Your SlideShare is downloading. ×
0
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Dataviz Pres1109
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Dataviz Pres1109

439

Published on

Published in: Education, Business, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
439
On Slideshare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • Transcript

    • 1. Data Monica Bulger & Aaron Sobel Visualization Bren School of Environmental Science & Management University of California, Santa Barbara November 30, 2009
    • 2. 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
    • 3. Who is your audience?
    • 4. What is your inform purpose? request advocate publicize what else?
    • 5. Re-think how you represent data
    • 6. --image from visualcomplexity.com
    • 7. Think visually --image from visualcomplexity.com
    • 8. What is your data story? --image from Design & the Elastic Mind exhibition, “The Million Dollar Blocks Project” available at http://www.moma.org
    • 9. “Data slides aren’t really about data. They are about the meaning of data.” --Duarte (2008)
    • 10. Maps tell stories visually
    • 11. --image from Design & the Elastic Mind exhibition, “New York Talk Exchange” available at http://www.moma.org
    • 12. 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)
    • 13. 3-D allows for visualizing complex data -- Time Magazine http://www.time.com/time/covers/20061030/where_we_live/
    • 14. Visualizing the distance to the nearest McDonald’s --Image from infosthetics, available at http://www.infosthetics.com
    • 15. 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/
    • 16. 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/
    • 17. Maps in practice
    • 18. 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
    • 19. Draw the viewer’s attention
    • 20. Focus on key points -- image from Duarteʼs (2008) slide:ology, p. 69
    • 21. Find the best fit for representing your data visually
    • 22. 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.
    • 23. 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
    • 24. Decision trees can show the viewer why you chose the path you did --Image from FlowingData, available at http://www.flowingdata.com
    • 25. 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)?
    • 26. 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
    • 27. 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
    • 28. “Slides should be processed in 3 seconds or less. It’s impossible for people to process your slides and your words
    • 29. 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.
    • 30. * Thank you to Jim Frew and Darren Hardy for sharing their expertise throughout the workshop.

    ×