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Seattle Data Visualization

Join the SeaVis Study Group at Getty Images for an evening exploring color basics and hands-on exercises in data visualization. Learn about color theory, color pairing, and tools for selecting colors. Enhance your data visualization skills with valuable insights and practical knowledge.

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Seattle Data Visualization

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  1. Seattle Data Visualization Study Group 3/19/2013

  2. SeaVisStudy Group Welcome to Getty Images! • Canned soft drinks and water are in the glass-fronted cooler in the kitchen; coffee and tea are also available (mugs are in the cabinet over the sinks) • Bathrooms: head to the elevators, walk past them; turn right for the Men’s bathroom (blue doorway), left for the Women’s (magenta doorway) • Internet access: getty-guest pwd: gettyguestnet

  3. Why a Study Group Enrico Bertini - Assistant Professor at  NYU-Poly in New York where he does research and teaches Information Visualizationsays: 1) Study a Lot2) Steal3) Criticize4) Produce5) Seek Discomfort http://fellinlovewithdata.com/guides/how-to-become-a-data-visualization-expert-a-recipe

  4. Tonight • Color: The guidelines • Break • Hands-on Exercise w/Color • Next Meet-up

  5. Color Basics - Jill Morton http://www.colormatters.com/color-and-design/basic-color-theory

  6. What colors go together? • ANALAGOUS colors • Any three colors that are side by side on a 12 part color wheel, such as yellow-green, yellow, and yellow-orange. Usually one of the three colors predominates. • COMPLEMENTARY colors • Two colors that are directly opposite each other, such as red and green and red-purple and yellow-green. These opposing colors create maximum contrast and maximum stability. • BASED ON NATURE • Nature provides a perfect departure point for color harmony. In the illustration red yellow and green create a harmonious design, regardless of whether this combination fits into a technical formula for color harmony. http://www.colormatters.com/color-and-design/basic-color-theory

  7. Color in data viz • What to look for & ask What colors do you see? How many different colors? What colors are used for the structure? For the labels? For the data? Are some colors similar vs. very different? Based upon how the colors are used, what does each color indicate? Can you quickly and accurately identify individual data elements and the relationship between elements? Is there a key/legend - can you tell which labels go with each color?

  8. Examples http://infoactive.co/design-vs-dev/ - Leah Clear Skies chart – Jeannine [on the next slide] http://persquaremile.com/2012/11/08/population-density-and-the-2012-presidential-election/- David http://exposedata.com/parallel/ - Sam http://hint.fm/projects/flickr/ - Nathan http://www.yasiv.com/facebook - Andrei Other examples http://www.informationisbeautiful.net/visualizations/colours-in-cultures http://evolutionofweb.appspot.com/ http://www.visualizing.org/full-screen/49090 http://www.visualizing.org/full-screen/44527 Awesome XKCD blog on color and gender: http://blog.xkcd.com/2010/05/03/color-survey-results/ Gorgeous datavis on men vs women color naming: based on XKCD’s color survey http://www.datapointed.net/2010/09/men-women-color-names/

  9. How to Read the Charts Each column represents a different hour. The two numbers at the top of a column is the time. A digit 1on top of a 3 means 13:00 or 1pm. It's local time, in 24hr format. Cloud cover: The colors are picked from what color the sky is likely to be, with Dark blue being clear. Lighter shades of blue are increasing cloudiness and white is overcast. Seeing: This line forecasts astronomical seeing. In bad seeing, planets might look like they are under a layer of rippling water and show little detail at any magnification, but the view of galaxies is probably undiminished. Bad seeing is caused by turbulence combined with temperature differences in the atmosphere. Transparency: The line forecasts the total transparency of the atmosphere from ground to space. It's calculated from the total amount of water vapor in the air. Darkness: This line shows when the sky will be dark, assuming no light pollution and a clear sky. Black is a dark sky. Deep blue shows interference from moonlight. Light blue is full moon. Turquoise is twilight. Yellow is dusk and white is daylight. Wind: Forecasts wind speed at about tree-top level. May affect your comfort and the type observing you might be limited to. Humidity: This forecasts ground-level relative humidity. Humidity variations can predict the likelihood of optics and eyepieces dewing. Temperature: Forecast temperatures near the ground. In falling temperature conditions, mirrors may require additional cooling to reach equilibrium and so prevent tube currents.

  10. Tools for picking colors • Color Brewer: http://colorbrewer2.org/ Sequential schemes – data that goes from low to high; light colors for low values, dark colors for high values Diverging schemes – equal emphasis on mid-range and extreme values; middle has light colors, extremes are dark colors in contrasting hues Qualitative schemes – no magnitude, just visual differences between classes • Kuler - https://kuler.adobe.com/#themes/rating?time=30

  11. BREAK – 10-15 MINUTES • Canned soft drinks and water are in the glass-fronted cooler in the kitchen; coffee and tea are also available (mugs are in the cabinet over the sinks) • Bathrooms: from here, head to the elevators, walk past them; turn right for the Men’s bathroom, left for the Women’s

  12. Hands-on Exercise: Color • Divide into groups of 2-3 & grab pen/pencil/crayon & 8.5 x 11 paper • Look at the data and think about ho w color was used in the examples; come up with some ideas, try them out • The data [roughly drawn from http://www.gapminder.org/world] • From 1980 to 2005, did self-employed women become more educated? • Is this different for different parts of the world? • Mean number of years of education for self-employed women • Region: North America, Africa, Europe, Asia/Pacific Rim

  13. More to read and look at • http://www.csc.ncsu.edu/faculty/healey/download/viz.96.pdf • https://graphics.stanford.edu/wikis/cs448b-09-fall/Color • http://infosthetics.com/archives/2009/09/how_to_make_data_visualizations_useful_for_color_blind_users.html • http://fellinlovewithdata.com/guides/dealing-with-a-keleidoscope-too-many-values-too-few-colors • http://www.research.ibm.com/people/l/lloydt/color/color.HTM • http://mkweb.bcgsc.ca/brewer/ • http://www.ifweassume.com/2012/12/colors-in-visualizations-rainbow-of.html • http://www.juiceanalytics.com/design-principles/limit-colors/ • http://my.safaribooksonline.com/book/software-engineering-and-development/9781449379889/4dot-color-the-cinderella-of-data-visualization/id2762155#X2ludGVybmFsX0h0bWxWaWV3P3htbGlkPTk3ODE0NDkzNzk4ODklMkZpZDMxODA1NzkmcXVlcnk9 • http://datajournalismhandbook.org/1.0/en/delivering_data_7.html • http://www.netmagazine.com/features/top-20-data-visualisation-tools

  14. What’s next • Next meeting - Bellevue • Weds March 27th • Topic: Sketching • Assignment:  Look at http://www.napkinacademy.com/ and get a few ideas about how to create sketches • April • Seattle - Mon April 15th • Bellevue - Weds April 30

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