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Data Visualization

Guide

Page Contents

 

Eight visualization principles for researchers [1][2]

Good data visualizations make data meaningful and easy to interpret.  Dr. Stephen M. Kosslyn in Graph Design for the Eye and Mind lists eight major cognitive principles a researcher should think about when putting together a graphic:
 
  • Principle of Relevance: Communication is most effective when neither too much nor too little information is presented.
  • Principle of Appropriate Knowledge: Communication requires prior knowledge of relevant concepts, jargon, and symbols.
  • Principle of Salience: Attention is drawn to large perceptible differences.
  • Principle of Discriminability: Two properties must differ by a large enough proportion or they will not be distinguished.
  • Principle of Perceptual Organization: People automatically group elements into units, which they then attend to and remember.
  • Principle of Compatibility: A message is easiest to understand if its form is compatible with its meaning.
  • Principle of Informative Changes: People expect changes in properties to carry information.
  • Principle of Capacity Limitations: People have a limited capacity to retain and to process information and will not understand a message if too much information must be retained or processed.

Data visualization can convey incorrect information as well as show meaningful relationships. It is important that everyone associated with creating and interpreting a visualization exercises caution in choosing the visualization tools and in interpreting the results. Visualization should tell an accurate story. The presence of a powerful visualization can actually hinder our understanding if we are mislead to think that something is true when in fact it is false. [3]

 

Improve your visualization [3]

1. Design for your audience

2. Accurately represent the data

3. Keep it clear

 

Know your purpose:

1. Communication: Is it revealing unknowns or presenting knowns?

2. Interaction: Will there be a high or low level of user interaction?

3. Audience: Is it meant for public dissemination (eg, to a general audience), or private use (eg, by more technical audience)?

 

An example breakdown for data visualization in the public sector:

 

 

Revealing unknowns

Presenting knowns

High interaction

Private

Exploring data for patterns, using flexible visualisation tools such as Excel, GIS applications, Tableau, intranet Local Information Systems

Interactive performance management tools, providing a series of data reports on service delivery areas such as the economy, health, crime, and so on. Interactive features allow service and performance managers to drill deeper into performance data

Public

Interactive online systems, for example:

  • Gapminder, presenting socio-economic trend data;
  • Many Eyes allowing users to upload data and visualise in different ways

Communicating performance or service information to citizens online using interactive tools, eg location and quality of health services overlaid on Google Maps

Low interaction

Private

Communicating interim results of research to internal audience

Internal research briefings to senior managers

Public

Research reports presenting multiple views on data, eg Joint Strategic Needs Assessment (JSNA)

Communicating performance information to citizens using printed reports

 

Choosing a Good Chart[4]

Sources

[1] https://sites.google.com/site/knaerexemplars/

[2] http://teachingcenter.wikischolars.columbia.edu/file/view/LecturesThatSt...

[3] http://www.improving-visualisation.org/case-studies/id=7#jump_5

[4] http://www.extremepresentation.com/design/charts/

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Web Writing by Kim Pham, Map and Data Library, University of Toronto.