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Maximize Data Visualization Techniques in Science

Harness visual acuity to convey vast data quickly using color, space, depth, and patterns. Understand Anscombe's Quartet and benefits of visualization like precision and data density. Explore visualization methods for different data types. Learn about the role of color in perception and visualization models. Discover techniques like scalar visualization, glyphs, isosurfaces, and volume rendering. Address challenges and open problems in visualization for science.

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Maximize Data Visualization Techniques in Science

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  1. Visualization for Science Bill Howe

  2. Topics • Motivation • Color • SciVis vs. InfoVis • Core SciVis Techniques • Vis + DB • Open Problems

  3. Check Assumptions: Why Visualize? • Problem: • How do you apprehend 100k tuples? • …when your short-term memory is 7-10 items. • Solution: • Harness the visual acuity of the eye to convey enormous amounts of information very quickly • Use color, space, depth, icons, patterns to increase information bandwidth • Caveat: • Easy to misrepresent the data with so many visual dimensions available

  4. Anscombe’s Quartet

  5. Anscombe’s Quartet (2) • mean of the x values = 9.0 • mean of the y values = 7.5 • equation of the least-squared regression line: y = 3 + 0.5x • sums of squared errors (about the mean) = 110.0 • regression sums of squared errors (variance accounted for by x) = 27.5 • residual sums of squared errors (about the regression line) = 13.75 • correlation coefficient = 0.82 • coefficient of determination = 0.67

  6. Anscombe’s Quartet (3)

  7. John Snow

  8. Benefits of Visualization • More precise and revealing • Anscombe’s Quartet • Data density: numbers / cm^2 • Galaxy Map • Human eye is a very efficient pattern detector

  9. Topics • Motivation • Color • SciVis vs. InfoVis • Core Techniques • Vis + DB • Open Problems

  10. Color Matters Lloyd Treinish, IBM Research, http://www.research.ibm.com/people/l/lloydt/

  11. Color: RGB • Carries a natural biological interpretation • as well as a techonolgical interpretation

  12. RGB Justified

  13. HSV • More intuitive model for humans • Difficult to compute the additive RGB values “in your head”

  14. Helps define a curve through the color space Common shapes: Linear S-Curve Common paths: “Rainbow” Color Map Tools

  15. Color Matters (2) Lloyd Treinish, IBM Research, http://www.research.ibm.com/people/l/lloydt/

  16. PRAVDA • Perceptual Rule-based Architecture for Visualizing Data Accurately • Guides color map selection based on human perception • Specifically, choices pruned using • spatial frequency • data type • (ordinal, interval, ratio) • user-selected visualization goal • (isomorphic, segmentation, highlighting)

  17. PRAVDA

  18. Topics • Motivation • Color • SciVis vs. InfoVis • Core SciVis Techniques • Vis + DB • Open Problems

  19. Scientific Visualization

  20. Information Visualization

  21. InfoVis vs. SciVis SciVis: Scientific & physically based InfoVis: Abstract [Card, Mackinlay, & Shneiderman 1999] SciVis: Spatialization given InfoVis: Spatialization chosen [Munzner 2003] Melanie Tory, Visualization 2003, used with permission

  22. Problems with these categories • SciVis or InfoVis? • Scientific, but not physically based • Bioinformatics • Mathematics: e.g., f(i, j, k, w) = i2 + j2 + k2 + w2 • Physically based, but not necessarily scientific • Air traffic control systems • Maps Melanie Tory, Visualization 2003, used with permission

  23. Continuous Data Discrete Data 1st Attempt: Continuous & Discrete Data Parallel Coordinates Direct Volume Rendering [Hauser et al.,Vis 2000] [Fua et al., Vis 1999] Isosurfaces Glyphs Scatter Plots Line Integral Convolution [http://www.axon.com/gn_Acuity.html] Node-link Diagrams [Cabral & Leedom,SIGGRAPH 1993] Streamlines [Lamping et al., CHI 1995] [Verma et al.,Vis 2000] Melanie Tory, Visualization 2003, used with permission

  24. Problems with 1st Attempt BUT… • The same data can be visualized discretely orcontinuously • So, our interpretation of data is more important than characteristics of data [http://www.tvweather.com] [http://www.wunderground.com] Melanie Tory, Visualization 2003, used with permission

  25. [Simeon Potts] [http://www.chem.swin.edu.au/modules/mod2/formats.html] 2nd Attempt: Model not Data Continuous Model Discrete Model Melanie Tory, Visualization 2003, used with permission

  26. 3rd Attempt: What about spatialization? • Visualization can be categorized according to whether the spatial layout is given or chosen [Munzner 2003] • We extend this idea to a continuum: Given Decreasing constraints Chosen [http://www.graphviz.org] [Llyod Treinish] [Heilmann et al., InfoVis 2004] Melanie Tory, Visualization 2003, used with permission

  27. Ex: Biochemical Pathway Visualization source: Betsy Skovran

  28. Ex: Biochemical Pathway Visualization source: Lauro Lins and Claudio Silva, SCI, Utah

  29. Tangible Benefits • Reproducible Science • Visualization by Analogy • Reduce “Time to Insight”

  30. Topics • Motivation • Color • SciVis vs. InfoVis • Core SciVis Techniques • Vis + DB • Open Problems

  31. Visualization Techniques • Scalars • Glyphs • Color • Slices • Isosurface • Vectors • Barbs • Streamlines • Volume Rendering

  32. Structured Grids • Regular topology • Potentially irregular geometry • Fast, but difficult to construct for complex domains

  33. Unstructured Grids • Irregular Topology • Irregular Geometry • Easier to fit to complex domains, • but algorithms more complicated and slower

  34. Cutting Planes

  35. Isosurfaces

  36. Isosurfaces

  37. Isosurfaces (Isolines) • For each cell, mark nodes as above or below the isovalue • Problem: • Desired isosurface intersects cells in many different ways • Observation: • There is only a small number of configurations possible • …when considering algebraic equivalences

  38. Isosurfaces (Isolines) • 2D Case

  39. Isosurfaces • 3D case

  40. Isosurfaces (done) • Marching Cubes: • Same 16 cases work for larger cubes consisting of many cells • Suggests an Out-of-core algorithm that works on a block of cells at a time

  41. Streamlines

  42. Streamlines

  43. Streamlines • Algorithm • From a seed point, cast a ray in the direction of the vector field, repeat • Amounts to integrating the vector field • Design challenges • Evenly spaced streamlines • Discontinuities and boundaries

  44. Volume Rendering

  45. “Full” Volume Rendering

  46. Topics • Motivation • Color • SciVis vs. InfoVis • Core SciVis Techniques • Vis + DB • Open Problems

  47. Converging Requirements Vis DB

  48. Why Vis Needs DB “Transferring the whole data generated … to a storage device or a visualization machine could become a serious bottleneck, because I/O would take most of the … time. A more feasible approach is to reduce and preparethe data in situ for subsequent visualization and data analysis tasks.” -- SciDAC Review Current Research Topics in Vis: • “Query-driven Visualization” • “In Situ Visualization” • “Remote Visualization”

  49. Why DB Needs Vis

  50. Why DB Needs Vis (2) “What does the salt wedge look like?”

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