have them approved by me by week 3. Thus you should plan to e-mail me your preliminary choice (and maybe a second choice) by February 1. The programming project teams will be divided into Group A and B, and each will present their progress 3 times during the term. I will assign teams to groups once I have approved the dataset choices. For the research projects, there will be 4 sessions for people to do their 10-15 minute talks. I will seek volunteers for week 4, and assign the rest at random.

Some Data Sources:

  1. Tons of data the government collects-
  2. National Center for Health Statistics -
  3. National Archive of Criminal Justice Data -
  4. StatLib at CMU -
  5. Links to more statistics datasets -
  6. Everything about baseball -
  7. Weather data -
  8. UC Irvine KDD Archive -
  9. Inter University Consortium for Political and Social Research -
  10. InfoVis and Vis conference contest data sets - conference web sites
  11. KDD Cup Data Discovery Challenges -
  12. A nice collection of data and information visualization challenges - http://
  13. Border bouncing data from NVAC - see Prof. Ward

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Matthew Ward 2014-01-27