Two days of tutorials withNaomi B. Robbins and Lynn ChernyDecember 11-12, 2014, NYC
Learn how to communicate data clearly in charts and graphs, both static and interactive, followed by a deep-dive into network visualization techniques.
Naomi B. Robbins, Ph.D.NBR
Naomi B. Robbins is a consultant and seminar leader who specializes in the graphical display of data. She is the author of Creating More Effective Graphs, published by Chart House in 2013 (originally published by Wiley). Dr. Robbins has been the keynote speaker at international conventions and has spoken on graphs to corporations, government agencies, universities, professional societies, and non-profits. She received her Ph.D. in mathematical statistics from Columbia University. Naomi left Bell Labs to form NBR.
Lynn Cherny, Ph.D.Ghostweather R&D
Lynn Cherny is a visualization and data analysis consultant who speaks and teaches when she's not creating D3 web visualizations. She was a UX designer and manager for 18 years before turning her focus to data visualization. She moderates the data-vis-jobs mailing list and is a committee member of the 2015 OpenVis Conference, a conference focused on open web visualization tools and design. Her Ph.D. is from Stanford University.
Communicating Data Clearly (Day 1, 9-5)
Communicating Data Clearly describes how to draw clear, concise, accurate graphs that are easier to understand than many of the graphs one sees today. The tutorial emphasizes how to avoid common mistakes that produce confusing or even misleading graphs. Graphs for one, two, three, and many variables are covered as well as general principles for creating effective graphs.This workshop begins by reviewing human perception and our ability to decode graphical information. It continues by:
- Ranking elementary graphical perception tasks to identify those that we do the best.
- Showing the limitations of many common graphical constructions.
- Demonstrating newer, more effective graphical forms developed on the basis of the ranking.
- Providing general principles for creating effective graphs, as well as metrics on the quality of graphs.
- Commenting on software packages that produce graphs.
- Comparing the same data using different graph forms so the audience can see how understanding depends on the graphical construction used.
- Discussing Trellis Display (a framework for the visualization of multivariate data) and other innovative methods for presenting more than two variables.
- Presenting Mosaic Plots and other graphical methods for categorical data.
Since scales have a profound effect on our interpretation of graphs, the section on general principles contains a detailed discussion of scales including:
- To include or not to include zero?
- When do logarithmic scales improve clarity?
- What are breaks in scales and how should they be used?
- Are two scales better than one? How can we distinguish between informative and deceptive double axes?
- Can a scale "hide" data? How can this be avoided?
Design for Interactivity (Day 2 Morning, 9-12)
Designing a good interaction layer for a data visualization requires a special subset of user experience skills. This tutorial will cover practical principles and examples, going from solid academic work to current word-on-the-street tips and tricks used by the best practitioners. Current and famous examples of interactivity will be used to illustrate all points and form the basis of group discussion. There will be one mockup exercise using pencil and paper, to cement the principles. (Note, this is not a coding workshop, although tools will be discussed.)
- Principled frameworks for discussion about visualization interaction design
- "Overview first, zoom and filter, details on demand" -- Shneiderman (1996)
- Taxonomy of interactive dynamics for visual analysis in Heer & Shneiderman (2012)
- Coordinated linked views
- Annotation layers: rollovers, highlights, auto-summaries, and more
- The time and place for animation
- Tools for adding interactivity, from simple to complete customization (such as D3.js)
Network Visualization (Day 2 Afternoon, 1.30-5)
Network data is pervasive, but remains difficult to visualize well. Any relationship between entities can be viewed as a network diagram. The first pass visual is usually a "hairball" without structure or interactivity to help the end-user make sense of relationships. In this tutorial, we'll cover simple statistics that will help with clarifying important aspects of the network, as well as alternative layouts and interaction techniques. Examples will be primarily from Gephi (gephi.github.io) and D3.js or sigma.js. This is not a coding course, but the final section will be a brief optional introduction to Gephi for any who would like to get a head-start on using that tool. Rather than attempt hand-drawn networks as group exercises, we'll do a few layouts together in Gephi, which attendees can work along with or just watch.
- Network data and the problem of the hairball
- How people "read" and "misread" network diagrams
- Alternative layouts: Matrix, chord, arc, edge bundling...
- Algorithmic layouts such as force-directed
- Large network issues
- Calculations on networks to improve design: degree, betweenness, community-finding
- Interaction techniques using metadata for filter, search, highlight
- (Depending on time/interest) Gephi UI tour and tips/tricks