Harnessing the Power of Visualization 'Concept to Execution'

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Harnessing the Power of Visualization 'Concept to Execution' VISUALIZATION WORKSHOP Amit Chourasia Visualization Services Group San Diego Supercomputer Center, UCSD AGENDA 01:30pm – 02:15pm SESSION 1A: Information visualization (Lecture) Motivation for visualization Assay of Techniques and their Application 02:15pm – 03:30pm SESSION 1B: Scientific visualization (Lecture) Assay of Techniques Application Use Cases Best Practices 03:00pm – 03:30pm Q&A, BREAK 03:30pm – 04:45pm SESSION 2: Visualization with VisIt (Hands on) 04:45pm – 05:00pm Q & A, Adjourn Be Prepared for Session 2: Visualization Hands On Make sure you have VisIt (2.5.2*): http://visit.llnl.gov/executables.html *Preferred version Sample Data: Unzip sample data to your Desktop https://wci.llnl.gov/codes/visit/2.3.0/VisItClassData.zip Sphere Data http://users.sdsc.edu/~amit/forums/sphere.zip TUTORIAL GOALS Session 1 Visualization concepts Visualization use cases Best practices in visualization Strengths and limitation of visualization Introduction to VisIt software – Perform basic tasks in VisIt Session 2 Perform Basic and Intermediate tasks with VisIt Remote visualization with VisIt on Gordon SESSION 1A: INFORMATION VISUALIZATION VIZ MISCONCEPTIONS • I am not an artist thus can’t do viz • Viz is an art, not science or engineering • Viz is a one-time task • Viz is useful only for communication • Viz SU’s are insignificant Why should you care about visualization? HOW MANY 3’S IN FIRST 350 NUMBERS OF PI? 3.14159265358979323846264338327950288419716939937510582097494459230781640628620 8998628034825342117067982148086513282306647093844609550582231725359408128481117 450284102701938521105559644622948954930381964428810975665933446128475648233786 783165271201909145648566923460348610454326648213393607260249141273724587006606 3155881748815209209628292540917153643 HOW MANY 3’S IN FIRST 350 NUMBERS OF PI? 3.14159265358979323846264338327950288419716939937 8 510582097494459230781640628620899862803482534211 3 706798214808651328230664709384460955058223172535 5 940812848111745028410270193852110555964462294895 3 493038196442881097566593344612847564823378678316 4 527120190914564856692346034861045432664821339360 6 726024914127372458700660631558817488152092096282 2 92540917153643 2 Total 33 What did you observe? PREATTENTIVE PROCESSING Unconsciously gathering information from the environment Preattentive Attributes(partial list): position, orientation, scale color, brightness, saturation shape, texture WHAT IS VISUALIZATION? Working Definition Encoding/mapping data into a visual representation to gain/extract insights DATA • 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 VARIABLES • Scalars • Vectors • Tensors • Multi-dimensional Slide: Courtesy of Sean Ahern, NICS MOTIVATION FOR VISUALIZATION Create visual representations based on underlying data that are • Concise (Yes) • Unambiguous (Preferably) • Intuitive (Trainable) • Interactive (Desirable) • Scalable (We wish) VISUALIZATION BUILDING BLOCKS Viz Elements Viz Attributes View Attributes • Glyphs (symbols: e.g. Alphabets, Arrows, Points) • Transforms • Viewpoint (Position, • Lines • Projection Rotation, Scale) • Triangles (Orthographic, • Color Perspective) • Voxels* (volume element) *Cannot be directly represented on • Opacity • Canvas displays Viz Reinforcement • Texture • Light • Distortion (e.g. displacement) • Motion (e.g. Camera, time steps) • Filter (e.g. threshold, resample, subset, slice, clip) • Add Context (e.g. Connectivity, Map Overlay) VISUALIZATION TECHNIQUES Scientific Viz Information Viz • Color Map • Plots (scatter, bar, pie …) • Contours, Isosurface And Explicit • Heatmaps Geometry • Parallel Coordinates • Volumetric • Treemaps • Streamlines • Partition Maps • Line Integral Convolution • Flow Maps • Topological • Networks • Glyphs PLOTS AND CHARTS Image: d3js gallery by Michael Bostock I II III IV x1 y1 x2 y2 x3 y3 x4 y4 10 8.04 10 9.14 10 7.46 8 6.58 8 6.95 8 8.14 8 6.77 8 5.76 13 7.58 13 8.74 13 12.74 8 7.71 9 8.81 9 8.77 9 7.11 8 8.84 11 8.33 11 9.26 11 7.81 8 8.47 14 9.96 14 8.1 14 8.84 8 7.04 6 7.24 6 6.13 6 6.08 8 5.25 4 4.26 4 3.1 4 5.39 19 12.5 12 10.84 12 9.13 12 8.15 8 5.56 7 4.82 7 7.26 7 6.42 8 7.91 5 5.68 5 4.74 5 5.73 8 6.89 mean(X) = 9, variance(X) = 11 mean(Y) = 7.5, variance(Y) = 4.12 linear regression lineY = 3 + 0.5*X correlation(X,Y) = 0.816 Anscombe’s Quartet 12 12 10 10 8 8 Y2 Y1 6 6 4 4 2 2 0 0 0 2 4 6 8 10 12 14 16 0 2 4 6 8 10 12 14 16 X1 X2 14 14 12 12 10 10 8 8 Y3 Y4 6 6 4 4 2 2 0 0 0 2 4 6 8 10 12 14 16 0 5 10 15 20 X3 X4 Anscombe 1973,TheAmerican Statistician HORIZON CHARTS Image: horizon charts by Michael Bostock HEATMAPS Process: Map scalar data to a color table Visual Validation Utility: To investigate range of data Swift error diagnostic and visual validation Images: Mathworks.com (Heatmap example) HEATMAPS = COLORMAPS = PSUEDOCOLOR PLOTS PARALLEL COORDINATES Process: Connect data in a pair wise manner on Y-Y axes PARALLEL COORDINATE - INTERACTIVITY • Filter example • Hover example • All hit stats example • Sophisticated example Utility: Summarize high dimensional data Find trends and relationships PARALLEL SETS Image: Titanic Survivors by Michael Bostock TREEMAP Process: Recursive mapping of hierarchical data into rectangles Treemap designed by Ben Shneiderman, UMD (1990) History and examples Utility: Compare tree structures and attributes of varying depth Image: Treemap Demo, UMD TREEMAP VARIATIONS Circular Treemap Voronoi Treemap Image: Kai Wetzel Circa (2005): Michael Blazer and Deussen TREEMAP IN ACTION: MAP OF THE MARKET WSJ’s SmartMoney TREEMAP -INTERACTION Image: Treemap example by Michael Bostock PARTITION MAP Process: transform a hierarchical data into a linearly proportionate rectangles Interactive Link Utility: examine proportionate distribution of hierarchical data FLOW MAP Process: connect items depicting flow in proportionate manner Utility: identify relationship, nature or flow (swell and attrition) and corresponding events Image: Charles Joseph Minard (1869) FLOW DIAGRAMS Image: Google Analytics SANKEY DIAGRAMS Image: Sankey Diagram by Michael Bostock NETWORKS Process: topologically represent hierarchical data Utility: Show and investigate relationships Image: Force Directed Layout by Michael Bostock LONDON UNDERGROUND MAP 1932 Image: A History of the London Tube Maps LONDON UNDERGROUND MAP 1933 Image: London Transport website DECISION TREE Image: “512 Paths to the White House” by Mike Bostock and Shan Carter, New York Times HIERARCHICAL NETWORK PRESENTED BY FILL OUT YOUR BRACKET Second Round Third Round Regional Regional National N A TION A L BRACKET DA Y National Regional Regional Third Round Second Round MA RCH 15- 16 MARCH 17-18 Semifinals Finals Semifinals MARCH 12 Semifinals Finals Semifinals MA RCH 17- 18 MA RCH 15- 16 MA RCH 2 2- 23 MA RCH 24 - 25 MA RCH 31 MA RCH 31 MA RCH 24 - 25 MARCH 22- 23 First Four ® 16 Miss. Val. (21-12) 58 14 BYU (25-8) 78 First Round* 16 Lamar (23-11) 59 12 California (24-9) 54 Mar 13 Mar 13 DAYTON Mar 14 Mar 14 S 16 W . Ky. (15-18) 59 W 14 Iona (25-7) 72 MA RCH 13- 14 MW 16 Vermont (23-11) 71 MW 12 S. Fla. (20 -13) 65 W atch On 1 Kentucky (32-2) 81 1 Syracuse (31-2) 72 1 Kentucky 87 1 Syracuse 75 16 W estern Ky. 66 16 UNC Asheville (24-9) 65 Louisville Pittsburgh 1 Kentucky 102 1 Syracuse 64 Mar 17 Mar 17 8 Iowa St. (22-10) 77 8 Kansas St. (21-10) 70 8 Iowa St. 71 8 Kansas St. 59 9 Connecticut (20 -13) 64 9 Southern Miss. (25-8) 64 Atlanta 1 Kentucky 82 1 Syracuse 70 Boston Mar 23 Mar 22 5 W ichita St. (27-5) 59 5 Vanderbilt (24-10) 79 12 VCU 61 5 Vanderbilt 57 12 VCU (28-6) 12 Harvard (26-4) 62 Albuquerque 70 Portland 4 Indiana 90 4 W isconsin 63 Mar 17 Mar 17 4 Indiana (25-8) 79 4 W isconsin (24-9) 73 4 Indiana 63 4 W isconsin 60 13 New Mexico St. (26-9) 66 13 Montana (25-6) 49 SOUTH 1 Kentucky 69 2 Ohio St. 62 EAST 6 ® 6 Cincinnati (24-10) UNLV (26-8) 64 ATLANTA Mar 31 Final Four Mar 31 BOSTON 65 11 Colorado 63 March 25 March 24 6 Cincinnati 62 11 Colorado (23-11) NEW ORLEANS 11 Texas (20 -13) 68 Nashville 59 Albuquerque 3 Baylor 75 MARCH 31 AND APRIL 2 6 Cincinnati 66 Mar 17 Mar 18 3 Baylor (27-7) 68 3 Florida St. (24-9) 66 3 Baylor 80 3 Florida St. 56 14 S. Dakota St. (27-7) 60 14 St. Bonaventure (20 -11) 63 Atlanta 3 Baylor 70 2 Ohio St. 77 Boston 7 Notre Dame (22-11) 63 Mar 23 Mar 22 7 Gonzaga (25-6) 77 10 Xavier 70 National 7 Gonzaga 66 10 Xavier (21-12) 67 10 West Virginia (19-13) 54 Pittsburgh Greensboro 10 Xavier 70 Championship 2 Ohio St. 81 Mar 18 Mar 17 2 Duke (27-6) 70 A PRIL 2 2 Ohio St. (27-7) 78 15 Lehigh 58 2 Ohio St. 73 15 Lehigh (26-7) 75 15 Loyola (MD) (24-8) 59 1 Kentucky 67 Kentucky 2 Kansas 59 1 Michigan St. (27-7) 89 1 North Carolina (29-5) 77 1 Michigan St. 65 1 UNC 87 16 LIU Brooklyn (25-8) 67 16 Vermont 58 Columbus 1 Mich. St. 44 1 UNC 73 Greensboro Mar 18 Mar 18 8 Memphis (26-8) 54 8 Creighton (28-5) 58 9 Saint Louis 61 8 Creighton 73 9 Saint Louis (25-7) 61 9 Alabama (21-11) 57 Phoenix 4 Louisville 72 1 UNC 67 St. Louis 5 New Mexico (27-6) 75 Mar 22 Mar 23 5 Temple (24-7) 44 5 New Mexico 56 12 South Fla. 56 12 Long Beach St. (25-8) 68 12 South Fla. 58 Portland 4 Louisville 57 13 Ohio 65 Nashville Mar 17 Mar 18 4 Louisville (26-9) 69 4 Michigan (24-9) 60 4 Louisville 59 13 Ohio 62 13 Davidson (25-7) 62 13 Ohio (27-7) 65 WEST 4 Louisville 61 2 Kansas 64 MIDWEST 6 Murray St.
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  • Global Media Journal - Australian Edition - Volume 3 Issue 2 2009
    Global Media Journal - Australian Edition - Volume 3 Issue 2 2009 Reporting in the ‘New Media’ Environment: How Today’s Television Journalists are Recycling Work Practices of the Past. Barbara Alysen – University of Western Sydney Abstract The issue of whether the practice of journalism for new mobile platforms and round-the-clock delivery represents a sharp break with the past or an adaptation of existing practices to meet new circumstances, remains unresolved within the study of Australian journalism. Some analysts see the contemporary news environment as forcing a revolutionary change in professional practice. In television news, for example, this has seen newsrooms addressing the challenges of new competition and reduced budgets by using associated online sites to present first versions of stories, requiring reporters to do more of the production work and more of their work in a ‘live’ environment while also drawing on the public for more of the material from which to generate news. However, the idea that journalism is undergoing a revolutionary shift in practice may owe its appeal, in part, to the relative dearth of historical analysis of work practices within Australian journalism in general, and broadcast journalism in particular. While some practices may, in fact, represent something new occasioned by changes in news media delivery others are more evolutionary and, in some ways, the skills required of journalists working in the ‘new media’ environment echo those required in a very different media environment. Drawing on archival research into television news in Australia, the objective is to compare the nature of work required of television journalists in the current mobile platform and 24-hour delivery markets with those required during a period of change in Australian television journalism in the early 1970s.
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