assignment
Learning outcomes:
By the end of this session you should be able to:
Understand and apply the Design Triangle
Understand and apply the Nested Model of Visualization Design and Validation
Understand and apply Fung’s Junk Chart Trifecta
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Agenda:
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Design Triangle
Nested Model
Fung’s Junk Chart Trifecta
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Design Triangle
Nested Model
Fung’s Junk Chart Trifecta
Design Triangle
Source: Miksch & Aigner (2014).
Representation
& Interaction
Data
Task
User
scale (quantitative vs. qualitative)
frame of reference (abstract vs. spatial)
kind of data (events vs. states)
number of variables (univariate vs. multivariate)
Group factors:
application domain (e.g., health-care, business etc.)
physical environment (e.g., poor lighting)
social factors (e.g., collaborative work or cultural specifics technical specifics (e.g., hardware, screen resolution)
Individual factors:
level of technical and domain expertise (e.g., experts, apprentices, or novices)
specific metaphors and mental models that are used
disabilities (e.g., color-blindness).
Elementary tasks address individual data elements (individual or individual groups of data)
Synoptic tasks involve a general view and consider sets of values or groups of data in their entirety.
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Design Triangle
Representation
& Interaction
Data
Task
User
Expressiveness
Source: Miksch & Aigner (2014).
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Expressiveness
A visualization is considered to be expressive if the relevant information of a dataset (and only this) is expressed by the visualization.
The term "relevant" implies that expressiveness of a visualization can only be assessed regarding a particular user working with the visual representation to achieve certain goals.
“A visualization is said to be expressive if and only if it encodes all the data relations intended and no other data relations.” [Card, 2008, p. 523]
Source: Miksch & Aigner (2014).
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Design Triangle
Representation
& Interaction
Effectiveness
Data
Task
User
Expressiveness
Source: Miksch & Aigner (2014).
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Effectiveness
A visualization is effective if it addresses the capabilities of the human visual system. Since perception, and hence the mental image of a visual representation, varies among users, effectiveness is user-dependent.
Nonetheless, some general rules for effective visualization have been established in the visualization community.
“Effectiveness criteria identify which of these graphical languages [that are expressive], in a given situation, is the most effective at exploiting the capabilities of the output medium and the human visual system.” (Mackinlay, 1986)
Source: http://www.infovis-wiki.net/index.php?title=Effectiveness
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Design Triangle
Representation
& Interaction
Expressiveness
Effectiveness
Appropriateness
Data
Task
User
Source: Miksch & Aigner (2014).
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Appropriateness
Appropriateness regards the trade-off between efforts required for creating the visual representation and the benefits yielded by it. If this trade-off is balanced, the visualization is considered to be appropriate.
Source:http://www.infovis-wiki.net/index.php?title=Appropriateness
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Design Triangle
Representation
& Interaction
Expressiveness
Effectiveness
Appropriateness
Data
Task
User
Source: Miksch & Aigner (2014).
Relevance
Usefulness
Cost
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Design Triangle
Nested Model
Fung’s Junk Chart Trifecta
Nested Model of Visualization Design and Validation
Source: Munzer (2009)
Domain Situation
Data/Task Abstraction
Encoding/Interaction Technique
Algorithm
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Nested Model of Visualization Design and Validation
Source: Munzer (2009)
Domain Situation
describing a group of target users, their domain of interest, their questions, and their data
Data/Task Abstraction
abstracting the specific domain questions and data from the domain specific form into a generic, computational form
Encoding/Interaction Technique
decide on the specific way to create and manipulate the visual representation of the abstraction
Algorithm
crafting a detailed procedure that allows a computer to automatically and efficiently carry out the desired visualization goal
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Nested Model of Visualization Design and Validation
Source: Munzer (2009)
Threat: Wrong problem Avoid: Observe and interview target users
Threat: Bad data/task abstraction
Validate: Test on target users, document usage for utility
Threat: Ineffective encoding/interaction technique
Validate: Test on users using qualitative/quantitative measures
Threat: Slow algorithm
Avoid: Analyze computational complexity
Validate: Measure algorithm speed
Implement System
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Design Triangle
Nested Model
Fung’s Junk Chart Trifecta
Kaiser Fung
Columbia University
Source: http://junkcharts.typepad.com/junk_charts/junk-charts-trifecta-checkup-the-definitive-guide.html
How to identify junk charts?
What is the QUESTION?
What does the DATA say?
What does the VISUAL say?
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The Trifecta
Source: http://junkcharts.typepad.com/junk_charts/junk-charts-trifecta-checkup-the-definitive-guide.html
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Single Issue: Type Q
Source: http://junkcharts.typepad.com/junk_charts/junk-charts-trifecta-checkup-the-definitive-guide.html
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Single Issue: Type D
Source: http://junkcharts.typepad.com/junk_charts/junk-charts-trifecta-checkup-the-definitive-guide.html
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Single Issue: Type V
Source: http://junkcharts.typepad.com/junk_charts/junk-charts-trifecta-checkup-the-definitive-guide.html
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Double Issue: Type QD
Source: http://junkcharts.typepad.com/junk_charts/junk-charts-trifecta-checkup-the-definitive-guide.html
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Double Issue: Type QV
Source: http://junkcharts.typepad.com/junk_charts/junk-charts-trifecta-checkup-the-definitive-guide.html
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Double Issue: Type DV
Source: http://junkcharts.typepad.com/junk_charts/junk-charts-trifecta-checkup-the-definitive-guide.html
HOW DIGITAL MUSIC SERVICES MAY BE FUELLING A ‘SUPERSTAR ARTIST ECONOMY’
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Triple Issue: Type QDV
Source: http://junkcharts.typepad.com/junk_charts/junk-charts-trifecta-checkup-the-definitive-guide.html
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Triple Issue: Type QDV
Source: http://junkcharts.typepad.com/junk_charts/junk-charts-trifecta-checkup-the-definitive-guide.html
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Recap:
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Design Triangle
Nested Model
Fung’s Junk Chart Trifecta
References:
Card,S. (2008) Information visualization, in A. Sears and J.A. Jacko (eds.), The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, Lawrence Erlbaum Assoc Inc, 2007.
Mackinlay, J. (1986). Automating the design of graphical presentations of relational information. ACM Transactions on Graphics,5(2), 110-141. doi:10.1145/22949.22950
Miksch, S., & Aigner, W. (2014). A matter of time: Applying a data–users–tasks design triangle to visual analytics of time-oriented data. Computers & Graphics, 38, 286-290. doi:10.1016/j.cag.2013.11.002
Munzner, T. (2015). Visualization analysis and design. Boca Raton: CRC Press, Taylor & Francis Group.
Tufte, E. (2001) The visual display of quantitive information.
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