Data Storytelling Studio: Climate Change

Readings

This page lists the readings assigned for each class session. Readings are due at the given session. Graduate students are expected to complete additional readings as listed.

SES #	TOPICS	READINGS
Finding and telling stories with data
1	Introduction
2	Course overview & asking questions
3	Course overview & asking questions (contd.)	Burghart, B. “What I’ve Learned from Two Years Collecting Data on Police Killings.” Gawker, August 2014. Veltman, N. “Scraping the web.” School of Data, November 2013. Graduate Readings Gurin, J. “Open Governments, Open Data: A New Lever for Transparency, Citizen Engagement, and Economic Growth.” SAIS Review of International Affairs 34 no. 1 (2014): 71–82. Gurstein, M. B. “Open data: Empowering the empowered or effective data use for everyone?” First Monday 16 no. 2 (February 2011).
4	Getting and cleaning data	Nguyen, D. “Chapter 1: Using Google Refine to Clean Messy Data.” ProPublica, December 30, 2010. DeBarros, A. “Excel: Extract text with FIND and MID.” on data, code & product [blog], October 9, 2012. Graduate Reading Wickham, H. “Tidy Data.” (PDF) Journal of Statistical Software 59 no. 10 (August 2014).
5	Analyzing data	Abelson, R. “Making Claims with Statistics.” Chapter 1 in Statistics as Principled Argument. Psychology Press, 1995. ISBN: 9780805805277. pp. 1–16. [Preview with Google Books]  Gonick, L. and W. Smith. The Cartoon Guide to Statistics. HarperPerennial, 1993. [Preview with Google Books] Gray, J., L. Chambers, and L. Bounegru. “Start with the Data, Finish with a Story” and “Data Stories.” In The Data Journalism Handbook. O’Reilly Media. British Psychological Society (BPS). Dancing Statistics. YouTube playlist, 4 videos. March 5, 2015. Robbins, N. “When Should I Use Logarithmic Scales in My Charts and Graphs?” Forbes Tech, January 19, 2012. Graduate Readings (focus on high-level takeaways) Koschinsky, J. “Data Science for Good: What Problems Fit?” (PDF) (Courtesy of Julia Koschinsky. License CC BY) Brennan, M. “Can computers be racist? Big data, inequality, and discrimination.” Ford Foundation, 2015. Leskovec, J., A. Rajaraman and J. D. Ullman. “Data Mining.” (PDF) Chapter 1 (author’s manuscript version) in Mining of Massive Datasets. Cambridge University Press, 2011. (Skip the complicated math parts.)
6	Telling a data-driven story	Zer-Aviv, M. “Disinformation Visualization: How to lie with datavis.” Visualizing Information for Advocacy (blog), January 31, 2014.  Edward R. Tufte. “Graphical Excellence.” Chapter 1 in The Visual Display of Quantitative Information. 2nd edition. Graphics Press, 2001. ISBN: 9781930824133. pp 13-52 Graduate Readings  Ware, C. “Visual and Verbal Narrative.” Chapter 7 in Visual Thinking for Design. Morgan Kaufmann, 2008. ISBN: 9780123708960. [Preview with Google Books] Segel, E., and J. Heer. “Narrative visualization: Telling stories with data.” (PDF - 1.4MB) IEEE Transactions on Visualization and Computer Graphics 16, no. 6 (2010): 1139-48.
Sketch 1: Charts and creative charts
7	Overview	McCloud, S. “Vocabulary of Comics.” Chapter 2 in Understanding Comics: The Invisible Art. William Morrow Paperbacks, 1994. ISBN: 9780060976255 Wilson, M. “Why You Don’t Make A Mindlessly Beautiful Visualization Of A Horrific Event.” Co.Design, August 8, 2015. Holmes, N. “why so serious.” YouTube. Aug. 31, 2009 Graduate Reading Pandey, A. V., K. Rall, M. L. Satterthwaite, O. Nov, and E. Bertini. “How Deceptive are Deceptive Visualizations?: An Empirical Analysis of Common Distortion Techniques.” Proceedings of the ACM Conference on Human Factors in Computing Systems 2015. NYU School of Law, Public Law Research Paper No. 15-03, Feb. 18, 2015.
8	Studio (in class work time)	Groeger, L. “Design Principles for News Apps & Graphics.” ProPublica, May 30, 2013.
9	Presentations and discussion
Sketch 2: Data sculptures
10	Overview	Blair, J. A. “The Rhetoric of Visual Arguments.” Chapter 2 in Defining Visual Rhetorics. Lawrence Erlbaum, 2014. ISBN: 9780805844023. pp. 41-61. Bertini, E., and M. Stefaner. Episode 17, “Data Sculptures.” Data Stories [podcast]. (Listen to the first 27 minutes.) Jansen, Y., P. Dragicevic, J. A. Isenberg, et al. “Opportunities and Challenges for Data Physicalization.” (PDF - 1.6MB). Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI), April 2015. Graduate Readings Yvonne Jansen, Kasper Hornbæk. “A Psychophysical Investigation of Size as a Physical Variable.” (PDF - 10MB) IEEE Transactions on Visualization and Computer Graphics 22 no. 1 (2016): 479 - 488.
11	Studio (in class work time)
12	Presentations and discussion
Sketch 3: Personal stories
13	Overview	boyd, d. “What World Are We Building?" Data and Society: Points, January 2016. Slobin, S. “What if Data Visualization is Actually People?” OpenNews: Source, April 2014. DuBois, R. L. “Insightful human portraits made from data.” TED Talk (video), February 2016.
14	Studio (in class work time)
	Optional: evening round-table with Emerson College and Northeastern University students
15	Presentations and discussion
Sketch 4: Participatory data games
16	Overview	Hart, V. and N. Case. Parable of the Polygons. Gordon, E., S. Walter, and P. Suarez. Engagement Games Guidebook. Engagement Lab at Emerson College and Red Cross/Red Crescent Climate Centre, 2016. Graduate Reading Valkanova, N., R. Walter, A. Vande Moere, and J. Müller. “MyPosition: Sparking Civic Discourse by a Public Interactive Poll Visualization.” In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW ’14). ACM (2014): 1323–1332.
17	Studio (in class work time)
18	Presentations and discussion
Sketch 5: Maps and creative maps
19	Overview	Ericson, M. “When Maps Shouldn’t Be Maps.” Blog post (October 2011). Sack, C. “A #NoDAPL Map.” Northlandia (November 1, 2016). Gamio, L. “Election maps are telling you big lies about small things.” Washington Post, November 1, 2016. Graduate Reading D’Ignazio, C. “What Would Feminist Data Visualization Look Like?” MIT Center for Civic Media, December 2015.
20	Studio (in class work time)
21	Presentations and discussion
Final project studio
22	Group forming
23	Mentor feedback, studio (in class work
24	Studio (in class work time)
25	Final project presentations and discussion