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DATA + DESIGN where math and art collide Trina Chiasson, Infoactive | @trinachi | infoactive.co/data-design
This year, I did something new. I worked with 80+ volunteer contributors to write a 300-page ebook.
How do you write an open source ebook? It started with a message…
Hi Trina! Stats dork from Chicago here… Do you have any plans to include tutorials for basic data cleaning and data select...
This is what most statistics textbooks look like. Most designers say…
This isn’t for me
HAVE YOU EVER SEEN A BAD INFOGRAPHIC? 24.5%NO 84.5% YES
Why do so many infographics suck?
THEORY #1 Graphic Designers are evil. They sacrifice truthful data representation for aesthetic gain.
THEORY #2 Marketers are evil. They sacrifice truthful data representation for more clicks.
THEORY #3 Most people aren’t evil. Good data visualization is really hard to do.
Who has skills in programming, design, and data analysis?
Not everyone is blessed with the innate ability to make brilliant data visualizations.
What data collection looked like, not too long ago
What data storage looked like, not too long ago
What data collection looks like today
1985: The birth of Excel
But what do we do with all of this data?
The price of light is less than the cost of darkness. — Arthur C. Nielsen, Market Researcher & Founder of ACNielsen
Let’s send our data to a designer who can make it look pretty.
But most designers are not trained in stats
How about a friendly introduction to data?
But how do you write an open source ebook?
There’s no open source book on how to write an open source book.
Don’t underestimate the awesomeness of strangers on the internet.* * Especially strangers who volunteer to write books abo...
In six months, we wrote and released the English version. Data + Design is now being translated in Chinese, Russian, Spani...
Is it true that dataviz people hate pie charts?
That’s a complex question. Arguments against pie charts:
13% 100%
13%
A table is nearly always better than a dumb pie chart; the only worse design than a pie chart is several of them, for then...
Graphical perception: Theory, Experimentation, and Application to the Development of Graphical Models
Meat Pies & Color Theory
What Color Is This Chart? #TheChart
Hot Pie Cold Pie
Hot PieCold Pie
Men who cannot read this chart Men who can read this chart
So you should use monochromatic color scales, right?
Monochromatic scales are better for continuous data
Be careful with multicolor scales
Red tends to stand out against other colors
Can you find the red circle?
How about now?
Which is easier?
Finding boundaries in color vs. shapes Adapted from: Healey, Christopher G., Kellogg S. Booth, and James T. Ennis. “High-S...
So what colors should I use?
It depends, but…
BLUE & ORANGE
More visual trickery
Our brains look for baselines to compare distances
But the dark blue line is measured on a vertical scale.
Tricky, indeed.
Another example…
At first glance, you might think that the dark blue line decreased in value.
More visual trickery!
Same data, different story.
How do we make data more human?
US unemployment rate from 2007-2009
http://www.nytimes.com/interactive/2009/11/06/business/economy/unemployment-lines.html?_r=0
In 2008, the Sichuan Earthquake killed over 60,000 people in China.
“For seven years she lived happily on this earth” - Mother of an earthquake victim
How will you share your data?
DATA + DESIGN where math and art collide Trina Chiasson, Infoactive | @trinachi | infoactive.co/data-design
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
Data Design: Where Math and Art Collide
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Datos y análisis, Diseño, Tecnología
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16 de mar de 2015

Data Design: Where Math and Art Collide

Slides from SXSW 2015 session on the intersection of data and design:

http://schedule.sxsw.com/2015/events/event_IAP41090

By Trina Chiasson from https://infoactive.co

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Data Design: Where Math and Art Collide

  1. DATA + DESIGN where math and art collide Trina Chiasson, Infoactive | @trinachi | infoactive.co/data-design
  2. This year, I did something new. I worked with 80+ volunteer contributors to write a 300-page ebook.
  3. How do you write an open source ebook? It started with a message…
  4. Hi Trina! Stats dork from Chicago here… Do you have any plans to include tutorials for basic data cleaning and data selection techniques for users who may not have any statistics background? — Dyanna Gregory
  5. This is what most statistics textbooks look like. Most designers say…
  6. This isn’t for me
  7. HAVE YOU EVER SEEN A BAD INFOGRAPHIC? 24.5%NO 84.5% YES
  8. Why do so many infographics suck?
  9. THEORY #1 Graphic Designers are evil. They sacrifice truthful data representation for aesthetic gain.
  10. THEORY #2 Marketers are evil. They sacrifice truthful data representation for more clicks.
  11. THEORY #3 Most people aren’t evil. Good data visualization is really hard to do.
  12. Who has skills in programming, design, and data analysis?
  13. Not everyone is blessed with the innate ability to make brilliant data visualizations.
  14. What data collection looked like, not too long ago
  15. What data storage looked like, not too long ago
  16. What data collection looks like today
  17. 1985: The birth of Excel
  18. But what do we do with all of this data?
  19. The price of light is less than the cost of darkness. — Arthur C. Nielsen, Market Researcher & Founder of ACNielsen
  20. Let’s send our data to a designer who can make it look pretty.
  21. But most designers are not trained in stats
  22. How about a friendly introduction to data?
  23. But how do you write an open source ebook?
  24. There’s no open source book on how to write an open source book.
  25. Don’t underestimate the awesomeness of strangers on the internet.* * Especially strangers who volunteer to write books about data in their free time.
  26. In six months, we wrote and released the English version. Data + Design is now being translated in Chinese, Russian, Spanish, and French.
  27. Is it true that dataviz people hate pie charts?
  28. That’s a complex question. Arguments against pie charts:
  29. 13% 100%
  30. 13%
  31. A table is nearly always better than a dumb pie chart; the only worse design than a pie chart is several of them, for then the viewer is asked to compare quantities located in spatial disarray both within and between charts […] Given their low density and failure to order numbers along a visual dimension, pie charts should never be used. Edward Tufte, "The Visual Display of Quantitative Information”
  32. Graphical perception: Theory, Experimentation, and Application to the Development of Graphical Models
  33. Meat Pies & Color Theory
  34. What Color Is This Chart? #TheChart
  35. Hot Pie Cold Pie
  36. Hot PieCold Pie
  37. Men who cannot read this chart Men who can read this chart
  38. So you should use monochromatic color scales, right?
  39. Monochromatic scales are better for continuous data
  40. Be careful with multicolor scales
  41. Red tends to stand out against other colors
  42. Can you find the red circle?
  43. How about now?
  44. Which is easier?
  45. Finding boundaries in color vs. shapes Adapted from: Healey, Christopher G., Kellogg S. Booth, and James T. Ennis. “High-Speed Visual Estimation Using Preattentive Processing.” ACM Transactions on Computer-Human Interaction 3.2 (1996): 4.
  46. So what colors should I use?
  47. It depends, but…
  48. BLUE & ORANGE
  49. More visual trickery
  50. Our brains look for baselines to compare distances
  51. But the dark blue line is measured on a vertical scale.
  52. Tricky, indeed.
  53. Another example…
  54. At first glance, you might think that the dark blue line decreased in value.
  55. More visual trickery!
  56. Same data, different story.
  57. How do we make data more human?
  58. US unemployment rate from 2007-2009
  59. http://www.nytimes.com/interactive/2009/11/06/business/economy/unemployment-lines.html?_r=0
  60. In 2008, the Sichuan Earthquake killed over 60,000 people in China.
  61. “For seven years she lived happily on this earth” - Mother of an earthquake victim
  62. How will you share your data?
  63. DATA + DESIGN where math and art collide Trina Chiasson, Infoactive | @trinachi | infoactive.co/data-design

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