Dataviz Pres1109

Data Monica Bulger & Aaron Sobel Visualization Bren School of Environmental Science & Management University of California, Santa Barbara November 30, 2009

Data visualized in 2009 Group Projects --images from “Pathway to Self-Funding,” “Cumulative Impacts of Large-Scale Renewable Energy Development in the West Mojave,” “Management Recommendations for Piute Ponds, EAB,” and “Post-Fire Sedimentation and Flood Risk Potential in the Mission Creek Watershed of Santa Barbara,” available at http://www.bren.ucsb.edu/research/gp2009.htm

Who is your audience?

What is your inform purpose? request advocate publicize what else?

Re-think how you represent data

--image from visualcomplexity.com

Think visually --image from visualcomplexity.com

What is your data story? --image from Design & the Elastic Mind exhibition, “The Million Dollar Blocks Project” available at http://www.moma.org

“Data slides aren’t really about data. They are about the meaning of data.” --Duarte (2008)

Maps tell stories visually

--image from Design & the Elastic Mind exhibition, “New York Talk Exchange” available at http://www.moma.org

Barack Obama Personal Visits by State Jan 2007 - Feb 2008 --Image created by graduate students in UCSBʼs Geography Department using data from IGERT “Issue Browser” project (2009)

3-D allows for visualizing complex data -- Time Magazine http://www.time.com/time/covers/20061030/where_we_live/

Visualizing the distance to the nearest McDonald’s --Image from infosthetics, available at http://www.infosthetics.com

Stacked Graph Each colored layer represents a musician, progressing from left to right through the eighteen month span growing wider when listening was more frequent, and skinnier when it was not. -- Stacked Graph http://www.leebyron.com/what/lastfm/

Maps in practice

Sample GP brief Where is the Cuyama Valley located? -- Anderson, C., Dobrowski, B., Harris, M., Moreno, E., Roehrdanz, P. (2009). Conservation Assessment for the Cuyama Valley (Project Brief). Bren School of Environmental Science and Management, University of California, Santa Barbara. Available at http://www.bren.ucsb.edu/research/gp2009.htm

Draw the viewer’s attention

Focus on key points -- image from Duarteʼs (2008) slide:ology, p. 69

Find the best ﬁt for representing your data visually

Pie charts vs. bar graphs proportion comparison -- image from Dutton, W.J. & Helsper, E.J. (2007). Oxford Internet Survey 2007 Report: The Internet in Britain. Oxford Internet Institute, UK.

What does the pie chart tell us? What information is missing? --Adlerman, D., Maciejowski, N., Randall, J., Shirley, R. (2009). Management Recommendations for Piute Ponds Edwards Air Force Base, California (Project Poster). Bren School of Environmental Science and Management, University of California, Santa Barbara. Available at http://www.bren.ucsb.edu/research/ gp2009.htm

Decision trees can show the viewer why you chose the path you did --Image from FlowingData, available at http://www.ﬂowingdata.com

activity In groups of 3, improve the following image. --Consider what it’s trying to say --Identify necessary vs. extraneous information --How can you clarify the information and make it more meaningful for your target audience(s)?

Sample GP diagram -- Anderson, C., Dobrowski, B., Harris, M., Moreno, E., Roehrdanz, P. (2009). Conservation Assessment for the Cuyama Valley (Project Brief). Bren School of Environmental Science and Management, University of California, Santa Barbara. Available at http://www.bren.ucsb.edu/research/gp2009.htm

GP diagram in context --Hess, L. , Johnson, P., Karasek, T., Port-Minner, S., Radhakrishnan, U. (2009). Pathway to Self-Funding: A Case Study on the Calfornia Commerical Spiny Lobster Fishery (Project Poster). Bren School of Environmental Science and Management, University of California, Santa Barbara. Available at http://www.bren.ucsb.edu/research/gp2009.htm

“Slides should be processed in 3 seconds or less. It’s impossible for people to process your slides and your words

Notes from discussion: * avoid noise: colors that don’t make sense, shapes that aren’t signiﬁcant, arrows that don’t serve a purpose. This information can be mis-interpreted. * if linear relationship, show it, don’t complicate it with unnecessary info. * is it necessary for each box to be separate? Combine related information.

* Thank you to Jim Frew and Darren Hardy for sharing their expertise throughout the workshop.

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Dataviz Pres1109

by Monica Bulger on Dec 01, 2009

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Dataviz Pres1109 — Presentation Transcript