Scientific Information and Data Visualization CDS 301 Fall, 2008 Jie Zhang Copyright ©

Introduction Visualization What? To be seen Why? To be understood How? A pipeline What? Most effective way human takes in information, digest information, making decision, and being creative Why? One picture is worth 1000 words How? A pipeline approach: acquisition, filtering, mapping and rendering

What? Visualization is the process of presenting data in a visual form that allows rapid understanding and finding Data visualization is at the crossroads of mathematics, computer science, cognitive and perception science, engineering, and physics. Cave painting, drawing, diagram, bar charts, histograms, correlation plots Figure: http://www.calico.ie/blog/uploaded_images/cavepaint-728304.jpg

Why? Provide “insights” Answers to concrete questions about a given problem Facts about a given problem that we are not aware of Scientific insights Understanding observations Testing existing theories Forming new hypothesis Predictions What is science? Physics, Chemistry, Biology, Psychology, Socialology What is the scientific process? reasoning

Simulation of Galaxy Interaction C:\teaching\resources\Visulization_Astronomy\VIDEO_TSVTS_17_1_VOB using Windows Media Player

Space Weather: the Process It starts from an eruption from the Sun. Prediction depends on how it propagates

Space Weather: the Systems

Space Weather: effects On Communication and Navigation

Stereoscopic Technique C:\teaching\2008\2008_CDS301\presentation\ch1_introduction\20070520_195.mov

Interactive Visualization

Info Canvas C:\teaching\2008\2008_CDS301\resource\7infocanvas.mov

How? A Conceptual Pipeline Data Acquisition Filtering Mapping Rendering From dataset to visual representation Data visualization (scivis) Information visualization (infovis) Raw dataset -> prepared dataset -> visual representation -> and draw visual representation Terminology: narrow and broad definitions

Text Book Required: “Data Visualization: Principles and Practice” by Alexandru C. Telea, 2008 Supplement: “Information Visualization: Perception for Design” By Colin Ware, 2004 “Information Visualization: Beyond the Horizon” By Chaomei Chen, 2004 “Introduction to Scientific Visualization” By Helen Wright, 2007

Content Introduction (Ch 1) From Graphics to Visualization (Ch 2) Data Presentation (Ch 3) Visualization Pipeline (Ch 4) Scalar Visualization (Ch 5) Vector Visualization (Ch 6) Tensor Visualization (Ch 7) Domain-Modeling Techniques (Ch 8) Image Visualization (Ch 9) Volume Visualization (Ch 10) Information Visualization (Ch 11) Human Perception (Daniel Carr) OpenGL (Fernando Camelli)

Assignment and Grade Homework (25%) 6 - 8 homework or mini-projects A comprehensive research project Exams One mid-term (20%) one final (25%) Attendance (5%)

Programming Tools C/C++ OpenGL Matlab IDL (Interactive Data Language)

Contact Office Hour: 3 – 4 PM, Thursday or by appointment