1. Visual Perspectives bernice.e.rogowitz@gmail.com iPLANT Visual Analytics Workshop November 5-6, 2009 ;lk Visual Analytics Bernice Rogowitz Greg Abram

2. Visual Perspectives bernice.e.rogowitz@gmail.com Visual Representation of Data Rene Descartes (1596 – 1650) Values of X 0.5 2.4 5.7 4.2 3.4 Values of Y 40 33 2 18 32 Insight: Represent Magnitude as a Distance

3. Visual Perspectives bernice.e.rogowitz@gmail.com Visualization: Mapping Data onto Visual Dimensions Many visual dimensions -Lines, glyphs, -Color, grayscale -Depth, texture -Motion, 3D Monte Carlo Risk Analysis Data

4. Visual Perspectives bernice.e.rogowitz@gmail.com Four Visualizations of the Same Data Which is “correct” ? -- depends on the data, the task and the domain. Monte Carlo Risk Analysis Data

5. Visual Perspectives bernice.e.rogowitz@gmail.com The Rainbow Color Map Data Value (Z)

6. Visual Perspectives bernice.e.rogowitz@gmail.com Why Perception Matters In the standard, default “Rainbow” color map, equal steps in the magnitude of the data are not perceived as equal steps Rogowitz and Treinish, IEEE Spectrum 1998 “The End of the Rainbow”

7. Visual Perspectives bernice.e.rogowitz@gmail.com Color Perception Experiments test the degree to which different trajectories in 3-D color space convey magnitude information

8. Visual Perspectives bernice.e.rogowitz@gmail.com

10. Using Color to draw attention and mark semantic regions

11. Visual Perspectives bernice.e.rogowitz@gmail.com Using Color to highlight semantics

12. Visual Perspectives bernice.e.rogowitz@gmail.com … a closer look

13. Visual Perspectives bernice.e.rogowitz@gmail.com Interactive Visual Exploration Using Color “Brushing” to help reveal linkages Year pop dji auto housing prime economy helps users explore features in high-dimensional data Diamond

14. Visual Perspectives bernice.e.rogowitz@gmail.com 67197.8920.32924.1652241.857.667.8 67.5198.9110.33424.6622287.756.571.3 68199.920.34125.8182327.359.477.3 68.5200.8980.34927.6612385.360.783.6 69201.8810.35728.7842416.562.685.8 69.5202.8770.36829.0372433.263.986.4 70204.0080.37930.4492408.662.185.4 70.5205.2950.38931.5732435.861.787.7 71206.6680.39932.8932478.661.593.4 71.5207.8810.40634.4312491.16298.5 72209.0610.41235.7622545.665.6105.7 72.5210.0750.41838.0332622.167.6112.3 73211.120.42741.542273471.8126.3 73.5212.0920.44242.5422738.374.4125 74213.0740.46843.2112747.473120.2 74.5214.0420.49346.0622719.373.6130.2 75215.0650.52346.5052642.766.3124.8 75.5216.1950.5449.6182714.965.7140 76217.2490.55852.8862804.469.9156.4 76.5218.2330.5754.9912828.672.5162.4 77219.3440.58756.999289675.5177 77.5220.4580.60860.3423001.878.9186.5 78221.6290.62761.243020.578.8188.9 78.5222.8050.65567.1363142.683.3210 79224.0530.68571.1743181.785.1215.6 79.5225.2950.7373.6673207.485.6223.9 80226.6560.7879.4073233.485.9225 80.5227.940.82679.3113159.181.2218.7 81229.0540.87284.9433261.185.2241.1 81.5230.1680.91586.8063264.687.1246.9 82231.290.94485.9943170.482.4245.1 82.5232.3780.97588.9773154.582252.8 83233.4620.97991.6073186.680.8266.7 83.5234.490.99898.8853306.485.3295.2 84235.5251.021105.1333451.791322.7 84.5236.5481.041106.7813520.693.9337.785237.6081.057110.3933577.593.1361.485.5238.681.077114.4193635.894.1387.286239.7941.099118.4773721.196.1381.886.5240.8621.095119.5933712.494.8426.487241.9431.115119.2473781.296.5403.387.5243.031.139129.9213858.9100.8441.3 71206.6680.39932.8932478.661.593.4 71.5207.8810.40634.4312491.16298.5 72209.0610.41235.7622545.665.6105.7 72.5210.0750.41838.0332622.167.6112.3 73211.120.42741.542273471.8126.3 73.5212.0920.44242.5422738.374.4125 74213.0740.46843.2112747.473120.2 74.5214.0420.49346.0622719.373.6130.2 75215.0650.52346.5052642.766.3124.8 75.5216.1950.5449.6182714.965.7140 76217.2490.55852.8862804.469.9156.4 76.5218.2330.5754.9912828.672.5162.4 77219.3440.58756.999289675.5177 77.5220.4580.60860.3423001.878.9186.5 78221.6290.62761.243020.578.8188.9 Parametric Snake Plot Parallel Coordinates Animated 3-D Scatterplot Fractal Foam Dynamic linking (“brushing” between different data representations)

15. Visual Perspectives bernice.e.rogowitz@gmail.com Many different types of data…. CCA CGAGTA CAA C CGA GTA CCCAA ATGAACACCCAA AAACCCATGATG CACAACAACACC CGA ATGAGACCC AACACCACCAAC CACCCATGA CGA AACACCGAGAAA AT GTACACCCAG time series numerical categorical field image sequence 3-D geometry text GIS graph

16. Visual Perspectives bernice.e.rogowitz@gmail.com Visualizing Patterns across data types

17. Visual Perspectives bernice.e.rogowitz@gmail.com Example: Finite Element Heart Excitation Model  3D computational model for investigating heart disease.  150,000 nodes.  Multiple simulation parameters at each node, 60 time steps. Gresh, Rogowitz, Winslow, et al, 2000 Winslow, et al, 2000 Collaboration with Johns Hopkins University

18. Visual Perspectives bernice.e.rogowitz@gmail.com Interactive Data Exploration, linking numerical parameters and 3-D geometric representation LARGE PEAK AT ZERO

19. Visual Perspectives bernice.e.rogowitz@gmail.com Interactive Data Exploration, linking numerical parameters and 3-D geometric representation COLOR ONLY DATA ABOVE THE PEAK

20. Visual Perspectives bernice.e.rogowitz@gmail.com Interactive Data Exploration, linking numerical parameters and 3-D geometric representation SHOW ONLY COLORED POINTS

21. Visual Perspectives bernice.e.rogowitz@gmail.com Interactive Data Exploration, linking numerical parameters and 3-D geometric representation COLOR PEAKS DIFFERENTLY

22. Visual Perspectives bernice.e.rogowitz@gmail.com Visualization for Visual Analysis  Judgments (Tasks) Magnitude of a variable or set of variable Correlations, trends over timeTrends over time Interaction effects Patterns Connections and relationships Outliers  User Actions View a static representation Browse (pan, zoom, select, rotate) Filter Explore relationships within a data set; across different data sets Identify semantic regions of interest, and explore the behavior of that subset, across representations – “brushing” View over time Transform variables, create new variables Tag and annotate Integrated analysis and visualization of analysis

23. Visual Perspectives bernice.e.rogowitz@gmail.com Visualization and Visual Analysis Framework Infrastructure Array3DImage Sequence TablesVideoText Operations, Functions, Tools (visualization, mathematical libraries, analytical methods) User Interactions Analytical Judgments Communication “Workflow” is a path through this hierarchy Different workflows for different users (personas) Flexible re-use and re-parameterization of functions for different use cases Extensibility (standard APIs, pre-established hooks, metadata)

24. Visual Perspectives bernice.e.rogowitz@gmail.com Bernice’s Web page – Visualization in Plant Genetics  Please let me know if there are other sites or examples I should include  http://sites.google.com/site/bernicerogowitz/plant-genetics- visualization http://sites.google.com/site/bernicerogowitz/plant-genetics- visualization