Data Visualization For The Community
[Formerly Data Visualization from 2D to 4D]
DM-GY 9103-C, Fall 2018
Prof. Arlene Ducao, arlduc [at] nyu.edu
Thursdays, 3:30-6:20 PM
2 Metrotech, Room 820
- To understand the history, functionality, and anatomy of data visualization.
- To classify data and information visualization based on temporal, spatial, tangible, and contextual criteria.
- To understand the politics and community contexts that inform data visualization.
- To choose and apply the appropriate tools for developing a wide array of basic data visualizations.
- To plan and execute a complex data visualization project based on human-centered design principles, including significance, relevance, and usability.
Note: Guest lecturers and trips are subject to change.
Phase I: Let’s Visualize.
- Session 1: September 6. Class Overview, Icebreaker, and Toolkit.
- Session 2: September 13. Urban Data. Guests: Prof Laura Wolf-Powers, Hunter College Urban Policy and Planning; Jonathan Tarleton, Boston Housing Authority.
- Session 3: September 20. Context, Education, and Informal Economies. Guests: Kate Mytty, MIT CREATE; Raphael Santo, NYU/CSforAll
- Session 4: September 27. Image-based Data. Guest : Marina Hassapopoulou, NYU Moving Image Archiving and Preservation.
- Session 5: October 4. Phase 1 visualization presentations.
Phase 2: Whose Data Is It, Anyway?
- NO CLASS: October 11. Self-guided field trip to NYSCI.
- Session 6: October 18. Field trip to Pro-Public and NYU Data Services (Bobst).
- Session 7: October 25. Book Club and Beyond Visualization: Data Analysis. Guest TBD.
- Session 8: November 1. Book and community updates. Guest TBD.
- Session 9: November 8. Phase 2 paper presentations.
Phase 3: Visualization for Actual People.
- Session 10: November 15. Guest Speaker: Nick Bartzokas, American Museum of Natural History. Phase 3 check-ins. Activism Collaborative Storymap.
- NO CLASS: November 22, Thanksgiving.
- Session 11: November 29. Data Vis Distinguished Alum: Anneka Goss. Phase 3 one-on-one project discussions. Color Discussion. Good Vis, Bad Vis.
- Session 12: December 6. Student Requests (guests and discussions). Final Project Studio time.
- Session 13: December 13. Final project presentations. Invite your community partners!
Suggested Tools (and see more in the Assignment 1 PDF)
- Data exploration tools. See some examples at Northwestern Knight Lab and DataBasic.io.
- A tabular software environment (e.g. Excel, Google sheet, Zoho sheet, etc.)
- A relational software environment or interface (e.g. MySQL, Tableau, SODA)
- Web visualization libraries (e.g. D3.js, Threejs, Bokeh, SVG)
- A cartographic package (TileMill, CartoDB, QGIS)
- A natural language processing tool (IBM Watson)
- A 3D or CAD tool (e.g. Unity3D, TinkerCAD)
- Creative computing tools (e.g. Processing, Arduino, Quartz Composer)
- Data Analysis tools (e.g. R, Jupyter Notebooks, Matlab, SPSS)
Recommended Books (to be discussed in Class 1)
Technique / Science Books
- Börner, Katy. Atlas of Science: Visualizing What We Know. Cambridge: MIT Press, 2010.
- Börner, Katy. Atlas of Knowledge: Anyone Can Map. Cambridge: MIT Press, 2015.
- Börner, Katy and David E. Polley. Visual Insights: A Practical Guide to Making Sense of Data. Cambridge: MIT Press, 2014.
- Cairo, Alberto. The Truthful Art: Data, Charts, and Maps for Communication. New Riders, 2016.
- Day, Ronald. Indexing It All: The Subject in the Age of Documentation, Information, and Data. MIT Press, 2016.
- Lima, Manuel. Visual Complexity: Mapping Patterns of Information. Princeton: Princeton Architectural Press, 2013.
- Meeks, Elijah. D3.js In Action. Manning Publications, 2015.
- Munzner, Tamara. Visualization Analysis and Design. AK Peters / CRC Press, 2014.
- Murray, Scott. Data Visualization For The Web. O’Reilly, 2013.
- Telea, Alexandru. Data Visualization: Principles and Practice, Second Edition. AK Peters / CRC Press, 2014.
- Ware, Colin. Information Visualization, Third Edition: Perception For Design. Morgan Kaufman, 2012.
- Wong, Dona. The Wall Street Journal Guide to Information Graphics: The Dos and Don’ts of Presenting Data, Facts, and Figures. W.W. Norton & Company, 2013.
- Yau, Nathan: Visualize This: The Flowing Data Guide to Design, Visualization, and Statistics. Wiley, 2011.
Historical / Foundational Books
- Burke, Colin. Information and Intrigue: For Index Cards to Dewey Decimals to Alger Hiss. MIT Press, 2014.
- Brinton, Willard Cope: Graphic Methods for Presenting Facts. New York: The Engineering Magazine Company, 1914.
- Brinton, Willard Cope: Graphic Presentation. New York: Brinton Associates, 1939.
- Bertin, Jacques: Semiologie Graphique (Semiology of Graphics). ESRI Press, 2010 (first edition 1967).
- Drucker, Joanna. Graphesis: Visual Forms of Knowledge Production. Cambridge: Harvard University Press, 2014
- Halpern, Orit. Beautiful Data: A History of Vision and Reason Since 1945. Duke University Press, 2015.
- Kitchin, Rob. The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. SAGE Publications: 2014.
- Mireilles, Isabel. Design for Information: An Introduction to the Histories, Theories, and Best Practices Behind Effective Information Visualizations. Rockport Publishers, 2013.
- Rumsey, Abby Smith. When We Are No More: How Digital Memory is Shaping Our Future. Bloomsbury Press, 2016.
- Strosberg, Eliane. Art and Science. Abbeville Press, 2015.
- Tufte, Edward. The Visual Display of Quantitative Information. Graphics Press, 1983.
- Tufte, Edward. Envisioning Information. Graphics Press, 1990.
- Tufte, Edward. Visual Explanations. Graphics Press, 1997.
- Zielinsky, Siegfried. Deep Time of Media: Toward an Archeology of Hearing and Seeing by Technical Means. MIT Press, 2008.
Recommended Web Sites
Thursday by appointment. E-mail arlduc [at] nyu.edu to make an appointment.
- 20% Phase 1 Project: Demonstration of prototype & brief write-up.
- 20% Phase 2 Paper: Research article & MLA-formatted bibliography.
- 25% Phase 3 Final: An ethnographic project drawing on skills and concepts developed in Phase 1 and 2.
- 20% Class participation, engagement, respect.
- 15% Weekly blog posts based on class discussion and project development. At least nine posts are required for the semester (three posts per class phase). These posts should be numbered (e.g. “Blog Post #1”). Blog posts of the Phase 1, 2, and 3 projects do not count towards the nine required weekly posts.
- Encouraged extra credit options:
- Expanded blogging
- Video documentation
- Project web site
- Conference paper
Attendance to all class sessions is mandatory. Class starts at 3:30 sharp. Excused absence requests, i.e. for a religious holiday or a conference, must be made at least 3 business days ahead of the scheduled absence. Emergency absences must be accompanied by official documentation, i.e. a doctor’s note or MTA notice. One letter grade drop will occur for every two unexcused late arrivals or one unexcused absence. For additional NYU School of Engineering Academic Policies and Requirements, please consult this link.
Technology Use in the Classroom: Participation, Engagement, Respect!
Laptop computers and other mobile devices are invaluable tools when used responsibly. However, this technology can also be incredibly distracting, especially in the classroom. When in class, you may use your laptops and other devices for any activities pertaining to the course: taking notes, researching material relevant to our readings and discussions, doing VFS homework, making class presentations, etc. However, if I sense that technology use is occurring at the expense of participation, engagement, and respect, I will require that all laptops and phones be stowed away. Also, during class screenings and class presentations, your laptops should not be used.
All work for this class must be your own and specific to this semester. Any work recycled from other classes or from another, non-original source will be rejected with serious implications for the student. Plagiarism, knowingly representing the words or ideas of another as one’s own work in any academic exercise, is absolutely unacceptable. Any student who commits plagiarism must re-do the assignment for a grade no higher than a D. In fact, a D is the highest possible course grade for any student who commits plagiarism. Please use the MLA style for citing and documenting source material.