Visualizing Data (Department of Commerce - OUSEA)

Data Visualization: Using Data to Tell a Story
December 13, 2023
Aaron Chafetz, Tim Essam, and Karishma Srikanth | USAID

Presented to DoC's Office of the Undersecretary for Economic Affairs (OUSEA) and Chief Data Officers

Transcript

1. Visualizing Data Using Data to Tell a Story December 13, 2023 | United States Department of Commerce Office of the Under Secretary for Economic Affairs
2. 2 This presentation was made possible by the support of the American people through the United States Agency for International Development (USAID) under the U.S. President's Emergency Plan for AIDS Relief (PEPFAR). The contents in this presentation are the sole responsibility of the authors, and do not necessarily reflect the views of USAID, PEPFAR or the United States Government.
3. How did we get here? 3
5. 5 “PEPFAR has prioritized, and made significant progress toward, transitioning a substantial majority of our funding by agency to local partners…” PEPFAR 2022 Annual Report to Congress - State Department
6. 6 Figure H. Funding Local Partners for Sustainable Epidemic Control PEPFAR 2022 Annual Report to Congress - State Department
7. What makes a good government visualization? 7
8. What makes a good government visualization? It depends. 8
10. SESSION PRESENTERS 10 US Agency for International Development Bureau for Global Health | Office of HIV/AIDS | Strategic Information Branch AARON Chafetz Senior Economist achafetz@usaid.gov KARISHMA Srikanth Data Analyst ksrikanth@usaid.gov TIM Essam Senior Data Scientist tessam@usaid.gov
11. 11 PEPFAR US President's Emergency Plan for AIDS Relief
12. Takeaways 12
13. Bottom Line Up Front (BLUF) • Be intentional • Be guided by a central question • Be patient, good visualization takes time 13
14. • Understand the benefits of visualizing and exploring data • Be able to identify problems with a visualization • Discover how text can enhance visualizations • Feel comfortable with data visualization principles Learning Objectives 14
15. Why visualize data? 15
16. 16 How many 4s are in the visual? Adapted from An Economist's Guide to Visualizing Data - Jon Schwabish
17. 17 8 8 2 7 5 4 6 1 5 6 4 6 8 2 4 8 8 1 4 5 8 1 8 8 0 4 3 8 4 7 6 7 3 6 0 7 0 1 8 5 How many 4s are in the visual? Adapted from An Economist's Guide to Visualizing Data - Jon Schwabish
18. 18 8 8 2 7 5 4 6 1 5 6 4 6 8 2 4 8 8 1 4 5 8 1 8 8 0 4 3 8 4 7 6 7 3 6 0 7 0 1 8 5 How many 4s are in the visual? Adapted from An Economist's Guide to Visualizing Data - Jon Schwabish
19. 19 8 8 2 7 5 4 6 1 5 6 4 6 8 2 4 8 8 1 4 5 8 1 8 8 0 4 3 8 4 7 6 7 3 6 0 7 0 1 8 5 These are 4s. What we typically see How many 4s are in the visual? Adapted from An Economist's Guide to Visualizing Data - Jon Schwabish
20. These choices arenʼt random; there are good reasons graphics teams do what they do Covid response hampered by population data glitches - FT/Oliver Barnes & John Burn-Murdoch 20
21. Better Data Visualizations - Jon Schwabish Preattentive attributes 21
22. 22 8 8 2 7 5 4 6 1 5 6 4 6 8 2 4 8 8 1 4 5 8 1 8 8 0 4 3 8 4 7 6 7 3 6 0 7 0 1 8 5 There are 6 fours in the visual below. How many 4s are in the visual? Adapted from An Economist's Guide to Visualizing Data - Jon Schwabish
23. 23 “Effective graphics avoid taxing working memory, guide attention, and respect familiar conventions” -Franconeri et al., 2021
24. 24 Do PEPFAR graphics typically adhere to these principles?
25. 25 x1 x2 x3 x4 y1 y2 y3 y4 10 10 10 8 8.04 9.14 7.46 6.58 8 8 8 8 6.95 8.14 6.77 5.76 13 13 13 8 7.58 8.74 12.74 7.71 9 9 9 8 8.81 8.77 7.11 8.84 11 11 11 8 8.33 9.26 7.81 8.47 14 14 14 8 9.96 8.1 8.84 7.04 6 6 6 8 7.24 6.13 6.08 5.25 4 4 4 19 4.26 3.1 5.39 12.5 12 12 12 8 10.84 9.13 8.15 5.56 7 7 7 8 4.82 7.26 6.42 7.91 5 5 5 8 5.68 4.74 5.73 6.89 Average 9.0 9.0 9.0 9.0 7.5 7.5 7.5 7.5 Variance 11.0 11.0 11.0 11.0 4.1 4.1 4.1 4.1 St. Dev. 3.3 3.3 3.3 3.3 2.0 2.0 2.0 2.0
26. 26 x1 x2 x3 x4 y1 y2 y3 y4 10 10 10 8 8.04 9.14 7.46 6.58 8 8 8 8 6.95 8.14 6.77 5.76 13 13 13 8 7.58 8.74 12.74 7.71 9 9 9 8 8.81 8.77 7.11 8.84 11 11 11 8 8.33 9.26 7.81 8.47 14 14 14 8 9.96 8.1 8.84 7.04 6 6 6 8 7.24 6.13 6.08 5.25 4 4 4 19 4.26 3.1 5.39 12.5 12 12 12 8 10.84 9.13 8.15 5.56 7 7 7 8 4.82 7.26 6.42 7.91 5 5 5 8 5.68 4.74 5.73 6.89 Average 9.0 9.0 9.0 9.0 7.5 7.5 7.5 7.5 Variance 11.0 11.0 11.0 11.0 4.1 4.1 4.1 4.1 St. Dev. 3.3 3.3 3.3 3.3 2.0 2.0 2.0 2.0
28. Why annotate data? 28
29. 29 Visualization is about text too!
30. 30 ? ? ? ? ? ? ? ? ? ?
32. 32 “Titles and supporting text should convey the message of a visualization” -Borkin et al., 2015
33. Measles 33
37. 37 Wall Street Journal: Battling Infectious Diseases in the 20th Century: The Impact of Vaccines
44. Workflow Process 44
45. 45 Photo by Sonja Langford on Unsplash
46. 46 What Questions to Ask When Creating Charts - Lisa Charlotte Muth
47. 47 Kudos and criticisms Plot A Plot B
48. 48 Good data viz is iterative and happens behind the scenes
49. 1 6 5 4 3 2 49 1 Good data viz is iterative and happens behind the scenes
50. 50 Truth Truth Truth @visualizevalue [2022-05-09]
51. “ 51 Do No Harm Guide - J. Schwabish and A. Feng | Institute • Use people-first language • Order labels and responses purposefully • Carefully consider colors, icons, and shapes ”
52. Key Principles 52
53. 53 Declutter and Focus: Empirically Evaluating Design Guidelines for Effective Data Communication - Ajani et al., 2022 Cluttered
54. 54 Decluttered Declutter and Focus: Empirically Evaluating Design Guidelines for Effective Data Communication - Ajani et al., 2022
55. 55 Focused Declutter and Focus: Empirically Evaluating Design Guidelines for Effective Data Communication - Ajani et al., 2022
56. 56 Sort your data Use color judiciously Declutter and focus Use text to reinforce
57. Remake 57
58. 58 PEPFAR Annual Report to Congress 2022 “PEPFAR has prioritized, and made significant progress toward, transitioning a substantial majority of our funding by agency to local partners”
60. 60 Sorted Colors Clutter Title Labels Axes
61. 61 Sorted Colors Clutter Title Labels Axes
62. 62 Sorted Colors Clutter Title Labels Axes
63. 63 Sorted Colors Clutter Title Labels Axes
64. 64 Sorted Colors Clutter Title Labels Axes
65. 65 Sorted Colors Clutter Title Labels Axes
66. 66 Sorted Colors Clutter Title Labels Axes
67. 67 Sorted Colors Clutter Title Labels Axes Four operating units achieved USAIDʼs 70% goal Local partner share of budget 70% goal All of these operating units fell short of the 70% of goal.
68. 68 “PEPFAR has prioritized, and made significant progress toward, transitioning a substantial majority of our funding by agency to local partners”
69. 69 Sorted Colors Clutter Title Labels Axes Four operating units achieved USAIDʼs 70% goal Local partner share of budget 70% goal All of these operating units fell short of the 70% of goal. PEPFAR Annual Report to Congress 2022 “PEPFAR has prioritized, and made significant progress toward, transitioning a substantial majority of our funding by agency to local partners”
70. Where to get started? 70
73. 73 OHA Data Visualization Cheatsheet
74. 74 Financial Times Visual Vocabulary
75. 75 OHA Data Visualization Cheatsheet
76. Data Wrapper 76 OHA Style Guide Do No Harm Guide Adobe Color colorbrewer viz-palette Click any box for a link to the resource Color Brewer Color Palettes
77. 77 Click any box for a link to the resource MS Palettes Excel Hacks Excel Data Viz Open Source Tools R & Rstudio Tableau Tutorials
78. Recapping our Bottom Line • Be intentional • Be guided by a central question • Be patient, good visualization takes time 78 Aaron Chafetz achafetz@usaid.gov Tim Essam tessam@usaid.gov Karishma Srikanth ksrikanth@usaid.gov
79. 79 Notes and Attributions • Gestalt Principles - https://www.interaction-design.org/literature/topics/gestalt-principles • 1.3.3 Gestalt rules from Kieran Healy's Data Visualization - https://socviz.co/lookatdata.html • DatasauRus Package - https://cran.r-project.org/web/packages/datasauRus/vignettes/Datasaurus.html • What Questions to Ask When Creating Charts - https://blog.datawrapper.de/better-charts/ • How Weʼve Learned Data Viz, and Why You May Want To Do It Differently - https://medium.com/nightingale/how-weve-learned-data-viz-and-why-you-may-want-to-do-it-differently-ec1267bd39b2 • PEPFAR Annual Report to Congress - https://www.state.gov/wp-content/uploads/2022/05/PEPFAR2022.pdf • Graphic design has rules, and they work … - https://twitter.com/MR_RO_BO_T/status/1533517961377587201 • #RotateTheDamnPlot - https://twitter.com/ikashnitsky/status/1521960898440613889?s=20&t=rxuGq6l-O8BMdYDG-zzw9A • bar charts and dot plots and line graphs, oh my! - https://www.storytellingwithdata.com/blog/bar-charts-and-dot-plots-and-line-graphs-oh-my • Examples from: Three Simple Flexible tools for Empowered Data Visualization - https://www.youtube.com/watch?v=W02ZlvulHSY • OHA Style Guide - https://issuu.com/achafetz/docs/oha_styleguide • Better Visualizations - Jon Schwabish • Covid response hampered by population data glitches - Oliver Barnes & John Burn-Murdoch - https://www.ft.com/content/125fbaf8-175a-4e2e-852a-9995ca5176b2 • M. A. Borkin et al., "Beyond Memorability: Visualization Recognition and Recall," in IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 519-528, 31 Jan. 2016, doi: 10.1109/TVCG.2015.2467732. • Franconeri, S. L., Padilla, L. M., Shah, P., Zacks, J. M., & Hullman, J. (2021). The Science of Visual Data Communication: What Works. Psychological Science in the Public Interest, 22(3), 110–161. • Ajani K, Lee E, Xiong C, Knaflic CN, Kemper W, Franconeri S. Declutter and Focus: Empirically Evaluating Design Guidelines for Effective Data Communication. IEEE Trans Vis Comput Graph. 2022 Oct;28(10):3351-3364. doi: 10.1109/TVCG.2021.3068337. Epub 2022 Sep 1. PMID: 33760737.