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The Wonderful World of Data : Beyond the Basics

The Wonderful World of Data : Beyond the Basics

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The Wonderful World of Data : Beyond the Basics

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  1. The Wonderful World of Data : Beyond the Basics Anne Klein Barna, MA, Health Analyst Barry-Eaton District Health Department abarna@bedhd.org

  2. Outline • Data Visualization • Charts: Best Practices • Using data in coalition and program evaluation. • Social determinants and health equity data. • Techniques for problem prioritization. • New tools in data communication • Qualitative analysis techniques • Storytelling in prevention

  3. Visual communication Our eyes provide the greatest amount of information that is processed by our brains. Show, don’t tell!

  4. General Principles of Tufte http://www.youtube.com/watch?v=HfXSltlDfDw

  5. Grand Principles of Analytical Design (Edward Tufte) • Show comparisons. Compared with what? • Show causality. How did it happen? • Multivariate analyses. To clarify, add detail. • Choose the best mode. Integrate words and numbers. Don’t restrict yourself. Whatever it takes. • Document everything and tell people about it. Integrity, quality, and credibility are important. • Passion about the content. High quality and high integrity of the content.

  6. Books: Edward Tufte and Stephen Few

  7. Data Journalism Handbook (free) • http://datajournalismhandbook.org/

  8. Enhancing Visual Communications • Attract attention in an aesthetically pleasing way • Integrated into written and oral communications • Relevant and tailored to the audience • Gender, race, ethnicity, socioeconomics • Simplicity! Note this section adapted from: Communicating Public Health Information Effectively, 2002, Nelson et.al.

  9. Forms of Visual Communication

  10. Table • Great way to bore someone! • OK for lists • Must rapidly display a clear pattern Actual Causes of Death, 1990 Tobacco 400,000 Diet and Physical Activity 300,000 Alcohol 100,000 Firearms 35,000 Illicit Use of Drugs 20,000 But… a chart would still be better 

  11. Charts and Graphs Misconceptions Handout

  12. Random Dots

  13. Humans make into patterns!

  14. Anatomy of a (bad) Chart • http://www.excelcharts.com/blog/anatomy-of-a-bad-chart/

  15. So, what’s a good chart?

  16. Bar Charts • Display the magnitude of numbers • Compare groups • How many bars?

  17. BEWARE of messing with scales on bar charts!

  18. Stacked bar charts are hard to understand & decode

  19. Line Graphs • Very good at displaying trends over time • Problems include too many lines (use 4 or less unless chart is big and ) and poor labeling • Example:

  20. Data Visualization in Excel • http://www.excelcharts.com/blog/data-visualization-excel-users/ • This is an in-process online tutorial

  21. Origin Chart (number line)

  22. Bar chart version (separates values)

  23. Stack bars, then curve around…

  24. Fill in the doughnut hole…

  25. Add another dimension (Tree Map)

  26. Oh, the pie chart… • Google returns 2.2 million pie charts in image search, 1.8 million bar charts and only 0.34 million line charts; • Percentage of 3D pie charts in the first page: around 30%; • Percentage of pie charts with exploded slices: around 15%; • Bad pie charts (3D or exploded slices or legend or too many data points or no labels or unsorted slices): around 99%.

  27. Evolution of a pie

  28. HARD to compare: Area, Arc, and Angle

  29. Pie Chart Do’s and Don’ts • Don’t use 3D • Don’t explode your pies • Don’t use a legend • Don’t use too many chunks (groups of slices) • Label the slices • Sort the slices • Don’t compare pie charts

  30. Maps • Excellent when you have data sub-county • Google maps • www.michigantrafficcrashfacts.org • Census/American Factfinder • Most health departments and counties have GIS capability – but you must have geocoded data or addresses or rates for specific areas.

  31. Pictures Use as part of the design, not part of the chart.

  32. Typography & Good Design • Proximity • Alignment • Repetition • Contrast & White Space • Fonts You know it when you see it…or do you?

  33. Data-to-ink ratio • Examine your chart – what is the amount of ink used to display your data? What is the amount of ink used for other parts of the display? • The more ink used for data, the better. • Using Excel 2007 versus an earlier version will help do this for you.

  34. This is what the default chart looks like.

  35. A low data-to-ink ratio…

  36. Color and Shading • Cost • Warm colored objects set on cool backgrounds • Cultural meanings of colors • Shading can convey meaning, esp, maps • Consider the color blind – can they still read it? • Fill patterns

  37. Colors have meaning Gender Nationality Political Party Nature School Familiar Objects

  38. Colors have meaning

  39. Titles and Labels • Convey meaning with your title if possible • Label your axes unless it’s obvious or previously explained • Thoughts on legends? • Get rid of every other line that you can! • Date • Source

  40. Distortion • Sometimes the same data can be plotted with different y-value axes, and it looks very different. • Consider whether you are accurately displaying a trend if you “zoom in”.

  41. 3-D Charts Please don’t.

  42. This is not good.

  43. Pictograms or Glyphs http://www.fao.org/docrep/006/t7838e/T7838E06.htm

  44. Precise Representation of Distinct Individuals