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Lecture 1: Introduction Information Visualization CPSC 533C, Fall 2011 Tamara Munzner UBC Computer Science Wed, 7 September 2011 1 / 62 Course Home Page main source readings, lecture slides, all information reload frequently, updates

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Lecture 1: Introduction

Information Visualization CPSC 533C, Fall 2011 Tamara Munzner

UBC Computer Science

Wed, 7 September 2011

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Course Home Page

main source

readings, lecture slides, all information reload frequently, updates common!

permanent URL

http://www.cs.ubc.ca/∼tmm/courses/533-11

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Course Design

reading-intensive course

reading front-loaded in first 9 weeks (less than in past: using new textbook draft)

  • ral presentations

major presentation project update, project final

writing

questions, proposal, final report

programming

project course (unless do analysis option) time management critical: staged development

no problem sets or exams schedule

no classes week of VisWeek (Oct 24, 26)

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Course Structure

lectures/readings

weeks 1-9 (no classes week 8) I lecture 2-3 core readings required, further readings optional submit questions for each lecture (19%) discussion (3%)

presentations (25%)

weeks 10-13 student presentations

  • nly presenter does topic readings

discussion (3%)

project (50%)

weeks 6-14 meetings, proposal, update, final

http://www.cs.ubc.ca/∼tmm/courses/533-11/structure.html

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Course Mark Breakdown

class participation: 25%

questions 75%, discussion 25%

presentation: 25%

details later

project: 50%

proposal 10% interim update presentation 10% final presentation 10% final written report 20% project content 50%

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Required Readings

Munzner

Information Visualization: Principles, Methods, and Practice pre-publication draft chapters posted one week before reading is due

many papers

color PDF downloads from page some are DL links; use library EZproxy

no required textbook to buy

  • ptional reading: Ware, Tufte

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Prerequisites

no courses required HCI very useful computer graphics useful

no graphics background: constraint on project choices

grads from other departments welcome

if no programming background: do analysis/survey project

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Participation

6%: discussions in class

both lectures and student presentations

19%: questions for each required reading

two for longer draft book chapters

  • ne for shorter papers

due at 11am Mon/Wed for day’s reading

attendance expected

tell me in advance if you know you’ll miss class question credit still possible if submitted in advance tell me when you recover if you were ill

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Questions

questions or comments fine to be less formal than written report

correct grammar and spelling expected nevertheless be concise: a few sentences good, one paragraph max!

should be thoughtful, show you’ve read and reflected

poor to ask something trivial to look up

  • k to ask for clarification of genuinely confusing section

book vs paper comments

best: substantive comments on material also useful: order of explanation, undefined words you didn’t know not enough: typos/grammar (but fine to mention)

grading into buckets:

great 100%, good 89%, ok 78%, poor 67%, zero 0%

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Question Examples: Poor

Well, what exactly Pad++ is? Is it a progarmming library

  • r a set of API or a programming language? how can we

use it in our systems, for xample may be programming in TCL or OpenGL may be ? I learned some from this paper and got some ideas of my project.

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Question Examples: OK

This seems like something fun to play around with, are there any real implementations of this? Has a good application for this type of zooming been found? Is there still a real need for this now that scroll wheels have become prevailent and most people don’t even use the scroll bar anymore? Playing with the applet, I find I like half of their

  • approach. It’s nice to zoom out as my scroll speed

increases, but then I don’t like the automatic zoom in when I stop scrolling. Searching the overview I found the location I wanted, but while I paused and looked at the

  • verview, I fell back in to the closeup. I think they need

to significantly dampen their curve.

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Question Examples: Good

It would be interesting to compare the approach in this paper to some other less-mathematically-thought-out zoom and pan solutions to see if it is really better. Sometimes ”faking it” is perceived to be just as good (or better) by users. The space-scale diagrams provided a clear intuition of why zooming out, panning then zooming in is a superior navigation technique. However, I found the diagram too cumbersome for practical use, especially for objects with zoom-dependent representations (Figure 11).

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Question Examples: Great

I’m curious as to what would have happened if the authors had simply preselected the values of the free parameters for the participants in their user study, and then had the users compare their technique to the standard magnification tools present in a ’normal’ application (much like the space-scale folks did). Could it be that the users are ‘manufacturing’ a large standard deviation in the free parameter specifications by settling for values that merely produce a local improvement in their ability to manipulate the interface, instead of actively searching for an optimal valuation scheme? In a related vein, the speed-dependent automatic zooming met with mixed success on some applications. Isn’t this success related to how ”compressible” some information is? i.e. because zooming must necessarily throw out some information, it isn’t obvious which information to keep around to preserve the navigable structure.

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Presentations

second half of class

sign up by Oct 21

material (exact numbers TBD, depending on enrollment)

1 paper from my suggested list 2 papers your choice

talk

slides required summary important, but also have your own thoughts

critical points of papers comparison and critique

grading

per-paper: summary 70%, critique 30% synthesis: critique/synthesis 100% general: presentation style 50%, content prep 50% balance between 3 pieces depends on num papers assigned

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Presentation Topics

http://www.cs.ubc.ca/∼tmm/courses/533- 11/presentations.html

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Projects

choice 1: programming

common case I will only consider supervising students who do programming projects

choice 2: analysis

use existing tools on dataset detailed domain survey particularly suitable for non-CS students

choice 3: survey

very detailed domain survey particularly suitable for non-CS students

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Projects: More

stages

meetings with me for approval by Oct 11-21 (at latest) proposal due Fri Oct 28 update presentations Nov 14/16/21 final presentations Mon Dec 12 2-5 final report Wed Dec 14 noon

resources

software data project ideas

http://www.cs.ubc.ca/∼tmm/courses/533-11/resources.html

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Course Goals and Feedback

twofold goal

specific: teach you some infovis generic: teach you how to be a better researcher

detailed written comments on writing and presenting

both content and style at level of paper review for your final project goal: within a week or so

before updates, for early presentations

fast grading for reading questions

great 100%, good 89%, ok 78%, poor 67%, zero 0% goal: turn around by next class

  • ne week at most

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Office Hours

5-6 Wed after class, or by appointment

  • ffice in X661, ICICS/CS X-Wing

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Reserve Books

Information Visualization: Perception for Design, Colin Ware (2nd ed) Envisioning Information, Edward R. Tufte, Graphics Press 1990 The Visual Display of Quantitative Information, Edward

  • R. Tufte, Graphics Press 1983

Visual Explanations, Edward R. Tufte, Graphics Press 1997 Readings in Information Visualization: Using Vision To Think; Card, Mackinlay, and Shneiderman, eds; Morgan Kaufmann 1999. The Visualization Toolkit, 3rd edition; Schroeder, Martin and Lorensen; Kitware Inc. 2004

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Information Visualization

visual representation of abstract data

computer-generated, often interactive

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Interactivity

static images

10,000 years art, graphic design

moving images

150 years cinematography

interactive graphics

30 years computer graphics, human-computer interaction

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Information Visualization

visual representation of abstract data

computer-generated, often interactive help human perform some task more effectively

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Information Visualization

visual representation of abstract data

computer-generated, can be interactive help human perform some task more effectively

bridging many fields

graphics: drawing in realtime cognitive psych: finding appropriate representation HCI: using task to guide design and evaluation

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Information Visualization

visual representation of abstract data

computer-generated, can be interactive help human perform some task more effectively

bridging many fields

graphics: drawing in realtime cognitive psych: finding appropriate representation HCI: using task to guide design and evaluation

external representation

reduces load on working memory

  • ffload cognition

familiar example: multidigit multiplication

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External Representation: multiplication

paper mental buffer 57 x 48 —-

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External Representation: multiplication

paper mental buffer 57 x 48 [ 7*8=56] —-

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External Representation: multiplication

paper mental buffer 5 57 x 48 [ 7*8=56] —- 6

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External Representation: multiplication

paper mental buffer 5 57 x 48 —- 6

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External Representation: multiplication

paper mental buffer 5 57 x 48 [5*8=40 + 5 = 45] —- 456

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External Representation: multiplication

paper mental buffer 57 x 48 —- 456

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External Representation: multiplication

paper mental buffer 57 x 48 [7*4=28] —- 456

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External Representation: multiplication

paper mental buffer 2 57 x 48 [7*4=28] —- 456 8

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External Representation: multiplication

paper mental buffer 2 57 x 48 —- 456 8

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External Representation: multiplication

paper mental buffer 2 57 x 48 [5*4=20+2=22] —- 456 228

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External Representation: multiplication

paper mental buffer 57 x 48 —- 456 228

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External Representation: multiplication

paper mental buffer 57 x 48 —- 456 228 ——– 6

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External Representation: multiplication

paper mental buffer 57 x 48 —- 1 456 228 ——– 36 [8 + 5 = 13]

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External Representation: multiplication

paper mental buffer 57 x 48 —- 1 456 228 ——– 36

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External Representation: multiplication

paper mental buffer 57 x 48 —- 1 456 228 ——– 736 [4+2+1=7]

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External Representation: multiplication

paper mental buffer 57 x 48 —- 456 228 ——– 736

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External Representation: multiplication

paper mental buffer 57 x 48 —- 456 228 ——– 2736

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Information Visualization

visual representation of abstract data

computer-generated, can be interactive help human perform some task more effectively

bridging many fields

graphics: drawing in realtime cognitive psych: finding appropriate representation HCI: using task to guide design and evaluation

external representation

reduces load on working memory

  • ffload cognition

familiar example: multidigit multiplication infovis example: topic graphs

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External Representation: Topic Graphs

[Godel, Escher, Bach. Hofstadter 1979] Turing - Halting problem Halting problem - Infinity Paradoxes - Lewis Carroll Paradoxes - Infinity Infinity - Lewis Carroll Infinity - Recursion Infinity - Zeno Infinity - Paradoxes Lewis Carroll - Zeno Lewis Carroll - Wordplay Halting problem - Decision procedures Tarski - Truth vs. provability Tarski - Epimenides Tarski - Undecidability Paradoxes - Self-ref ...

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External Representation: Topic Graphs

  • ffload cognition to visual systems

minimal attention to read answer

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External Rep: Automatic Layout

manual: hours, days

(Godel, Escher, Bach. Hofstader 79)

automatic: seconds

Infinity Halting Problem Unpredictably long searches Recursion Zeno Paradoxes Cantor’s diagonal trick Lewis Carroll Halting problem BlooP and FlooP Syntax vs. semantics MU puzzle Fermat Lengthening and shortening rules Self-ref Propositional calculus Number theory Language Confusion of levels R.e. and recursive sets Zen Achilles and the Tortoise Epimenides Godel Quine Jumping out of the system Wordplay GEB, EGB Dialogues Bach Form vs. Content Typogenetics Formal systems Strange Loops or Tangled Hierarchies Genetics Program vs. data Theorems and nontheorems MU-puzzle Computers Sloth Canon Self-engulfing Print Gallery Free will and consciousness Genetic Code Central Dogma Self-rep DNA Ribosomes RNA Use vs. mention Central Dogmap Proteins Viruses Self-assembly Records Explicit vs. implicit meaning Turing Decision procedures Six-part Ricercar Babbage Church-Turing Thesis Turing test Magnificrab Church Undecidability MU Cage Mumon and Joshu Holism vs. reductionism Tarski Truth vs. provability This node Crab Canon Contracrostipunctus Godel code Incompleteness AI 2-D vs. 3-D Lucas Henkin Genetic code Decoding mechanisms Translation Escher Achilles and the Tortise Trip-lets Mentality and mechanizability Author Musical Offering Canons, fugues Order and Chaos Prelude, Ant Fugue Mind-body problem Creativity Analogies Sameness SHRDLU Frames Conceptual nearness Semantic networks Bongard problems Genetic code Isomorphisms Slippability TNT This network Crab Form vs. content Record players Decoding Mechanisms Machine language High-level languages Figure vs. ground Symbol vs. object Magritte Goldberg Preluge, Ant Fugue Contrafactus Music Crab canon Hardware Primes vs. composites Goldbach Crab cannon Dragon Crab Cannon Chunking Primes vs. Composites Neurons Brains Minds Software Symbols

dot, (Gansner et al 93)

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Lecture Topics

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Design Studies

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Visualization Design

domain problem characterization data/operation abstraction design encoding/interaction technique design algorithm design

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Data Principles

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Visual Encoding Principles

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Interaction Principles

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View Composition Methods

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Data Reduction Methods

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Dimension Reduction Methods

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Tabular Data

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Graphs/Trees

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Spatial Fields / SciVis

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InfoVis vs. SciVis

is spatialization given (scivis) or chosen (infovis) infovis: how to represent

choosing, doing, evaluating huge space of possibilities: random walk ineffective need design guidelines broad range of application domains discrete math: stats, graph theory, combinatorics, ...

scivis: heavy algorithms focus

small set of app domains

volume rendering (medical imaging) flow (fluid dynamics)

continuous math: signal processing, flow topology, meshing, ...

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Evaluation

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Research Process/Papers

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Reading For Next Time

  • Visualization. Munzner. Chapter 27, Fundamentals of

Graphics

  • verview to show you spirit/content of this course

Visual Exploration and Analysis of Historic Hotel Visits. Weaver et al. MizBee: A Multiscale Synteny Browser. Meyer, Munzner, Pfister. reading questions due 11am Monday by email

Subject: 533 submit Q02 plain text is best PDF if you must no RTF/DOC/etc...

http://www.cs.ubc.ca/ tmm/courses/533-11/#today

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