Workshop: Communicating data effectively with visualizations

To complement our Data-Driven Advocacy Campaign Guide, 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:


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

Data exercise:

  • 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

  • Nominal
  • Ordinal
  • Interval
  • Hierarchical

Relationships within quantities

  • Ranking
  • Ratio
  • Correlation

Patterns through relationships

What type of visuals work best?

Types of Charts, on Wikipedia »


  • 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
  • Ranking
  • Part-to-whole
  • Deviation
  • Distribution
  • Correlation

Designing Information

Attribute Quantitave
Line length Yes
2-D position Yes
Orientation No
Line width Somewhat
Size Somewhat
Shape No
Curvature No
Added marks No
Enclosure No
Hue No
Intensity Somewhat

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

Data components

  • 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?

chartjunk-WTF  chartjunk-3D chartjunk-shootmenowchartjunk-legend

Multiple variables and advanced visualizations

  • A combination of basic graph elements to convey a complex message more effectively

Interactive visualizations