Eric Barrett, from Jumpstart Georgia put together a workshop on communicating data effectively with visualizations.
The workshop was held in Tbilisi, Georgia, on April 23 & 24, 2013. Here are Eric’s notes. (And here are the notes that I took during the workshop. They’re a bit messy…)
A few sample visualizations:
- New York Times: The Jobless Rate for People Like You
- Gun deaths in the United States
- Georgia Election Portal — built by Jumpstart Georgia for the National Democratic Institute (NDI)
- Speaking Stones, a tool by Jumpstart Georgia that allows users to explore, compare, and engage in the old and new faces of different places, districts, regions, cities, and countries
Why do we collect data anyway?
What is data?
- Data are values of qualitative or quantitative variables, belonging to a set of items.
What is structured data?
- Show sample data
- Understand geographic distribution of participants homes
- Understand age distribution of participants
- Understand gender distribution of participants
- Understand distribution of levels of education of participants
- Understand income distribution of partipants
- Data acquisition from participants
- Data recording
- Data cleaning
- Data analysis
What do visualizations show?
Relationships within categories
Relationships within quantities
Patterns through relationships
What type of visuals work best?
- Display simple relationships between quantitative values and the categorical subdivisions
- Easy to look up values
- Easy to compare values
Graph (the visual display of quantitative information)
- Values are displayed within an area delineated by one or more axes
- Values are encoded as visual objects positioned in relation to the axes
- Axes provides scales (quantitative and categorical) that are used to assign values and labels to the visual objects
Basic graphs and the relationships they show:
- Nominal comparison
- Time series
Gestalt principles of visual perception
- Proximity — Objects that are close together are perceived as a group
- Similarity — Objects that share similar attributes (e.g., color or shape) are perceived as a group
- Enclosure — Objects that appear to have a boundary around them (e.g., formed by a line or area of common color) are perceived as a group
- Closure — Open structures are perceived as closed, complete, and regular whenever there is a way the can be reasonably interpreted as such
- Continuity — Objects that are aligned together or appear to be a continuation of one another are perceived as a group
- Connection — Objects that are connected (e.g., by a line) are perceived as a group
General design objectives of quantitative communication
- Highlight the data
- Reduce the non-data ink
- Enhance the data ink
- Organize the data
- Group the data
- Prioritize the data
- Sequence the data
- Correspondence to tick marks
- Maintain visual correspondence to quantity
- Zero-based scales
- Avoid 3-D
- Support components
- Visual attributes of components
What is “chart junk”?
- What is the practical question?
- What does the data say?
- What does the chart say?
Multiple variables and advanced visualizations
- A combination of basic graph elements to convey a complex message more effectively