《Information Visualization》Course Designg—— IV Course Spring’14

Graduate Coursef UCASof UCAS

Feb 28th, 2014

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Outline

• Topic OverviewSyllabus: A List of 18 Lectures• Syllabus: A List of 18 Lectures

• Course BasicsM l• Material

• Evaluation• Miscellaneous • Q&A

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InformationVis li ti

VisualizationVisualizationPipeline

• InfoVis Beginner

• InfoVis p• InfoVis Practitioner

• InfoVis Researcher

Data UserData UserAbstract Information not seen in nature

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not seen in nature

Overview of Topics• Interactions• Explorative Visual Analysis

• Data Types• Data Model

• Perception• Evaluation

Data User

• Visualization Design

Visualization• Visualization Design• Visualization Toolkit and System• Multidimensional Visualization• Time-series/Spatiotemporal

• Text and Document Visualization • Network Visualization• High Performance Visualization• Dynamic Visualization

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p pVisualization • Dynamic Visualization

Lecture 1: The Beauty of Information Visualization (1 Class)Visualization (1 Class)

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Lecture 2: Course Design (1 Class)g ( )

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Lecture 3: The Value of Information Visualization (1 Class)Visualization (1 Class)

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Lecture 4: Perception (1 Class)p ( )

We can see the yellow

better than the blue

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Lecture 5: Design of InformationVisualization (2 Class)Visualization (2 Class)

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Lecture 6: Data Models and Data Types(2 Class)(2 Class)

• 1-D Linear Document Lens, SeeSoft, Info Mural. 1 D Linear Document Lens, SeeSoft, Info Mural

• 2-D Map GIS, ArcView, PageMaker, Medical imagery

• 3-D World CAD, Medical, Molecules, ArchitectureciV

iz

3 D World CAD, Medical, Molecules, ArchitectureS

• Multi-Var Spotfire, Tableau, GGobi, TableLens, ParCoords,

• Temporal LifeLines, TimeSearcher, Palantir, DataMontage

Viz

• Tree Cone/Cam/Hyperbolic, SpaceTree, Treemap

• Network Pajek, JUNG, UCINet, SocialAction, NodeXLInfo

V

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Lecture 7: Explorative Visual Analytics(1 Class)(1 Class)

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Lecture 8: Text and Document Visualization (3 Class)Visualization (3 Class)

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Lecture 9: Network Visualization (3 Class)( )

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Lecture 10: Interaction Design (2 Class)g ( )

O kl d C i i i i i f i iOakland Crimespotting is an interactive map of crimes in Oakland and a tool for understanding crime in cities.

http://oakland crimespotting org/

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http://oakland.crimespotting.org/

Lecture 11: Dynamic Visualization (1 Class)y ( )

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Lecture 12: Information Visualization Software Toolkit/System/Programming (3 Class)– Toolkit/System/Programming (3 Class)

InteractiveInteractiveVisualization

with D3w th D3

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Lecture 13: Multidimensional and Multivariate Data Visualization (3 Class)Multivariate Data Visualization (3 Class)

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Lecture 14: Time Series Visualization(1 Class)(1 Class)

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Lecture 15: Spatiotemporal Data Visualization (2 Class)Visualization (2 Class)

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Lecture 16: Evaluation (3 Class)( )

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Guest Lecture: Scientific Visualization in Simulations (2~3 Class)Simulations (2~3 Class)

By Guihua Shan, CNIC, CAS

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Guest Lecture: Big Data Analysis (3 Class)

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Class Presentation

Yours?Yours?

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Course Objectivej• Learn the concept of Information Visualization, its design principle core workflow/pipeline and a brief design principle, core workflow/pipeline and a brief history of development. Understand the various methods to create a visualization, know typical information ypvisualization toolkits/systems• Develop the basic skill to apply classical information visualization methods in analyzing (big) data, know how to select from different visualization metaphors based on the characteristics of data type user and taskthe characteristics of data type, user and task• Gain a background to be creative in information visualization research by inventing innovative IV designs visualization research, by inventing innovative IV designs, implementing new IV systems, evaluating other’s visualizations and preparing to grow as an expert in the

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p p g g pfield

Course Basics• Instructor: Lei Shi (shil [at] ios.ac.cn) • Time: Friday 9:20 – 12:10 Week 1 3-14Time: Friday, 9:20 12:10, Week 1, 3 14

– No class next week– Class break once in 3 hours

• Location: TBD• Pre-require: No strict oneq

– Programming and/or Design and/or Analytics and/or Cognitive will be plus• TA: Pengyu Han from Tsinghua U• Workload: NO EXAM (?)

– 3 assignments (homework)– 1 final project (3~5 people each group)– More if you want to finish a research paper or challenge entry (VAST Challenge’14 [07/07/2014] PacificVis’15 [01/09/2014])

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(VAST Challenge 14 [07/07/2014], PacificVis 15 [01/09/2014])

TA Introduction: Pengyu Hangy• Major: Computer Science• Education Background:Education Background:

Second year Master student at Tsinghua University• Projects:Projects:

– Digital library based on social network– Enrollment information management system of Ministry ofg y yEducation

– Visualization development of code analysis platforml–Network security visualization

•Office: Tsinghua University FIT 4-106Offi H 8 00 18 00(M F i)•Office Hour: 8:00-18:00(Mon-Fri)

•E-mail: hanpengyu2003@126.com

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Course Website• Address:

–http://lcs.ios.ac.cn/~shil/wiki/index.php/UCAS-IV14p // . . . / /w / .p p/ V•Usage

–Click the “create account” button to create you accounty–Please use your Student ID as your user name–Click the “Upload file” button on the left toolbox if you want to upload your homework The supported file types:upload your homework. The supported file types:'png', 'gif', 'jpg', 'jpeg', 'zip', 'doc', 'pdf', 'ppt', 'xls', 'docx', 'pptx', 'xlsx'pp–Please upload your homework in the specified area in Assignment pageThe format of title of your homework: “id”+” ”+”name”–The format of title of your homework: id + _ + namee.g. 123456_John

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Material• Course wiki: http://lcs.ios.ac.cn/~shil/wiki/index.php/UCAS-IV14

– Syllabus, slides, material all on the wikiy u , , m w– Assignment, project submitted on wiki, will be public accessible

• Text book– Edward Tufte. The Visual Display of Quantitative Information. Graphics Press, 2001.

Colin Ware Information Visualization Perception for Design – Colin Ware, Information Visualization – Perception for Design, Third Edition, 2013 – 陈为，沈则潜，陶煜波。数据可视化. 电子工业出版社，2013年11月.

• Other readings– Recommended reading for each lecture on our wiki

• Online resources: – See wiki

h b d d f b !

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– We have bonus and credits for contributors!

Evaluation• Class participation: 15%• Assignments: 40%Assignments 40%

– Online visualization usage: 10%– Visualization design: 15%

G h d i 15%– Graph drawing: 15%

• Final project: 45%BONUS: 10%• BONUS: 10%

– Ask questions at class: 1 grade each, 5 grades at maximum– Contribute InfoVis resources to our course wiki: 1 grade for each topic, g p ,5 grades at maximum (in case accepted)

• PolicyL 10% d d i h d l ( i fi l j )– Late: 10% deduction each day late (assignment, final project)

– Duplication: 50% score for each people/team (or divided by #Dup)– Open source: open source usage encouraged, MUST cite the source of

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p p g gthe code/app/data and explain which part is new

Miscellaneous• Office hour

– @ISCAS: by appointment@ y pp m• Course contact

– Mail list: Google groups?g g p– Will establish a discussion forum (HPY)

• Potential poster session in UCAS– TBD

• Commercial visualization software license– TBD

•

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Questions?Questions?

What’s Next -- Lecture 3: The Value of Information VisualizationInformation Visualization

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