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CS525D - Data Visualization - Fall, 2008

Prof. Matthew Ward
FL231, 831-5671, matt@cs.wpi.edu
Office Hours: Tuesday at 2, Thursday and Friday at 1, Monday at 3, 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: I am in the process of finishing a textbook in this field, co-authored with Georges Grinstein and Daniel Keim. We will be using drafts of each chapter, some of which are more polished than others! Part of your assignment will include identifying bugs in the text, as well as testing out the exercises and projects.

Additional Resources: All documents for the course will be made available in the directory /cs/cs525d-f08/doc on the CCC file servers. 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:

I will provide each student with a 2GB USB memory stick for turning in your assignments. The stick will have copies of the current book chapters on it in separate folders. Within those folders you should create files or subdirectories for each of your homework components. To better help with planning for this course (current and future versions) I'd appreciate it if you included, for each component, how much time you actually spent on it. The sticks will be collected every 2 weeks and be returned on the following week. Please do not put the textbook chapters in a publicly accessible place - we don't want someone publishing our work under their own name!

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

Term Project: For the second half of the semester, the weekly programming projects will be replaced by a single term (really half term) project. The steps of this 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. 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.
  3. Explore your dataset and identify a modest number of "interesting" features in the data.
  4. Write a short (less than or equal to 8 pages, single spaced) paper describing the 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.

This project is due by the start of our last class. Assuming we have time, students will be given the opportunity to present their results.

Grading: Your grade will be based on bi-weekly reviews of your homework from the chapters we've covered. For each review, I will assign a letter grade, and your final grade will be an 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. Late assignments without prior permission may have a negative impact on the grade.

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 it supports at least 256 colors and your programs can be demonstrated on a machine on campus.

Software Resources: OpenGL, Java2D, Java3D, 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. Some visualization tools that you can download and test include:

  1. XmdvTool - http://davis.wpi.edu/xmdv
  2. SpiralGlyphics - http://davis.wpi.edu/~matt/projects/SpiralGlyphics/
  3. OpenDX - http://www.opendx.org
  4. Prefuse - http://prefuse.org
  5. DeVise - http://www.cs.wisc.edu/~devise/
  6. VTK - http://public.kitware.com/VTK/
  7. CViz - http://www.alphaworks.ibm.com/tech/cviz
  8. VolVis - http://www.cs.sunysb.edu/~vislab/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:

September 3:
Introduction and Foundations
September 10:
Data Models and Preprocessing
Septemer 17:
Perceptual Issues
September 24:
Visualization Frameworks and Taxonomies
October 1:
Spatial Data Visualization Techniques
October 8:
Geovisualization Techniques
October 15:
Non-Spatial Data Visualization Techniques (Multivariate)
October 22:
No Class (VisWeek Conference)
October 29:
Non-Spatial Data Visualization Techniques (Trees/Graphs)
November 5:
Interaction Concepts
November 12:
Interaction Techniques
November 19:
Designing Effective Visualizations
December 3:
Evaluating Visualizations
December 10:
Systems and Applications
December 17:
Future Directions

Data Sources:

  1. National Center for Health Statistics -
    http://www.cdc.gov/nchs/datawh/ftpserv/ftpdata/ftpdata.htm
  2. National Archive of Criminal Justice Data - http://www.icpsr.umich.edu/NACJD/
  3. StatLib at CMU - http://lib.stat.cmu.edu/
  4. Links to more statistics datasets - http://it.stlawu.edu/~rlock/maa51/data.html
  5. Everything about baseball - http://www.baseball1.com/
  6. Weather data - http://www.ncdc.noaa.gov/oa/climate/climatedata.html
  7. UC Irvine KDD Archive - http://kdd.ics.uci.edu/
  8. Inter University Consortium for Political and Social Research - http://www.icpsr.umich.edu/
  9. InfoVis and Vis conference contest data sets - conference web sites
  10. Border bouncing data from NVAC - see Prof. Ward

Links:

  1. Michael Friendly's Visualization Gallery
  2. Periodic Table of Visualizations
  3. Many Eyes, from IBM
  4. Selected Topics in Graphical Analytics
  5. Some nice visualizations from the New York Times
    1. Naming Names
    2. Buy or Rent
    3. Candidate Travels
    4. Healthcare Costs
    5. A Collection of them
  6. A survey of visualizations gathered by UMD
  7. A cool visualization of global demographic data
  8. A gallery of visualizations implemented in Prefuse

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Matthew Ward 2008-09-03