152 | Year in Review 2019
Data Stories
Data Science and Data Visualization Are Never Neutral
i-Reporter: Data science and data visualization are never neutral. The challenge is to ask how we might make different decisions that could help work towards justice. There's been some great wor in this area in the past year, i'll turn to my three exciting developments. A new book by doctor ruha benjamin shows us how these algorithms are all part of a larger system.
Hi everyone! We are once again at the end of a whole year. After having “end of the year episodes” with other podcasters, going around the world, and chatting with Andy and Robert, we decided to try something different this time: we asked a group of data visualization professionals to send us an audio snippet summarizing what happened in specific areas of the field over the last year. The result is a great multifaceted collage of stories and personalities. See below who we have interviewed and what they talked about.
Happy New Year! Thanks so much for listening to the show. We’ll see you in 2020 with a whole set of great new episodes!
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Links:
Alberto Cairo on Data literacy
- Nightingale, a publication edited by the Data visualization society
- Improvement of free or freemium tools: Datawrapper, Flourish. Crowdsourcing of RawGraphs successful
- New popularizing books: Ben Jones’ “Avoiding Data Pitfalls” / Stephanie Evergreen’s “Data Visualization Sketchbook” / Cole Nussbaumer’s “Storytelling with Data: Let’s Practice!” / Alberto Cairo’s own: “How Charts Lie”
- The pace at which podcasts such as yours publish (not a new development, but still)
- Andy Kirk’s Little of Visualization design series (ongoing effort)
- Alberto’s own recent MOOC (12,000+ people)
- Upcoming conferences: IRE-NICAR, Malofiej, Computation+Journalism, the Data Visualization Society conference, etc.
Amelia Wattenberger on Learning data visualization from a newcomer’s perspective
- Data visualization society
- Figma (UI design tool)
- Lots of free tutorials and ways to get started in data viz
- Amelia’s bird’s eye view of the library
- Challenge: awareness about where data comes from!
- The erroneousness of considering data as “facts”
- Show how data can be biased or misconstrued
Andy Kirk on Data tools
- The acquisition of Looker by Google
- Flourish
- Data Wrapper
- Raw Graphs (fundraising for v2.0)
- Challenge:
- Data illustrator and Charticulator did not develop further
- How do you create outputs for multiple platforms
- More techniques to explore more encodings
David Bauer on Data Journalism
- Bar chart races!
- From data-driven to data-inspired stories (more about people behind the data)
- New focus on climate change / showing the data does not do the trick
- Teams invest in tools! + role of Data Wrapper
- David’s newsletter “Weekly Filet”
Elijah Meeks on Data viz within the industry
- Data visualization hitting the mainstream
- First datavis president / Trump interested in the actual chart
- Michelle Rial / beyond coffee table books / “Data Humanism”
- Giorgia Lupi and her fashion line
- Data vis no longer only a supplemental skill
- No longer see the development of many new types of visualizations, we are more optimizing what we have
- Not only limited to technical people
Jen Christiansen on Science communication
- Scientists and designers are now speaking the same language!
- Visualization by Nadieh Bremer: In Many Places, the Sun Peaks Well after 12:00
- Beyond data as “truth”, even in science! [Postmodern Data Science?]
- Article in Scientific American: How to Get Better at Embracing Unknowns, by Jessica Hullman
- Warming Stripes by Ed Hawkins
Jessica Hullman on Viz research
- Research
Pierre Dragicevic et.al. Explorable Multiverse Analyses
J. Hullman, P. Resnick, E. Adar. Hypotetical Outcome Plots
N. McCurdy. Making room for implicit error: a visualization approach to managing data discrepancy
N. McCurdy, M. Meyer. IEEE TVCG 2019. A Framework for Externalizing Implicit Error Using Visualization
Yea Seul Kim, K. Reinecke, J. Hullman. Explaining the Gap: Visualizing One’s Predictions Improves Recall and Comprehension of Data
Yea Seul Kim, L. Walls, P.M. Krafft, J. Hullman. A Bayesian Cognition Approach to Improve Data Visualization. ACM CHI 2019
Michael Correll. Vis for Digital Humanities Workshop Keynote (at IEEE VIS 2019) - Book
Data Feminism, by Catherine d’Ignazio and Lauren F. Klein
Lauren Klein on Data ethics
Book: Data Feminism, by Catherine d’Ignazio and Lauren F. Klein
- Biased algorithms
- Series of events: The new Jim Code
- ACM Fairness and Accountability Group + CRAFT Conference
- Kate Crawford’s Anatomy of an AI system
Maarten Lambrecht on Xenographics
- Some of the charts in the xenographics collection pop up in the wild
- Unsolved issue: data visualisation in education, both at lower as in higher levels of education
- Tools: RAWGraphs
Maral Pourkazemi on Diversity and inclusion
- Gender diversity in the field (women in the field lead a lot!)
- More empowered. Taken more into consideration.
Mitchell Whitelaw on Viz localism
- Renewed attention to local data practices
- John Thackara: Bio-regional design
Paolo Ciuccarelli on Visualization & design
- Interest in design as a discipline
- Shift towards the human
- Automating design
- Data literacy
- Tools: Raw Graphs (fundraising for v2.0)
Thomas Dahm on Data viz conferences
- Us by Night (Belgium)
- Beyond Tellerrand (Germany)
- Offf Barcelona
- (Relatively) good mix of speakers
- Dataviz speakers booked as speakers at more general design conferences
- Agencies do conferences
- Unsolved challenges:
- Swag
- Sponsors
- Thomas Dahm’s Neon Moiré
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