Information Visualization Chris North cs 3724 HCI Presentations

Information Visualization Chris North cs 3724: HCI

Presentations • terrence witt • • Vote: UI Hall of Fame/Shame?

Homework 1 results • Avg: ~76 • User and Task analysis? • HCI metrics? principles? • “Learning time is good” • “Learning time is good because users have knowledge X and we know that because of Z”

Quiz • Some principles for layout design: • • fitts law: distance, size Start top left, left to right, top to bottom Avoid scroll bars at all cost!!! …

discussion • “What about my mom…? ”

What is Information Visualization? • The use of computer-supported, interactive, visual representations of abstract data to amplify cognition

The Big Problem • contacts, dict/thes, music, news, email, web • Books, papers, scientific data, VB ref Data Transfer How? • vision: 90 Mb/sec • Hear: 10/s, 44 k/s • Smell: 1 • Touch: • Taste • Neural link: huge • esp Human

Human Vision • • • Highest bandwidth sense Fast, parallel Pattern recognition Pre-attentive Extends memory and cognitive capacity • (Multiplication test) • People think visually Impressive. Lets use it!

Find the Red Square: The pop-out effect

• Which state has highest Income? • Relationship between Income and Education? • Outliers?

College Degree % Per Capita Income

Visual Representation Matters! • Text vs. Graphics • What if you could only see 1 state’s data at a time? (e. g. Census Bureau’s website) • What if I read the data to you?

The Big Problem • Human Data Transfer How? •

The Bigger Problem Human Data Transfer Data How?

Interactive Graphics • Homefinder

forms • Avoid the temptation to design a form-based search engine • More tasks than just “search” • How do I know what to “search” for? • What if there’s something better that I don’t know to search for? • Hides the data

User Tasks • Easy stuff: Excel can do this • Min, max, average, % • These only involve 1 data item or value • Hard stuff: • • Patterns, trends, distributions, changes over time, outliers, exceptions, relationships, correlations, multi-way, combined min/max, tradeoffs, clusters, groups, comparisons, context, anomalies, data errors, Visualization can do this! Paths, …

More than just “data transfer” • Glean higher level knowledge from the data • Hides data • Hides “information” • Nothing learned • Zero insight • Reveals data • Reveals information about data that is not necessarily “stored” in the data • Learn = data information • Insight!

More than just “data transfer” • Glean higher level knowledge from the data • Hides data • Hides “information” • Nothing learned • Zero insight The Insight • Reveals data Factor • Reveals information about data that is not necessarily “stored” in the data • Learn = data information • Insight!

Class Motto Show me the data!

What’s the Big Deal?

Presentation is everything!

My Philosophy: Optimization Computer • Serial • Symbolic • Static • Deterministic • Exact • Binary, 0/1 • Computation • Programmed • Follow instructions • Amoral Human • Parallel • Visual • Dynamic • Non-deterministic • Fuzzy • Gestalt, whole, patterns • Understanding • Free will • Creative • Moral Visualization = the best of both Impressive computation + impressive cognition

Homework #2: Info. Vis. Tools • Get some data: • Tabular, >=5 attributes (columns), >=500 items (rows) • Use 2 visualization tools + Excel: • Spotfire, Table. Lens, Parallel Coordinates • Mcbryde 104 c • 2 page report: • Discoveries in data • Comparison of tools • Due: • Feb 19: A-K • Feb 21: L-Z

Project 2: Java • • • 3 students per team Ambitious project 0: form team (feb 14) 1: design (feb 28) 2: initial implementation (mid march) 3: final implementation (end march)

February • • Feb 14: Project 2, java: teams due Feb 19, 21: Homework #2, info vis Feb 26: midterm Feb 28: Project 2, java: design due • 2 tracks: • Infovis, design, eval • Java

Next • Project 1 due now Presentations: proj 1 design • Next Tues: jerome holman, john gibson • Next Thurs: john randal, tom shultz