Readings

Here are the readings for each week. Please have the readings done before the corresponding class. I’ll try not to assign too much reading, so please come ready to discuss!

(Please also don’t make me regret saying this but in case you absolutely can’t get all the reading done, they’re in order of importance so start at the top each week.)

Week 2, Feb 7: encoding data to visuals; making meaning of data

• Wilke, C. (2019). Fundamentals of data visualization: A primer on making informative and compelling figures (First edition). O’Reilly. https://clauswilke.com/dataviz/ chapters 2 & 3
• Cairo, A. (2019). How charts lie: Getting smarter about visual information (First edition). W. W. Norton & Company. intro (on Blackboard)
• Pick one (or both. They’re both great, but if you only choose one I’d go with the podcast.):
  • Read D’Ignazio, C. (2015). What would feminist data visualization look like? https://civic.mit.edu/feminist-data-visualization.html
  • Listen to PolicyViz, Schwabish, J. (n.d.). Catherine D’Ignazio and Lauren Klein (142). Retrieved January 30, 2024, from https://policyviz.com/podcast/episode-142-catherine-dignazio-and-lauren-klein/
• Watch Kosara, R. (2019). The DataSaurus, Anscombe’s Quartet, and why summary statistics need to be taken with a grain of salt. https://www.youtube.com/watch?v=RbHCeANCbW0 (5 min video)

Week 3, Feb 14: writing good code; color

• Wilke, C. (2019). Fundamentals of data visualization: A primer on making informative and compelling figures (First edition). O’Reilly. https://clauswilke.com/dataviz/ chapters 4, 6, 17 (color, amounts, proportional ink)
• Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science: Import, tidy, transform, visualize, and model data (2nd edition). O’Reilly. https://r4ds.hadley.nz/ chapters 1 & 9 (okay to skim as a code reference) (data visualization, layers)

Week 4, Feb 21: text & annotation

• Pick one (or both) of these styleguides to browse through. Take note of how they use color, text, annotations, and iconography, and what suggestions they have on other elements (labeling axes, direct labeling of points, style of titles, placement and rotation of labels, etc). How do these rules carry over between chart types or for different audiences? How strongly do they create a recognizable visual style or brand?
  • Urban Institute (2023). Urban Institute Data Visualization style guide. http://urbaninstitute.github.io/graphics-styleguide/
  • World Health Organization (2023). WHO Data Design Language v.0.9.2. https://apps.who.int/gho/data/design-language/ (it’s not super clear but you have to click through to each section)

Week 5, Feb 28: uncertainty, distributions, making good decisions

• Listen to Data Stories, Bertini, E., & Stefaner, M. (n.d.). Visualizing Uncertainty with Jessica Hullman and Matthew Kay (134). Retrieved January 30, 2024, from https://datastori.es/134-visualizing-uncertainty-with-jessica-hullman-and-matthew-kay/
• Wilke, C. (2019). Fundamentals of data visualization: A primer on making informative and compelling figures (First edition). O’Reilly. https://clauswilke.com/dataviz/ chapters 7, 9, 10, 13, 14, 20 (okay to skim)
• Pick one. If you post on Discord or tell us in class what you learned, you’ll get 1 participation point:
  • Cairo, A. (2019). How charts lie: Getting smarter about visual information (First edition). W. W. Norton & Company. chapter 1 (on Blackboard)
  • Smith, N. (2023). How not to be fooled by viral charts. https://www.noahpinion.blog/p/how-not-to-be-fooled-by-viral-charts
  • Listen to Bertini, E., & Stefaner, M. (n.d.). Cognitive Bias and Visualization with Evanthia Dimara (116). Retrieved February 14, 2024, from https://datastori.es/116-cognitive-bias-and-visualization-with-evanthia-dimara/

Week 6, March 6: responsibility

Focus on finishing the case studies—only one reading and it’s a podcast

• Listen to Schwabish, J. (n.d.). Frank Elavsky (208). Retrieved February 29, 2024, from https://policyviz.com/podcast/episode-208-frank-elavsky/ and browse through the accessibility tool they discuss: Elavsky, F. (2022). The Chartability Workbook. In Chartability. https://chartability.github.io/POUR-CAF/

Week 7, March 13: accessibility and empathy

• I forgot to put last week’s podcast on Blackboard, so if you missed it please listen to it for this week
• Makulec, A. (2020). Ten Considerations Before you Create another Chart about COVID-19. In Nightingale. https://medium.com/nightingale/ten-considerations-before-you-create-another-chart-about-covid-19-27d3bd691be8
• BocoupLLC (2017). A Data Point Walks Into a Bar: Designing Data For Empathy - Lisa Charlotte Rost. https://www.youtube.com/watch?v=8XgF-RmNwUc
• Elghany, S. (2023). How Ethical Data Visualization Tells the Human Story. In Nightingale. https://nightingaledvs.com/ethical-data-visualization-tells-the-human-story/

Week 8, March 27: midterm projects

• Yau, N. (2017). One Dataset, Visualized 25 Ways. In FlowingData. https://flowingdata.com/2017/01/24/one-dataset-visualized-25-ways/

Week 9, April 3: intro to spatial data viz

• Wilke, C. (2019). Fundamentals of data visualization: A primer on making informative and compelling figures (First edition). O’Reilly. https://clauswilke.com/dataviz/ chapter 15
• Listen to Nussbaumer Knaflic, C. (n.d.). Maps with Kenneth Field (41). Retrieved March 27, 2024, from https://storytellingwithdata.libsyn.com/storytelling-with-data-41-maps-with-kenneth-field

Week 10, April 10: color, text, annotations part 2

• Muth, L. C. (2024). How to choose a color palette for choropleth maps. In Datawrapper. https://blog.datawrapper.de/how-to-choose-a-color-palette-for-choropleth-maps/
• Muth, L. C. (2024). How to choose an interpolation for your color scale - Datawrapper Blog. In Datawrapper. https://blog.datawrapper.de/interpolation-for-color-scales-and-maps/
• Listen or read (or both): O’Donohue, D. (n.d.). Communicating With Maps - The Art Of Cartography. Retrieved March 31, 2024, from https://mapscaping.com/podcast/communicating-with-maps-the-art-of-cartography/
• Lovelace, R., Nowosad, J., & Muenchow, J. (2019). Chapter 2 Geographic data in R. In Geocomputation with R. https://r.geocompx.org/spatial-class.html—skim / use just as a reference

Week 11, April 17: responsibility and context

• Sadler, R. C. (2016). How ZIP codes nearly masked the lead problem in Flint. In The Conversation. http://theconversation.com/how-zip-codes-nearly-masked-the-lead-problem-in-flint-65626
• Simmon, R. (2024). From Space to Story in Data Journalism, Nightingale. In Nightingale. https://nightingaledvs.com/from-space-to-story-in-data-journalism/
• Wiseman, A. (2015). Bad Maps Are Everywhere These Days. Here’s How to Avoid Being Fooled. Bloomberg CityLab. https://www.bloomberg.com/news/articles/2015-06-25/how-to-avoid-being-fooled-by-bad-maps

Week 12, April 24: storytelling with maps

• Seaberry, C. (2018). CT Data Story: Housing Segregation in Greater New Haven. DataHaven. https://ctdatahaven.org/reports/ct-data-story-housing-segregation-greater-new-haven (sorry for assigning my own project, just skim it)
• John Nelson Maps (2023). Reimagining a Classic Cheysson Map. https://www.youtube.com/watch?v=ibHkrK42uok
• Abbasi, O. (2022). Dr. Lawrence Brown: Baltimore’s Black Butterfly and White L. https://www.tableau.com/foundation/data-equity/economic-power/black-butterfly-baltimore