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Designing Data Visualizations to Strengthen Health Systems

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Slide deck from our hands-on workshop hosted at the 4th Global Symposium on Health Systems Research, focused on basic design tips, tricks, and best practices to improve your charts and graphs.

Published in: Data & Analytics

Designing Data Visualizations to Strengthen Health Systems

  1. 1. Designing Data Visualizations to Strengthen Health Systems Amanda Makulec Visual Analytics Advisor,JSI Jeff Knezovich Director,Quaternary Consulting Health Systems Research Symposium 15 November 2016 |Vancouver,BC,Canada
  2. 2. Amanda Makulec, MPH Visual Analytics Advisor John Snow Inc. Jeff Knezovich Director Quaternary Consulting
  3. 3. conceptual data driven declarative exploratory idea illustration everyday data viz idea generation visual discovery Matrix credit: Harvard Business Review’s Good Charts
  4. 4. Data Journalism Learn more on how to craft effective data journalism at http://datajournalismhandbook.org/
  5. 5. Where did viz start?
  6. 6. Key terms and definitions
  7. 7. What is data visualisation? • A way of visually conveying information – often quantitative in nature – in an accurate, compelling format. • Usually makes relationships more apparent (e.g. by clustering, color coding and by putting items in scale). • Can be static or interactive.
  8. 8. Data visualisation, or information visualisation? 1001000 1100101 1101100 1101100 1101111
  9. 9. Data visualisation, or information visualisation? 72 101 108 108 111
  10. 10. Data visualisation, or information visualisation?
  11. 11. Brief history
  12. 12. Are data visualisations new? • William Playfair – 1786: line graph and bar chart of economic data – 1801: pie chart and circle graph
  13. 13. • Florence Nightingale – 1858 polar area diagram Are data visualisations new?
  14. 14. Are data visualisations new? • John Snow – 1854: Mapping deaths from a Cholera outbreak in central London
  15. 15. Who’s your audience?
  16. 16. Each persona represents a significant portion of people in the real world and enables the designer to focus on a manageable and memorable cast of characters, instead of focusing on thousands of individuals.” persona, n. “A persona is depicted as a specific person but is not a real individual; rather, it is synthesized from observations of many people. From: https://www.smashingmagazine.com/201 4/08/a-closer-look-at-personas-part-1/
  17. 17. Interests?
  18. 18. Motivations?
  19. 19. Action?
  20. 20. steps to develop simple data viz audience personas
  21. 21. 1. Pick your stakeholder groups
  22. 22. 2. Identify key personas within a stakeholder group
  23. 23. sample template
  24. 24. 3. Map personas by dimension.
  25. 25. Factual, analytical Feeling, intuitive Personal gain System gains External recognition Internal reward Simplicity Complexity Complacent Driver of change Debilitated by chaos Thrives on chaos Team oriented Individual/loaner Problem solver Defeatist Black and white Compromise Receptive Rigid Team leader Team member Carrot eater Stick driven Short term focus Long term vision Self accountable Cheater Internal motive External motive Technical Political Needs clarification Self-motivated Empowerer Underminer Works better in group Works better alone Data driven Story motivated Values independence Values collaboration Head Heart Personas on Continuums Much like the Myers-Briggs personality scale, personas can be ranked along continuums of characteristics that may impact their use of data for decisionmaking.Some examples of different “poles” identified in workshops are below.
  26. 26. Ministry of Health Rachel The Technocrat Pascal The Politician for example segmented to
  27. 27. Wants stories Wants numbers Motivated by passion Motivated by money Act based on feelings Act based on data Resilient problem solver Frustrated bureaucrat Champion for your issue Oppose your issue mapping by dimension
  28. 28. Wants stories Wants numbers Motivated by passion Motivated by money Act based on feelings Act based on data Resilient problem solver Frustrated bureaucrat Champion for your issue No knowledge of your issue mapping by dimension Rachel the Technocrat Pascal the Politician
  29. 29. find your persona
  30. 30. Not just job titles stakeholder groups organization names Focus on the human side of your data viz audience.
  31. 31. Let’s look at some visualizations.
  32. 32. WTF is wrong with the following visualisations?
  33. 33. WTF is wrong with: Image from Patients Association: http://www.patients-association.org.uk/reports/waiting-times-report-feeling-wait/
  34. 34. WTF is wrong with: Image from WTFviz: http://visual.ly/buzzing-trends-real-estate-market-bangalore
  35. 35. WTF is wrong with: Image from WTFviz: http://visual.ly/beyond-facebook-marketing-new-generation
  36. 36. WTF is wrong with: Image from WTFviz: http://viz.wtf/image/110276700184
  37. 37. WTF is wrong with: Image from WTFviz: https://twitter.com/BofA_News/status/558242696415166464
  38. 38. On Think Tanks Data Visualisation Competition Objectives: Inspire Strengthen capacity Encourage
  39. 39. Designing your visualizations
  40. 40. “The two optic nerves [in the eyes] are sending what we now know are 20 megabits a second of information back to the brain.” - Edward Tufte
  41. 41. System 1 vs System 2 thinking 17 x 24 = ? Example from Graham Odds
  42. 42. Conscious v sub-conscious bandwidth 0 2 4 6 8 10 Taste Auditory Olfactory Tactile Visual Sub-conscious (millions of bits per second) 0 10 20 30 40 Conscious (bits per second) Adapted from: Tor Norretranders' The User Illusion
  43. 43. make data sticky
  44. 44. Gestalt design: taking advantage of sub-concsious processing
  45. 45. Gestalt design principles: Implications for data visualisation Adapted from Alberto Cairo
  46. 46. Gestalt design principles: Semiotics and iconography ☺
  47. 47. Who makes a good data visualisation?
  48. 48. Who makes a good visualisation? Communication Research Design Technology From: https://onthinktanks.org/art icles/visualising-data-both-a- science-and-an-art/
  49. 49. • Data literacy – merging and tidying datasets • Statistical competencies – mean v median, ordinal versus scalar • Research methods - sampling • Research context Research
  50. 50. • For data collection – e.g. web scrapers • For data storage – e.g. database and SQL • For data manipulation – e.g. SPSS, R • For data visualisation – e.g. coding, like jQuery, HTML5 Technology
  51. 51. • Appropriate visualisation types • Chart fundamentals • Colour and form Design
  52. 52. • Understanding of users’ needs and perceptions • Finding and refining messages • Telling stories Communication
  53. 53. What makes a good data visualisation?
  54. 54. What makes a good visualisation? Interesting Function Form Integrity (McCandless, 2012)
  55. 55. Function Source: Gregor Aisch - http://slidesha.re/1jTg5Eq
  56. 56. Source: Adapted from from The Wall Street Journal guide to information graphics Form
  57. 57. Integrity
  58. 58. From: XKCD CONSUMERS OF BOILED EGGS Interestingness
  59. 59. What do you think are key elements of effective data visualisation?
  60. 60. preattentive attributes strategic chart selection
  61. 61. “ t h i n k i n g w i t h y o u r l i z a r d b r a i n ” preattentive attributes
  62. 62. Paired ColumnColumn Bar Paired Bar Stacked Bar Stacked Column Slope compare categories
  63. 63. Histogram Box and Whiskers Confidence Interval distribution
  64. 64. Scatterplot Bubble relationship
  65. 65. Line Stacked Area SparkLines Dot Plot time series 0 20 40 60 80
  66. 66. why dots? 0 20 40 60 80 Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8 Year 1 Year 5 0 20 40 60 80 Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8
  67. 67. Pie Donut part-to-whole
  68. 68. a cautionary tale
  69. 69. Image credit from:https://www.biostat.wisc.edu/~kbroman/topten_worstgraphs/bell_fig3.jpg
  70. 70. Image Source: http://www.nytimes.com/imagepages/2008/ 03/16/magazine/16wwln- lede.graphic.ready.html
  71. 71. Tree Map 0% 20% 40% 60% 80% 100% Icon Matrix part-to-whole better 839 294 145 50 Small Multiples Chart Horizontal Stacked Bar
  72. 72. icon matrix
  73. 73. pick your icon
  74. 74. for social share & infographics
  75. 75. View & order a copy at http://policyviz.com/graphic-continuum/ chart type quick reference
  76. 76. Common Pitfalls
  77. 77. Things to avoid when designing your visualizations. Many courtesy of Gregor Aisch (http://slidesha.re/1jTg5Eq)
  78. 78. Using 3d 25% 50% 25% 33% 33% 33% vs
  79. 79. Going colour crazy 0 1 2 3 4 5 6 7 8 9 10 Category 1 Category 2 Category 3 Category 4 0 1 2 3 4 5 6 7 8 9 10 Category 1 Category 2 Category 3 Category 4 vs
  80. 80. Quick colour aside Analogous (similar colours) monochromatic complementary From: Data visualisation: a practical guide to producingeffective visualisations for research communication http://resyst.lshtm.ac.uk/resources/data-visualisation-practical-guide-producing-effective-visualisations-research
  81. 81. Not remembering the objective Team 1 Team 2 Person A Person B Person C Person D Person E Person F Person G Person H vs
  82. 82. Not thinking about order 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 A B C D 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 D A C B vs
  83. 83. Not scaling to zero 11.5 12 12.5 13 13.5 14 14.5 15 Category 1 Category 2 Category 3 Category 4 0 2 4 6 8 10 12 14 16 Category 1 Category 2 Category 3 Category 4 vs
  84. 84. Not scaling to zero (exceptions) vs
  85. 85. Not labelling directly Series 1 Series 2 Series 3 0 1 2 3 4 5 6 1990 1995 2000 2005 0 1 2 3 4 5 6 1990 1995 2000 2005 Series 1 Series 2 Series 3 vs
  86. 86. Non-descriptive titles
  87. 87. Non-descriptive titles Youth Unemployment Rates in Europe
  88. 88. Non-descriptive titles Youth Unemployment on Historical High
  89. 89. Non-descriptive titles Youth Unemployment Divides Europe
  90. 90. Non-descriptive titles Youth Unemployment Divides Europe Seasonally adjusted unemployment rates of under 25s
  91. 91. Seven deadly sins of data visualisation https://onthinktanks.org/articles/on-datavis-judging-jeff-knezovichs-advice/ From: https://onthinktanks.org/articles/on- datavis-judging-jeff-knezovichs-advice/
  92. 92. Not telling a story
  93. 93. Misrespresentation
  94. 94. Shifting scales (without mentioning it)
  95. 95. Over- reliance on text
  96. 96. Ambiguity
  97. 97. Over- complication
  98. 98. Forgetting the point
  99. 99. Break time! Download me: tinyurl.com/DataVizatHSR
  100. 100. Designing in Excel
  101. 101. Think like a designer
  102. 102. Simple LESS IS MORE white space is nice
  103. 103. three things to improve every chart
  104. 104. 1: declutter
  105. 105. “Erase non-data-ink, within reason.”
  106. 106. 8 14.9 11.7 38.9 19.2 16.6 20.4 26.2 0 5 10 15 20 25 30 35 40 45 Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8 Year 1 Year 1
  107. 107. 0 10 20 30 40 50 Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8
  108. 108. 2: color sparingly
  109. 109. 0 10 20 30 40 50 Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8
  110. 110. 0 10 20 30 40 50 Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8
  111. 111. 0 1 2 3 4 5 6 Category 1 Category 2 Category 3 Category 4 Series 1 Series 2 Series 3 Super special brand-compliant graph! Inspired by Cole Naussbaumer at Stanford’s Data on Purpose, February 2016
  112. 112. 4.3 2.5 3.5 4.5 Category 1 Category 2 Category 3 Category 4 Series 1 Series 2 Series 3 Easier to see a key data series graph! Inspired by Cole Naussbaumer at Stanford’s Data on Purpose, February 2016
  113. 113. be mindful
  114. 114. custom styles
  115. 115. 3: purposeful title
  116. 116. 0 10 20 30 40 50 Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8 Quality of Care Score by Facility Ghana, 2015
  117. 117. 0 10 20 30 40 50 Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8 Facility 4 showed the highest quality of care. Despite scoring highest, its overall score was below 50%, indicating there is work to be done to improve quality of care.
  118. 118. 38.9 Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8 Facility 4 showed the highest quality of care. Despite scoring highest, its overall score was below 50%, indicating there is work to be done to improve quality of care.
  119. 119. chart templates
  120. 120. favorite things Graphic Continuum Dot plots Icon matrix Canva Piktochart Noun Project Visage DataViz 101 Pexels Slidedocs Google Sheets High Charts
  121. 121. Inspiration Skill building data viz recs
  122. 122. duarte.com // storycorps.org // skillshare.com storytelling recs
  123. 123. Amanda Makulec amanda_makulec@jsi.com @abmakulec Jeff Knezovich jeff@quarternary.co @knezovjb

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