Data Visualisation - A Game of Decisions with Andy Kirk
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These are the slides from Andy Kirk's webinar 'Data Visualisation - A Game of Decisions'. In the webinar Andy argues that the essence of effective data visualisation design is good decision-making. It is about knowing your options and understanding how to make your choices. By deconstructing the decisions demonstrated through case study examples, Andy illustrates the many little elements that make up the design anatomy of any data visualisation work. The aim of this session is to try demystify the challenges of developing capabilities in this area. Watch the webinar: https://www.youtube.com/watch?v=GVkXbQOzKNs&feature=youtu.be
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Data Visualisation - A Game of Decisions with Andy Kirk
- 1. DATA VISUALISATION A GAME OF DECISIONS Andy Kirk andy@visualisingdata.com www.visualisingdata.com @visualisingdata
- 2. 2 The visual representation and presentation of data to facilitate understanding What is data visualisation?
- 3. 3 Why facilitate and not deliver? Perceiving Interpreting Comprehending What does it mean? Is it good or bad? Meaningful or insignificant? Unusual or expected? What does it show? What’s plotted? How do things compare? What relationships exist? What does it mean to me? What are the main messages? What have I learnt? Any actions to take? CREATOR CONSUMER
- 4. 4 The importance of critical thinking to improve visual sophistication
- 5. 5 The importance of critical thinking to improve visual sophistication
- 6. 6 The importance of critical thinking to improve visual sophistication
- 7. 7 To make the best decisions you need to be familiar with all your options and aware of the things that will influence your choices. A game of decisions THINGS YOU COULD DO THINGS YOU WILL DO “IT DEPENDS”
- 8. 8 Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 2 Working with data Stage 3 Establishing your editorial thinking Stage 4 Developing your design solution
- 9. 9 Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 2 Working with data Stage 3 Establishing your editorial thinking Stage 4 Developing your design solution What’s the curiosity? What are the conditions? What’s the purpose?
- 10. 10Visualisation from http://filmographics.visualisingdata.com/ “What is the pattern of success or failure in the movie careers of a range of notable actors/directors?” What’s the curiosity? “An eagerness to understand something”
- 11. 11 What are the conditions? The factors and requirements
- 12. 12 What are the conditions? The factors and requirements http://chartmaker.visualisingdata.com/
- 13. 13 What’s the purpose? How will understanding be facilitated? https://www.bbc.co.uk/weather Explanatory Exploratory Exhibitory
- 14. 14 Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 3 Establishing your editorial thinking Stage 4 Developing your design solution Stage 2 Working with data Data acquisition, examination, transformation, and exploration
- 15. 15 HEADING SUMMARY STATS CREDITS LOGO 63 matches = 8 x 8 grid Working with data: Understanding its properties and qualities
- 16. 16 Working with data: Understanding its properties and qualities
- 17. 17 Working with data: Understanding its properties and qualities Qualitative (Textual) Bolt quote: “It wasn't perfect today, but I got it done and I’m pretty proud of what I've achieved. Nobody else has done it or even attempted it” Categorical (Nominal) The athletics event: Men's 100m Categorical (Ordinal) The medal category: Gold Quantitative (Interval) The estimated temperature at track level during the Men's 100m: 28℃ Quantitative (Ratio) Usain Bolt’s winning time: 9.81 seconds
- 18. 18 Working with data: Understanding its properties and qualities
- 19. 19 Working with data: Understanding its properties and qualities WHO? WHAT? HOW MUCH?
- 20. 20 Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 4 Developing your design solution Stage 2 Working with data Stage 3 Establishing your editorial thinking What questions are you trying to answer in support of the overriding curiosity?
- 21. 21 Editorial: Which angle(s) of analysis are relevant/interesting? How good was my run? What distance did I run? What time/pace did I run it in? What were my main achievements? What was the route elevation? What were my 1km splits?
- 22. 22 Editorial: Which angle(s) of analysis are relevant/interesting? How good was my run?
- 23. 23 Editorial: How will you frame your data (include vs. exclude)?
- 24. 24 Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 2 Working with data Stage 3 Establishing your editorial thinking Stage 4 Developing your design solution Making data representation, interactivity, annotation, colour, and composition choices
- 25. 25 Data representation: A recipe of marks and attributes Shape Line Form Point Size Position Angle Pattern Quantity Containment Connection Symbol Colour Visual placeholders to represent data items Visual properties to represent data values Direction
- 26. 26 Data representation: A recipe of marks and attributes Size Colour Line
- 27. 27 Data representation: A recipe of marks and attributes Shape Colour Size
- 28. 28 Data representation: How to show what you want to say? CATEGORICAL Comparing categories and distributions of quantitative values TEMPORAL Showing trends and activities over time HIERARCHICAL Charting part-to-whole relationships and hierarchies SPATIAL Mapping spatial patterns through overlays and distortions RELATIONAL Graphing relationships to explore correlations and connections
- 29. 29 Data representation: How to show what you want to say?
- 30. 30 Interactivity: Controlling what and how your data is presented Visualisation from http://www.visualisingdata.com/olympics2016/
- 31. 31 Annotation: Judging the right level of assistance Visualisation from http://www.visualisingdata.com/2016/05/boom-bust-shape-roller-coaster-season/
- 32. 32 Annotation: Judging the right level of assistance Illustration by Martin Handford https://www.amazon.com/Wheres-Waldo-Martin-Handford/dp/0763634980/ref=sr_1_5?ie=UTF8&qid=1306352231&sr=8-5 THERE’S WALLY
- 33. 33 Annotation: Judging the right level of assistance
- 34. 34 Colour: Colouring all your chart and project contents Visualisation from http://filmographics.visualisingdata.com/
- 35. 35 Colour: Colouring all your chart and project contents Visualisation by FinViz https://finviz.com/map.ashx?t=sec&st=w1
- 36. 36 Colour: Colouring all your chart and project contents Visualisation by FinViz https://finviz.com/map.ashx?t=sec&st=w1 Colour blindness simulator colororacle.org
- 37. 37 Composition: Making layout, sizing and positioning decisions
- 38. 38 BAR CHART UNIVARIATE BUBBLE PLOT BUBBLE PLOT SLOPE GRAPH MATRIX CHART Composition: Making layout, sizing and positioning decisions TITLE ABOUT THE DATA HEADLINES ABOUT THE SUBJECT SECTIONS & COMMENTARY
- 39. 39 Composition: Making layout, sizing and positioning decisions WHO? WHAT? HOW MUCH?
- 40. 40 Composition: Making layout, sizing and positioning decisions
- 41. 41 Composition: Making layout, sizing and positioning decisions
- 42. 42 Composition: Making layout, sizing and positioning decisions Visualisation by Andy Kirk http://www.visualisingdata.com/olympics2016/
- 43. 43 Demo A four-stage process for efficient and effective visualisation design
- 44. 44 Formulating the brief: Requirements
- 45. 45 Single slide overview to be used in a presentation to key stakeholders to show “how staff feel about working here” Formulating the brief: Requirements
- 46. 46 Formulating the brief: Tool constraints
- 47. 47 Working with data: Understanding its properties and qualities SURVEY RESULTS 8 x question categories about work issues 5 x response categories for scale of feelings 40 x question-response quantities (%, 100% total per question) DEMOGRAPHICS 4 x gender categories, 4 x quantities (% and abs. numbers) 3 x employment categories, 3 x quantities (% and abs. numbers) 6 x service length categories, 6 x quantities (% and abs. numbers)
- 48. 48 1. What the proportion of responses look like for each question? 2. What is the breakdown across respondent demographics? Editorial thinking: What questions are you trying to answer?
- 49. 49 Data representation: How to show what you want to say? CATEGORICAL Comparing categories and distributions of quantitative values TEMPORAL Showing trends and activities over time HIERARCHICAL Charting part-to-whole relationships and hierarchies SPATIAL Mapping spatial patterns through overlays and distortions RELATIONAL Graphing relationships to explore correlations and connections 1. What the proportion of responses look like for each question? 2. What is the breakdown across respondent demographics?
- 50. 50 Chart types: How to show what you want to say?
- 51. 51 Chart types: How to show what you want to say?
- 52. 52 Chart types: How to show what you want to say? Agreement Disagreeme nt No-opinion
- 53. 53 Chart types: How to show what you want to say? Agreement Disagreeme nt No-opinion
- 54. 54 Chart types: How to show what you want to say? Gender Female Male Other No response Employment Status Full-Time Part-Time No response Length of Service Less than 1 year Between 1 and 3 years Between 3 and 5 years Between 5 and 10 years Over 10 years No response Female Male Other No response 0 20 40 60 80 100 120 140 Gender Full-Time Part-Time No response 0 20 40 60 80 100 120 140 160 Employment Status Less than 1 year Between 1 and 3 years Between 3 and 5 years Between 5 and 10 years Over 10 years No response 0 10 20 30 40 50 60 70 80 90 Length of Service
- 55. 55 Chart types: How to show what you want to say? Back-to-back bar chart Bar chart Bubble chart
- 56. 56 Interactivity: Controlling what and how your data is presented Q3. Strongly Agree = 45% More info | Download data | Contact Results filtered for female respondents
- 57. 57 Annotation: Judging the right level of assistance Main observations verbalised
- 58. 58 Colour: Colouring all your chart and project contents
- 59. 59 Colour: Colouring all your chart and project contents
- 60. 60 Colour: Colouring all your chart and project contents Response categories Demographic bars Background shading Title text Section title text Chart axis and value labels
- 61. 61 Colour: Colouring all your chart and project contents
- 62. 62 Composition: Defining all size and position decisions Survey results breakdown Demographic breakdown Title
- 63. 63 Composition: Defining all size and position decisions
- 64. 64 Developing your design solution
- 65. 65 Developing your critical ‘eye’: Evaluating visualisations Design layers Design evaluation Data representation: How is the data visually represented? What choices are effective and why? What choices are ineffective, why? What would be better? Interactivity: Features to adjust the data and presentation What choices are effective and why? What choices are ineffective, why? What would be better? Annotation: Features of assistance What choices are effective and why? What choices are ineffective, why? What would be better? Colour: Data associations, editorial focus, and functional harmony What choices are effective and why? What choices are ineffective, why? What would be better? Composition: Layout, size and placement of all contents What choices are effective and why? What choices are ineffective, why? What would be better?
- 66. 66 Effective visualisation is TRUSTWORTHY Effective visualisation is ACCESSIBLE Effective visualisation is ELEGANT Developing your critical ‘eye’: What is effectiveness? Do I Believe it? Do I Understand it? Do I Like it?
- 67. 67 Learn more! ‘Introduction to Data Visualisation’ online course https://campus.sagepub.com/introduction-to-data-visualisation
- 68. DATA VISUALISATION A GAME OF DECISIONS Andy Kirk andy@visualisingdata.com www.visualisingdata.com @visualisingdata