Data visualization is crucial to understanding the big data being generated by apps and services. Data visualization toolkits such as D3.js and charting toolkits are immensely popular but it remains difficult to create meaningful dashboards or usable analytics tools or clear data visualizations. This talk will discuss data visualization principles, present best practices, showcase excellent visualizations in practice, and share useful tips and mistakes learned.
5. Data visualization helps people understand data through visual display.
6. Communicating knowledge clearly and efficiently. Displaying data to understand cause and effect.
7. “The purpose of visualization is insight, not pictures” Ben Schneiderman A.K.A. It’s not about aesthetics.
8. “#DataVisualization is not about creating infographics for your marketing department. - @RamiSayar #bigdata #dataviz” I dare you to tweet that.
10. Booming Field – Potentially Creating 1.9 Million Jobs.
11. Let’s start with this + some sample D3.js code.
12. The context in which data is visually placed impacts the knowledge that can be gleaned or communicated.
19. Example: Take Care of your Choropleth Maps by Gregor Aisch Don’t mess around with your class limits.
20. Example: Take Care of your Choropleth Maps by Gregor Aisch Don’t mess around with your class colors.
21. Example: Take Care of your Choropleth Maps by Gregor Aisch • Place data in comparison for context, e.g. class count.
22. 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.
25. • 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.
26. The knowledge communicated through visualizations must match the underlying data.
32. • 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.
33. Although devices present data in two dimensions, this desolate flatland can be escaped.
37. 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.
38. Link: UK Temperature History Yes I know, it’s meant to be interactive.
39. Aggregating details can reveal the knowledge present in data.
41. Example: Will it Shuffle? By Mike Bostock Run SHUFFLE DEMO.
43. Layering and parallelizing data visualizations can reveal insights but be careful not to form haystacks.
44. • 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.
45. How Likely Is It That Birth Control Could Let You Down? By Gregor Aisch and Bill Marsh.
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