1. Validation of Visualizations CS 4390/5390 Data Visualization Shirley Moore, Instructor September 24, 2014 1

2. 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 2

3. Four Levels of Vis Design 3

4. 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 4

5. 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? 5

6. 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. 6

7. 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 7

8. 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 8

9. Threats and Downstream Validation 9

10. Validation Example Sizing the Horizon by Heer, Kong, and Agrawala – http://vis.berkeley.edu/papers/horizon/ http://vis.berkeley.edu/papers/horizon/ 10

11. 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 11

12. 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 12

13. Preparation for Next Class Prepare downstream validation tests for Lab 2 visualizations 13