1 / 53

Cool Creative Communications: Dazzling Data Visualizations

Cool Creative Communications: Dazzling Data Visualizations. K iri Burcat, MLIS Data and Evaluation Coordinator National Network of Libraries of Medicine (NNLM). COURSE OBJECTIVES. Use National Library of Medicine resources to locate data sets Develop data visualizations using Tableau Public

lotus
Download Presentation

Cool Creative Communications: Dazzling Data Visualizations

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Cool Creative Communications: Dazzling Data Visualizations Kiri Burcat, MLIS Data and Evaluation Coordinator National Network of Libraries of Medicine (NNLM)

  2. COURSE OBJECTIVES Use National Library of Medicine resources to locate data sets Develop data visualizations using Tableau Public Critique peer data visualizations using an evaluation model

  3. FUNDAMENTALS OF DATA VISUALIZATION Image from d3js.org

  4. WHY VISUALIZE DATA? Photo by Andrew Neel on Unsplash.com

  5. Visualization enables viewers to quickly glean insights from data. Data and image from Census.gov

  6. FIND PATTERNS John Snow mapped the locations of water pumps and cholera deaths during an outbreak in London in 1854. He is credited with the discovery that cholera is transmitted through contaminated water. Image after John Snow [Public domain], from Wikimedia commons

  7. MAKE DECISIONS Image from Babynamewizard.com/voyager

  8. COMMUNICATE Graphic from the Wall Street Journal

  9. PERSUADE By w:Florence Nightingale (1820–1910). Public Domain, https://commons.wikimedia.org/w/index.php?curid=1474443

  10. HOW DO WE VISUALIZE DATA? HOW DO WE VISUALIZE DATA? Photo by Markus Spiske on Unsplash.com

  11. TYPES OF DATA

  12. VISUAL VARIABLES Position Changes in the x, y location Size Change in length, area, or repetition Color Hue Changes in hue at a given value Shape Color Value Changes from light to dark Orientation Changes in alignment Texture

  13. How many 3s? 193484059849506849002569864175369222689214785625978625258796356788952156785556421897512470025456970159786023682597462101025498567895165289

  14. How many 3s? Part 2 How many 3s? 193484059849506849002569864175369222689214785625978625258796356788952156785556421897512470025456970159786023682597462101025498567895165289

  15. PERCEPTION AND COGNITION PERCEPTION AND COGNITION “Vision Optimization”: We are always looking for patterns, form, and structure Image: Paul Rand (A note on IBM website) [Public domain]

  16. GESTALT PRINCIPLES GESTALT PRINCIPLES Image by Krisztina Szerovay, UX Knowledge Base

  17. GESTALT PRINCIPLES GESTALT PRINCIPLES SLIDE 2 Image by Krisztina Szerovay, UX Knowledge Base

  18. GESTALT PRINCIPLES GESTALT PRINCIPLES SLIDE 3 Image by Krisztina Szerovay, UX Knowledge Base

  19. GESTALT PRINCIPLES GESTALT PRINCIPLES SLIDE 4 Image by Krisztina Szerovay, UX Knowledge Base

  20. SIMPLICITY Humans have a limited capacity for processing input 19348405984950684900256986417536922268921478562597862525879635678895215678555642189751247002545697015978602368259746210102549856789516528916 19348405384950684900256986417536922268931478562593862525379635678895215678555632189751247002545697013978602368259746210102543856783516528916 VS

  21. PROXIMITY Objects that are closer to each other are perceived as being more related than the ones that are not positioned near them

  22. PROXIMITY PROXIMITY CONTINUED www.lib.umd.edu/stem

  23. PROXIMITY PROXIMITY SLIDE 3 www.lib.umd.edu/stem

  24. SIMILARITY If two objects have similar characteristics, we perceive these objects to be more related than the ones that don’t share these qualities.

  25. SIMILARITY slide 2 SIMILARITY If two objects have similar characteristics, we perceive these objects to be more related than the ones that don’t share these qualities.

  26. GESTALT PRINCIPLES APPLIED Gestalt Principles • Proximity • Similarity • Simplicity (an attempt, at least) Visual Variables • Hue • Position Image from Economist.com

  27. CHOOSING A VISUALIZATION CHOOSING A VISUALIZATION

  28. PRACTICAL DESIGN TIPS • Be mindful of cultural and symbolic connotations of color • Get it right in black and white – accessibility for people who are color blind • Stick to ~6 or fewer different colors • Follow familiar patterns and structure Image from d3js.org

  29. DESIGN CRITIQUE Image from Fundamentals of Data Visualization by Claus O. Wilke

  30. DESIGN CRITIQUE Continued DESIGN CRITIQUE The colors are misleading! • Shifts between hues look abrupt • Unintentionally draws attention to blue areas • 0% and 100% would look more similar than 0% and 50% • Defies conventional color associations Image from Fundamentals of Data Visualization by Claus O. Wilke

  31. DESIGN CRITIQUE Slide 3 DESIGN CRITIQUE The colors are misleading! • Shifts between hues look abrupt • Unintentionally draws attention to blue areas • 0% and 100% would look more similar than 0% and 50% • Defies conventional color associations Solutions: • Monochromatic palette • Colorbrewer2.org Image from Fundamentals of Data Visualization by Claus O. Wilke

  32. DESIGN CRITIQUE Slide 4 DESIGN CRITIQUE Image from Fundamentals of Data Visualization by Claus O. Wilke

  33. DESIGN CRITIQUE Slide 5 DESIGN CRITIQUE d The colors are meaningless! Image from Fundamentals of Data Visualization by Claus O. Wilke

  34. DESIGN CRITIQUE Slide 6 DESIGN CRITIQUE The colors are meaningless! Solution: • Choose 1, less saturated color for all the bars Image from Fundamentals of Data Visualization by Claus O. Wilke

  35. DESIGN CRITIQUE Slide 7 DESIGN CRITIQUE Image from Fundamentals of Data Visualization by Claus O. Wilke

  36. DESIGN CRITIQUE Slide 8 DESIGN CRITIQUE Too many colors! Image from Fundamentals of Data Visualization by Claus O. Wilke

  37. DESIGN CRITIQUE Slide 9 DESIGN CRITIQUE Too many colors! Solutions: • Color code dots by region of the US, rather than individual state • Selectively label dots on the chart to draw attention to outliers or interesting cases Image from Fundamentals of Data Visualization by Claus O. Wilke

  38. ASSIGNMENT ASSIGNMENT Data visualization Hall of Fame/Hall of Shame Find an example of a data visualization that you think is either especially effective or especially ineffective. Post on the discussion board with a link to the visualization and the answers to the following questions: • What is the data relationship that is being represented? • How is the creator representing it? • Why do you think this visualization is especially effective/ineffective? Image from the Junkcharts blog

  39. ADDITIONAL RESOURCES • Document on Moodle • List of visualization blogs and galleries for your assignment • Color choosing tools and accessibility information • Two supplemental resources • The best stats you’ve ever seen, by Hans Rosling, founder of Gapminder • 39 Studies of Human Perception in 30 minutesby Kennedy Elliot, graphics editor at the Washington Post

  40. Thank you! Kiri Burcat – kburcat@hshsl.umaryland.edu

  41. Cool Creative Communications: Dazzling Data Visualizations Module 2: Finding Data

  42. IN THIS MODULE • NLM Data Discovery platform • Watch webinar excerpt • Other Data Sources • Review document in Moodle • Assignment: Finding Data • Post on discussion board Photo by Fredy Jacob on Unsplash

  43. Assignment ASSIGNMENT Finding Data Find and download a dataset (or datasets) that you think would make for an interesting visualization. On the discussion board, attach the file as a CSV or Excel worksheet and answer the following questions: • What information is contained in this dataset? • What would be your goal in visualizing the data? • Would you need to further curate the data to achieve your goal? (ie delete unnecessary information or extract only certain information) Photo by Simson Petrol on Unsplash.com

  44. Thank you! kburcat@hshsl.umaryland.edu

  45. Cool Creative Communications: Dazzling Data Visualizations Module 3: Getting Started in Tableau Public

  46. IN THIS VIDEO • Downloading Tableau Public • Downloading Data from the World Bank data bank • Formatting Data • Create a simple visualization in Tableau Public

  47. TABLEAU DATA FORMATTING

  48. FURTHER RESOURCES Required • Tony Nguyen’s Cats vs. Dogs video Supplemental: • Tableau’s “Formatting Data” video • Tableau’s “How to get your data into Tableau Public” video • Tableau’s “How to pivot data in the Data Source” video

  49. Thank you! kburcat@hshsl.umaryland.edu

  50. Cool Creative Communications: Dazzling Data Visualizations Module 4: Dashboards, Stories, Interactivity

More Related