PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 B.S., Applied Physics, Condensed Matter San Diego State University – Advanced Physical Measurement 357 “Advanced Physical Pain” – Good at triple integrals of magnetic fields

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 The VPL Research ‘Data Glove’ Parkinson’s tremor, Lou Gehrig’s Disease (ALS), Huntington’s Disease

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, years graduate work in Neurophysiology – Biophysical basis of Parkinson’s Disease – Research wing of the Loma Linda VA under Dr. Ross Adey – Growing neurons, testing them with amphetamines, HPLC – Photoacoustic spectroscopy of neuromelanin for optical absorption properties – Nonlinear Dynamical Analysis of EEG and Dopaminergic Neurodynamics

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Compressed Dimensional Arrays EEG, ECG Spatiotemporal Isosurfaces

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Adjuct Professor at Cuyamaca College – Web Programming, PHP/MySQL, JavaScript, HTML5, CSS3, Flash Master’s Degree Student – SDSU Learning Design & Technology Program

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Why do we care about visualization? [Next sequence of slides courtesy of Amit Chourasia, SDSC, Director of Vis Services]

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16,

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, Total 35

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 What did you observe?

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Text (ASCII or Binary) Images (confocal microscopy, satellite imagery) High Dimensional (structured and Unstructured) Mesh discretizes space into points and cells -1D, 2D, 3D Slide: Courtesy of Sean Ahern, NICS

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Scalars Vectors Tensors Multi-dimensional Slide: Courtesy of Sean Ahern, NICS

Motivation For VISUALIZATION Create visual representations based on underlying data that are (Yes) (Preferably) (Trainable) (Desirable) (We wish) Concise Unambiguous Intuitive Interactive Scalable

Visualization Techniques Scientific Viz Color Map Contours, Isosurface And Explicit Geometry Volumetric Streamlines Line Integral Convolution Topological Glyphs Information Viz Plots (scatter, bar, pie …) Heatmaps Parallel Coordinates Treemaps Partition Maps Flow Maps Networks

Plots and Charts Image: d3js gallery by Michael Bostockd3js gallery

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 IIIIIIIV x1y1x2y2x3y3x4y mean(X) = 9, variance(X) = 11 mean(Y) = 7.5, variance(Y) = 4.12 linear regression lineY = *X correlation(X,Y) = 0.816

Anscombe’s Quartet Anscombe 1973,TheAmerican Statistician

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Process: Map scalar data to a color table Visual Validation Images: Mathworks.com (Heatmap example)Mathworks.com Utility:To investigate range of data Swift error diagnostic and visual validation

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Process: Connect data in a pair wise manner on Y-Y axes

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Filter example Hover example All hit stats example Utility: Summarize high dimensional data Find trends and relationships

Parallel Sets Image: Titanic Survivors by Michael BostockTitanic Survivors

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Process: Recursive mapping of hierarchical data into rectangles Image: Treemap Demo, UMDTreemap Demo Treemap designed by Ben Shneiderman, UMD (1990) History and examplesHistory and examples Utility: Compare tree structures and attributes of varying depth

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Image: Kai WetzelKai Wetzel Circular TreemapVoronoi Treemap Circa (2005): Michael Blazer and DeussenMichael Blazer and Deussen

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 WSJ’s SmartMoney

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Image: Treemap example by Michael BostockTreemap example

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Process: transform a hierarchical data into a linearly proportionate rectangles Utility: examine proportionate distribution of hierarchical data Interactive Link

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Image: Google Analytics

Sankey Diagrams Image: Sankey Diagram by Michael BostockSankey Diagram

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Process: topologically represent hierarchical data Utility: Show and investigate relationships Image: Force Directed Layout by Michael BostockForce Directed Layout

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Image: Visualization by Angi ChauVisualization Angi Chau

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Image: Uber taxi rides in San Francisco by Michael BostockUber taxi rides in San Francisco

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Marian Boguna, Fragkiskos Papadopoulos, and Dmitri Krioukov Image: Reingold-Tilford Tree by Michael BostockReingold-Tilford

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 “Geographic Location of Prague” Cosmographia Petrus Apianus, 1546 Edward Tufte, “The Visual Display of Quantitative Information”, 1983, Graphics Press

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Show the data Substance rather than method Avoid distortion Many numbers in small space Make large datasets coherent Encourage comparison Multiple levels of detail Serve a clear purpose Integrate with statistical and verbal descriptions From “Atlas of Cancer Mortality for U.S. Counties”, Mason et al, NIH 1975 Edward Tufte, “The Visual Display of Quantitative Information”, 1983, Graphics Press

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 “Location of cholera deaths relative to community water pumps”, Dr. John Snow E. W. Gilbert, “Pioneer Maps of Health and Disease in England”, Geographical Journal, 1958 Edward Tufte, “The Visual Display of Quantitative Information”, 1983, Graphics Press

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Hannibal’s campaign in Spain, Gaul, and northern Italy Napoleon’s March on Moscow, Charles Joseph Minard, Tableaux Graphiques et Cartes Figuratives de M. Minard, Edward Tufte, “The Visual Display of Quantitative Information”, 1983, Graphics Press

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Complex ideas communicated with clarity, precision, and efficiency Gives the greatest number of ideas in the shortest time with the least ink in the smallest space Nearly always multivariate Requires telling the truth about the data Edward Tufte, “The Visual Display of Quantitative Information”, 1983, Graphics Press

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Self-promoting Taken over by decorative forms or computer debris Purveys style rather than substance Edward Tufte, “The Visual Display of Quantitative Information”, 1983, Graphics Press

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Edward Tufte, “The Visual Display of Quantitative Information”, 1983, Graphics Press

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Utilization of Primary Colors in Displaying More than One Variable”, reviewed in American Statistician, 34 (1980) Edward Tufte, “The Visual Display of Quantitative Information”, 1983, Graphics Press

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 “Mathematical Model for Urban Air Pollution”, McRae et al, Atmospheric Environment, 16 (1982) Electroencephalogram (EEG), time series on left, 2D topographic brain maps on right Edward Tufte, “The Visual Display of Quantitative Information”, 1983, Graphics Press

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Visualization of a 2-dimensional dataset changing with time

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 Create a stack of 2D Dataset ‘Slices’ Use a visualization tool to create EEG ‘Isopotential’ Contour Surfaces

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, D analog to 2D topographic contour lines on USGS quadrangle maps 2D contour lines represent points of constant value on a surface 3D contour surfaces represent points of constant value in a volume

PACE Boot Camp 2 SDSC, San Diego, CA, October 15-16, 2014 In vivo In vitro In silico In situ