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Similar to Stronger Research Reporting Using Visuals
Stronger Research Reporting Using Visuals
- 1. Stronger Research ReportingUsing Visuals Applying Visual Design for Better Research – VCU Workshop 5th October, 2011
- 2. We live ina time of unprecedented Information Overload 2
- 3. “ The highest-paidperson in the first half of the next century will be the ‘storyteller.’ ” Rolf Jensen, 1996 3
- 4. As Story-tellers, welearn.. To write for the reader, not for yourself 4
- 5. As Story-tellers, welearn.. To write for the reader, not for yourself A story needs a logical flow 5
- 6. As Story-tellers, welearn.. To write for the reader, not for yourself A story needs a logical flow To have a point of view 6
- 7. As Story-tellers, welearn.. To write for the reader, not for yourself A story needs a logical flow To have a point of view Only to report data that is vital to telling the story 7
- 8. How can visualshelp in storytelling? Attention The eyes are drawn like a magnet to images. Less cognitive processing required, especially if Comprehension image is familiar. Complexity Best way to summarise / represent complexity. Can reveal patterns and relationships that would Understanding otherwise be hard to interpret or spot Retention Presence of illustrations significantly improves retention. Aesthetics What’s wrong with wanting it to look good? Timing Graphics reduce time required to explain. Pictures do a far better job of communicating Emotion emotion, and emotion does a far better job of inspiring action. 8
- 9. Types of Visuals Graphs Illustrations Data Viz 9
- 10. “Best 100 non-fiction booksof the twentieth century” - amazon.com
- 12. Graphs “When a graph is made, quantitative and categorical information is encoded by a display method. Then the information is visually decoded. This visual perception is a vital link. No matter how clever the choice of the information, and no matter how technologically impressive the encoding, a visualization fails if the decoding fails.” (William S. Cleveland, The Elements of Graphing Data, Hobart Press, 1994, p. 1)
- 13. To 3D ornot to 3D? 5 4 3 Series 1 2 Series 2 1 Series 3 Series 3 0 Series 1
- 14. To 3D ornot to 3D? 6 4 2 0 Series 1 Series 1
- 15. To 3D ornot to 3D? 6 4 2 0 Series 1 Series 1
- 16. To 3D ornot to 3D? 6 4 2 Series 1 0 Category Category Category Category Series 1 1 2 3 4
- 17. To 3D ornot to 3D? 6 4 Series 1 2 0
- 18. To 3D ornot to 3D? 6 4 Series 1 2 0
- 19. To 3D ornot to 3D? 6 4 Series 1 2 0
- 20. To 3D ornot to 3D? 6 5 4 Series 1 3 Series 2 2 Series 3 1 0 Category 1 Category 2 Category 3 Category 4
- 21. Losing Perspective 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
- 22. Losing Perspective 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
- 23.
- 24. Areas, Volumes andMagnitudes 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Category 1 Category 2 Category 3 Category 4
- 25. Areas, Volumes andMagnitudes 1 0.9 0.8 Ratio of size from Cat 1 to 2 is 1:2 0.7 BUT ratio or shape area is 1:4 0.6 0.5 0.4 0.3 0.2 0.1 0 Category 1 Category 2 Category 3 Category 4
- 26. Areas, Volumes andMagnitudes
- 27. Areas, Volumes andMagnitudes 1 0.5 0 Category 1 Category 2 Category 3 Category 4 Lie factor = size of effect shown in graphic / size of effect in data
- 28. Areas, Volumes andMagnitudes
- 29. Areas, Volumes andMagnitudes 14 12 10 Series 3 8 Series 2 6 Series 1 4 2 0 Category 1 Category 2 Category 3 Category 4
- 30. Areas, Volumes andMagnitudes
- 31. Who ate allthe Pies? Sales 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
- 32. Who ate allthe Pies? Sales 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
- 33. Who ate allthe Pies? Sales 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
- 34. Who ate allthe Pies? We make angle judgments when we read a pie chart, but we don’t judge angles very well. These judgments are biased; we underestimate acute angles (angles less than 90°) and overestimate obtuse angles (angles greater than 90°). (Naomi Robbins, Creating More Effective Graphs, Wiley, 2005, p. 49)
- 35. Who ate allthe Pies?
- 36. Who ate allthe Pies? Sales 1st Qtr 8% 17% 2nd Qtr 3rd Qtr 4th Qtr 22% 58%
- 37. Who ate allthe Pies? Q1 Q2 8% 8% 13% 17% 17% 22% 58% 62% Q3 Q4 9% 8% 10% 6% 23% 26% 60% 58%
- 38. Who ate allthe Pies? 70% 60% 50% Apples 40% Oranges 30% Bananas 20% Grapefruit 10% 0% Q1 Q2 Q3 Q4 Hollands and Spence found that trends are best analyzed with line graphs than with a series of pie charts. When estimating trends with line graphs, people can use a slope estimation procedure; with pie charts, they must perform multiple size discriminations between pie slices. Hollands JG, Spence I. Judgments of change and proportion in graphical perception. Hum Factors 1992;34:313-34.
- 39. Chart Junk &Data Ink 5 Category 4 2.8 4.5 3 Category 3 1.8 3.5 2 Category 2 4.4 2.5 2 Category 1 2.4 4.3 $0.00 $1.00 $2.00 $3.00 $4.00 $5.00 $6.00
- 40. Chart Junk &Data Ink Lipkus I M , Hollands J G J Natl Cancer Inst Monogr 1999;1999:149-163, Oxford University Press Gillan and Richman found that participants had faster response times and were more accurate when the data-ink ratio was high than when it was low. In addition, integrated tasks (e.g., global comparisons or synthesis judgments) appear to be more affected by the data-ink ratio than are focused tasks (e.g., selecting the value of a data point). Gillan DJ, Richman EH. Minimalism and the syntax of graphs. Hum Factors 1994;36:619-44
- 41. Chart Junk &Data Ink
- 42. Recap… Data Integrity –avoid: 1. 3 dimensional treatments 2. Tricks of perspective 3. Lie-factors of area or volume 4. Too many pies Data Clarity – avoid: 5. Unnecessary clutter 6. A low data-to-ink ratio
- 43. Tufte’s 5 principlesof GOOD information design 1. Enforce visual comparisons between groups 2. Show or suggest causality 3. Show multivariate data (more than 2 dimensions) 4. Content driven—all about explaining the data 5. Completely integrate words, numbers and images
- 44. 1. Enforce comparison In other words, we must always ask the question, “compared to what?”. Fortunately, visual comparison is faster and easier than mathematical or conceptual comparison: “visualization made it possible to see the effects of design changes on the pressure distribution of an airplane wing, for example. The same thing could be done with number crunching in theory, but it was a lot more immediate and obvious where things went wrong when the model was actually shown as an image” - Robert Kosara, http://stat-computing.org/newsletter/issues/scgn-22-1.pdf
- 45. 1. Enforcecomparison London’s Daily Greenhouse Gas Contribution 139 thousand tonnes of carbon dioxide would fill a sphere 521 metres across. To most Londoners, '139 thousand tonnes of carbon dioxide' is not a very meaningful quantity. Illustrating it in the context of London landmarks allows viewers to make it meaningful for themselves. simplified version
- 46. 1. Enforce comparison NewYork Weather for 1980 1980’s weather is compared against ‘normal’ weather averages allowing you to immediately spot points of difference. simplified version
- 47. 2. Suggest Causality Withoutan indication of cause, you can be left wondering what the point is. i.e. if you show a trend, it begs the question, why is this happening?
- 48. 2. Suggest Causality http://youtu.be/pLqjQ55tz-U
- 49. 3. Show MultivariateData The world we seek to understand is multivariate. The more variables, the more opportunities we have to see relationships and patterns simplified version
- 50. 3. Show MultivariateData New York Weather for 1980 3 Dimensions:- - Temperature - Precipitation - Humidity simplified version
- 51. 3. Show MultivariateData Increase in oil consumption oil consumption (Y axis) by year (X axis) and region (stacked area)
- 52. 3. Show MultivariateData Increase in oil consumption oil consumption (Y axis) by year (X axis) and region (stacked area) “Small Multiples” Also called Trellis / Lattice / Grid / Panel Chart
- 53. 3. Show MultivariateData How BI Customers Use their Platforms Platforms, by type of usage, by volume
- 54. 3. Show MultivariateData How BI Customers Use their Platforms Platforms, by type of usage, by volume
- 55. 3. Show MultivariateData Canadians think it time for a change of government, if they don’t see the Government as being on the right track. And their vote intentions tend to reflect that. NF Size of the circle is the amount of approval of the premier/PM SK Colour of Circle indicates vote difference • Dark green = 15+ vote lead, • Light green is 5-14 lead, Right Track • White = +/- 5% lead/trail, • Red= 5-14 trail & dark red (no example here) is AB trail by 15 or more MN BC PQ NB ON NS PEI Feds Time for Change
- 56. 3. Show MultivariateData
- 57. 4. Content-Driven If thereare elements that don’t serve the purpose of explaining the data, they are probably chart junk.
- 58. 4. Content-Driven New YorkWeather for 1980 There is nothing on here that is irrelevant
- 59. 4. Fully integratewords, numbers and images Aim for the viewer to be able to take in the whole picture in one glance, so avoid separate, complex legends which need to be continually referenced to make sense of the data
- 60. 4. Fully integratewords, numbers and images New York Weather for 1980 Key annotations are present right within the chart simplified version
- 61. 4. Fully integratewords, numbers and images Distinct Segments driven by exposure interactions and psychographic engagement Key annotations are present right within the chart
- 62. Napoleon’s March onMoscow illustrates the principles Enforce visual Completely comparisons — integrate the width of the words, tan and black numbers and lines gives you an images—in this immediate map, number comparison of the sit comfortably size of with words and Napoleon’s army the only legend at different times is a scale to during the march give a sense of distance The design should be Show content-driven — multivariate Napoleon’s March data — was designed as Napoleon’s an anti-war March shows poster…the six: army size, designer was location (in 2 passionate about dimensions), the information direction, time, being presented. and The point of the temperature poster wasn’t the Show causality — the map shows how the design, it was the temperature and river crossings defeated Napoleon. information. simplified version
- 63. Quiz: Does thismeet all of the criteria? simplified version
- 64. Data Visualization “Statistics journals rarely cover graphical methods… Outside of statistics, though, infographics and data visualization are more important. Graphics give a sense of the size of big numbers, dramatize relations between variables, and convey the complexity of data and functional relationships… sometimes to more efficiently portray masses of information that their audiences want to see in detail (as with sports scores, stock prices, and poll reports), sometimes to help tell a story (as with annotated maps), and sometimes just for fun:.” - Visualization, Graphics, and Statistics, Andrew Gelman and Antony Unwin, Statistical Computing & Graphics, July, 2010
- 65. Data Visualization Info-graphics Dynamic Data Visualization Dashboards
- 66. Info-graphics Summarizecomplex information using both decorative as well as data-driven visual elements
- 67.
- 68.
- 69. Dynamic Data Vizualisation Uses motion or other interactive elements to allow the user themselves to explore a dataset for insight
- 70. Dynamic Data Vizualisation -Some tools becoming available Many Eyes, (www.many-eyes.com)
- 71. Dashboards Summarizekey statistics into one page or panel of charts
- 72.
- 73. Dashboards Using Excel ‘Slicers’for a Dynamic Dash MY AWESOME DASHBOARD Gross Profit Total Sales 90000 80000 Salesperson 5 70000 60000 Salesperson 4 50000 40000 Sum of Total GP Salesperson 3 Sum of Total GP 30000 Sum of Total sales Sum of Total sales 20000 Salesperson 2 10000 0 Salesperson 1 0 50000 100000 150000 200000 Month Product Salesperson Jan-09 Product A Salesperson 1 Feb-09 Product B Salesperson 2 Mar-09 Product C Salesperson 3 Apr-09 Product D Salesperson 4 May-09 Salesperson 5 Jun-09 Jul-09
- 74. Illustrations “Askyourself this: What information are you representing with the written word on a slide that you could replace with a photograph (or other appropriate image or graphic)?.. Images are powerful, efficient and direct. Images can also be used very effectively as mnemonic devices to make messages more memorable. If people cannot listen and read at the same time, why do most PowerPoint slides contain far more words that images? … It takes the realization that modern presentations with slides and other multimedia have more in common with cinema (Images and narration) …than they do with written documents.” - Presentation Zen, Garr Reynolds, 2008
- 75. Illustrations Use of decorative, non-data driven images to add meaning to your reporting. Source images from good Use images along with quality, legal sources bold words to make your headline points Think like a designer: Simple, bold, colour-matched to your palette, Rule of 3rds For memorability or to emphasise a point pick an But you don’t need to be image that has an one: a tonne of image emotional appeal cute, manipulation tools right in comical, evocative PowerPoint. Don’t be afraid to try!
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- 88. That’s all folks!Questions? Contact me: Laura Davies, SVP Laura.davies@visioncritical.com 88