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FITC - Data Visualization in Practice
- 1. Rami Sayar - @ramisayar Technical Evangelist Microsoft Canada
- 2. • Quick Introduction to Data Visualization • Five Data Visualization Principles & Best Practices to Follow • How Successful Visualizations are Designed • Tips and Mistakes Learned with D3.js
- 3. • Working knowledge of JavaScript, D3.js and HTML5. • Basic understanding of statistics.
- 5. Data visualization helps people understand data through visual display.
- 6. Communicating knowledge clearly and efficiently. Displaying data to understand cause and effect.
- 13. “The purpose of visualization is insight, not pictures” Ben Schneiderman A.K.A. It’s not about aesthetics.
- 14. “#DataVisualization is not about creating infographics for your marketing department. - @RamiSayar #bigdata #dataviz” I dare you to tweet that.
- 18. Off course, I have to show you an infographic! Sorta… LINK: http://blogs.microsoft.com/work/2014/02/12/big- data-and-the-digital-universe
- 19. Booming Field – Potentially Creating 1.9 Million Jobs.
- 22. Let’s start with this + some sample D3.js code.
- 23. The context in which data is visually placed impacts the knowledge that can be gleaned or communicated.
- 24. Example: ‘Stop-and-Frisk’ Is All but Gone From New York by Mike Bostock • Controversial policing tactic that involved stopping and frisking people for what police deemed “suspicious behavior”. • Report data composed of detailed info such as location, time, etc...
- 25. Example: ‘Stop-and-Frisk’ Is All but Gone From New York by Mike Bostock Data Visualization Attempt 1:
- 29. Example: Take Care of your Choropleth Maps by Gregor Aisch Guardian data blog published a US poverty map…
- 30. Example: Take Care of your Choropleth Maps by Gregor Aisch Don’t mess around with your class limits.
- 31. Example: Take Care of your Choropleth Maps by Gregor Aisch Don’t mess around with your class colors.
- 32. Example: Take Care of your Choropleth Maps by Gregor Aisch • Place data in comparison for context, e.g. class count.
- 33. Mapping with D3.js • D3.js includes routines for handling geographic information. • GeoJSON is the geographic data file of choice. Fairly complicated process to convert primary mapping information to GeoJSON. • Instead use: johan/world.geo.json • TopoJSON is an extension of GeoJSON that encodes topology instead of geometrics -> smaller data files.
- 34. Mapping with D3.js • D3.js includes routines for handling geographic information. • GeoJSON is the geographic data file of choice. Fairly complicated process to convert primary mapping information to GeoJSON. • Use: johan/world.geo.json • TopoJSON is an extension of GeoJSON that encodes topology instead of geometrics -> smaller data files. • Use: mbostock/world-atlas • Use: mbostock/us-atlas
- 35. Mapping with D3.js Demo: Area Choropleth Demo: U.S. TopoJSON
- 36. • The context in which data is visually placed impacts the knowledge that can be gleaned or communicated. • Enforce the right comparisons for the context. • Many problems are multivariate (i.e. multiple variables) and that needs to be recognized in the data visualization.
- 37. The knowledge communicated through visualizations must match the underlying data.
- 38. Because I know that’s what some of you are thinking…
- 39. • Fairly obvious point but most people miss this surprisingly… or they do it knowingly… Example: Truncated Y-Axis from How to Lie with Data Visualization by Ravi Parikh
- 40. Example: Cumulative Graphs from How to Lie with Data Visualization by Ravi Parikh
- 41. Example: Effects of Temporal Aggregation from Modern Visual Evidence by Gregory Joseph, discovered in Visual Explanations by Edward Tufte. Quarterly Revenue Revenue by Fiscal Year (July 1 to June 30) Revenue by Calendar Year
- 42. Demo: Crossfilter Crossfilter is a JavaScript library for exploring large multivariate datasets in the browser. Crossfilter supports extremely fast (<30ms) interaction with coordinated views, even with datasets containing a million or more records.
- 43. • The knowledge communicated through visualizations must match the underlying data. • Follow convention in how you model your data and axis. • The objective of data visualization is communicate information to the viewer, misleading by deception or confusion (even accidentally) will not serve your purpose.
- 44. Although devices present data in two dimensions, this desolate flatland can be escaped.
- 45. Example: Using Three Dimensions to Show Skyline Changes. The Bloomberg Years: Reshaping New York by NYTimes Demo
- 46. Example: Visualizing Friendships by Paul Butler
- 47. Example: Using Three Dimensions to Convey Geographic Information at a Global Scale
- 48. Using three dimensions incorrectly adds no value or obfuscates the data visualization. Example: The Ebb and Flow of Movies: Box Office Receipts by NYTimes. Yes I know, it’s meant to be interactive.
- 49. Link: UK Temperature History Yes I know, it’s meant to be interactive.
- 50. Aggregating details can reveal the knowledge present in data.
- 51. On occasion, showing all the data points in one big visualization can communicate knowledge clearly. Example: House Hunting All Day, Every Day by Trulia Trends
- 52. Example: Will it Shuffle? By Mike Bostock Run SHUFFLE DEMO.
- 53. Example: Life Expectancy by Nathan Yau
- 54. Layering and parallelizing data visualizations can reveal insights but be careful not to form haystacks.
- 56. • Layering data on a common X Axis maximizes visualization of coincidence and anomalies. Best use for time series data. • Use Color and Transparency. • Parallelizing data is as powerful as layering data to show significant differences between multiple data sets.
- 57. How Likely Is It That Birth Control Could Let You Down? By Gregor Aisch and Bill Marsh.
- 58. Example: Introduction to Cubism by Mike Bostock CPU Usage
- 59. Example: Introduction to Cubism by Mike Bostock Network (10s)
- 60. Example: Introduction to Cubism by Mike Bostock Network (5m)
- 61. Demo Cubism Sample from github.com/xaranke/cubism-intro
- 63. Books • Anything by Edward Tufte • Knowledge/Information is Beautiful • Designing News by Franchi Conferences • Visualized in NYC Websites • InformationisBeautiful.net • FlowingData.com • Visualizing.org • Information Aesthetics • VisualComplexity.com • Mike Bostock • D3.js Gallery • Driven-by-data.net
- 64. • Edward Tufte - Data Scientist & Professor of PoliSci, Statistics, CS at Yale + Princeton for 33 years - @EdwardTufte • Mike Bostock – Graphics Editor at NYTimes + Creator of D3.js @mbostock • Jonathan Corum - Information Designer and NYTimes Science Graphics Editor - @13pt • Bret Victor – Purveyor of Impossible Dreams - @worrydream • Gregor Aisch – Graphics Editor at NYTimes + Creator of kartograph.js & datawrapper.js - @driven_by_data
- 65. • Introduction to Data Visualization • Five Data Visualization Principles & Best Practices to Follow • Context is king. • Visualizations must match the data. • Escape flatland when useful. • Aggregating details can reveal the knowledge present in data. • Layering and parallelizing data visualizations can reveal insights but be careful not to form haystacks. • How Successful Visualizations are Designed FITC - @RAMISAYAR
- 66. Follow @ramisayar FITC - @RAMISAYAR
- 67. ©2013 Microsoft Corporation. All rights reserved. Microsoft, Windows, Office, Azure, System Center, Dynamics and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.