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Validation of Visualizations

CS 4390/5390 Data VisualizationShirley Moore, Instructor

September 24, 2014

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Why Validate?

• Vis design space is huge and most visualizations are ineffective.

• Validate choices throught design and implementation process so as not to have to tear up and redo

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Four Levels of Vis Design

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Domain Situation• Target users• Their domain of interest• Their data• Their questions• Each domain has its own vocabulary for describing its data

and questions.• Usually some existing workflow• Example: Computational biologist using genomic sequence

data to ask questions about the genetic source of adaptivity in a species

• Vis designer needs to clearly understand users’ needs

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Requirements Elicitation

• Outcome: Deatiled list of questions to be asked about the data

• Which is better? 1) What is the density of coverage and where are the gaps across a chromosome? OR 2) What is the genetic basis of disease?

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Task and Data Abstraction

• Map domain-specific questions into abstract vis tasks such as browse, compare, summarize– This is an identification step.

• Choose the most appropriate data abstraction and transform original data if needed– This is a creative design step.

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Encoding and Interaction Idioms

• Visual encoding idiom – create a picture of the data

• Interaction idiom – how users control and change what they see

• Make design decisions based on understanding of human abilities such as visual perception and memory

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Algorithms

• Efficient implementation of visual encoding and interaction idioms

• Accuracy of data representation may also be an issue.

• May have choice of different algorithms – e.g., different volume rendering algorithms for creating images from MRI data

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Threats and Downstream Validation

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Validation Example

• Sizing the Horizon by Heer, Kong, and Agrawala– http://vis.berkeley.edu/papers/horizon/

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Class Exercise 1

• Write down questions to be answered by your Lab 2 visualization

• Interview a classmate about what questions they want answered about the data

• Revise your questions if needed

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Class Exercise 2

• Working with the same person you interviewed for the preceding exercise, share your What? Why? How? analysis for Lab 2

• Validate whether your data and task abstractions match the questions from Exercise 1

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Preparation for Next Class

• Prepare downstream validation tests for Lab 2 visualizations