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Designing Data Visualization
The document discusses the design and execution of data visualizations, highlighting the importance of data accuracy, exploration, and storytelling through visual representations. It covers various techniques and considerations for creating effective visualizations, including understanding data types, cleaning data, and the potential pitfalls of misrepresentation. Additionally, it emphasizes the iterative nature of visualization development and the necessity of tailoring visuals to the audience's needs.
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Designing Data Visualization
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- 3. THREE HORIZONS • DataExperience Developer at Thomson Reuters Labs • Avid kayaker/fisher • Can speak mandarin
- 4. • Exploring yourdata • Understanding data types • Visualization choices and considerations • How data visualization can lead to data exploration • Creating an experience with information • New models of data visualization • Telling a story through data TALK OVERVIEW PROCESS EXECUTION
- 5. How it Works Itall starts with data
- 6. HOW IT WORKS– IT ALL STARTS WITH DATA
- 7. • Is itstatistically accurate? • What is your sample size? • What is the margin of error? • Are you looking at a long enough timeframe? • When you use a sample to represent an audience, you must make sure that the people in your sample are representative of the audience. • Always spot check your data when combining multiple datasets for errors • Examine outliers – nuggets or inaccuracies HOW IT WORKS – IS IT ACCURATE?
- 8. •Unstructured text vs.structured text •File format (csv, tsv, txt, json…) •Encoding( vs. ) HOW IT WORKS – IS YOUR DATA CLEAN?
- 9. •Gaps in time •Areyou exploring all areas of where you can get data? •Can you aggregate other data sources • This can evolve over time HOW IT WORKS – ARE THERE MISSING PARTS?
- 10. EXPLORING YOUR DATA Unearthingthe narrative of your visualization
- 11. • What's yourhypothesis? • Are there trends in the data? • Start initial visualizations in excel/simple graph tools • Are there outliers? • Combine datasets • Corresponding data that would be of value for comparison • Deeper analysis • Sentiment analysis • NLP • Consider viewing data with different diagrams/visualization DATA EXPLORATION
- 12. •Not all datasetsare complete • Data cleaning will never get you to 100% •Some information is confidential •Consider ways to show missing data EXPLORING YOUR DATA – WHY IS THERE MISSING DATA?
- 13. Exploring your Data – Are you twisting the narrative? • Hidingthe greater picture • Only focusing on a specific time can leave out important comparison information • Excluding data points to give greater merit to a topic • Skewing Visualization in favour of your narrative • Visualizing data can tell false truths when information is not being accurately displayed EXPLORING YOUR DATA – ARE YOU TWISTING THE NARRATIVE?
- 14. Exploring your Data – Are you twisting the narrative? EXPLORING YOURDATA – ARE YOU TWISTING THE NARRATIVE?
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- 16. • Financial • Census/population •Aggregated non-numeric values • Dates/time • Percentage NUMERIC VALUES When to use: • Bar graphs • Scatterplots • Line graphs • Tables/data points • *Pie/Donut
- 17. • BAD PIE Notall pies are created equal
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- 19. • Open-ended response •Tweets • Address information • Form data • Text patterns • *Sentiment analysis • *Text extraction STRING /RESPONSE TEXT When to use: • Word cloud • Text snippet • Tooltip • Results output
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- 21. • Longitude andLatitude • IP address • Line interpolation • Directions • Traffic data SPATIAL / GEOGRAPHIC When to use: • Choropleth maps • Time lapse points • Map interfaces • Event detection • Point to point travel
- 22. • API accessto consolidated data • Twitter • RSS • News feeds • Analytics data • Survey data • Databases • Customer data GROUPED DATA SETS When to use: • Dashboards • Reports • Customer analytics
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- 24. Data Visualization Leadsto Exploration Iterative Development
- 25. Index all geo-locatedtweets referencing #UX in North America over a few months and explore the information for insights with the UX space Scenario
- 26. VISUALIZING DATA -CONSIDERATIONSPlot the lng/lat of the tweets
- 27. VISUALIZING DATA -CONSIDERATIONS • Add radius weighting to follower count • Sentiment analysis of feeling associated with tweet
- 28. • Add tooltipto show content of tweets • Group sentiment to show distribution
- 31. Index all geo-locatedtweets referencing #UX in North America over a few months and explore the information for insights with the UX space #UXploration of Data Recap Scenario
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- 33. • Who areyour audience? • How much detail do they need? • What is the margin of error in your data? • Is the insight actionable or just informative? • What story does the data tell? • Why do you need a visualisation? VISUALIZING DATA - CONSIDERATIONS
- 34. • Test differentways of visualizing your data • Is this for analysisor for story telling? • Multiple ways of seeing the same information can help reinforce • Consider the scales and dimensions on what your visualizing • Leverage the use of *colours VISUALIZING DATA – HOW TO?
- 35. • Clear • Specific •Keep it simple • To the point • Inline with audience VISUALIZING DATA – CONSIDERATIONS
- 36. • 3D –novelty vs. insight • Visualizing data for the sake of visualizing (number dressing) • Over complicating the information • Avoid graphical distortion – pick the right scale • Too many colours (no more than 6) • Reduce the need for math VISUALIZING DATA – THINGS TO AVOID
- 37. CASE STUDY Electric Vehicles– are we making a cleaner planet?
- 38. • Who areyour audience? • How much detail do they need? • What is the margin of error in your data? • Is the insight actionable or just informative? • What story does the data tell? • Why do you need a visualisation? VISUALIZING DATA - CONSIDERATIONS
- 39. VISUALIZING DATA –Iterations .01
- 40. VISUALIZING DATA –Iterations .01
- 41. VISUALIZING DATA –Iterations .01
- 42. VISUALIZING DATA –Iterations .2
- 43. VISUALIZING DATA –Iterations .3
- 44. VISUALIZING DATA –Iterations .4
- 45. VISUALIZING DATA –Iterations .5
- 46. VISUALIZING DATA –Iterations .9
- 47. VISUALIZING DATA –outcome
- 48. VISUALIZING DATA –outcome
- 49. • Who areyour audience? • How much detail do they need? • What is the margin of error in your data? • Is the insight actionable or just informative? • What story does the data tell? • Why do you need a visualisation? VISUALIZING DATA - CONSIDERATIONS
- 50. NEW MODEL OFDATA VIZ Static to Interactive to static and so on.
- 51. VISUALIZING DATA –Static
- 52. VISUALIZING DATA –Static
- 53. VISUALIZING DATA –Sliced Static
- 54. VISUALIZING DATA –Article Integration
- 55. TELLING A STORYTHROUGH DATA
- 56. VISUALIZING DATA –Static
- 57. VISUALIZING DATA –Static
- 58. VISUALIZING DATA –Static
- 59. VISUALIZING DATA –Static
- 60. DATA DESIGN DEVELOPMENTINTERSECTION DATA DEVELOPMENT DESIGN Explore Define
- 61. • Exploring yourdata • Understanding data types • Visualization choices and considerations • How data visualization can lead to data exploration • Creating an experience with information • New models of data visualization • Telling a story through data IN CLOSING… PROCESS EXECUTION
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