Information Visualization

Fall 2009

Jinwook Seo

SNU CSE

Interaction

Readings

� J.S. Yi, Y.A. Kang, J.T. Stasko and J.A. Jacko, "Toward a Deeper Understanding of the Role of Interaction in Information Visualization", IEEE Transactions on Visualization and Computer Graphics, Vol. 13, No. 6, Nov/Dec 2007, pp. 1224-1231.

� Chapter 10, Interacting with Visualizations, Information Visualization: Perception for Design, 2nd ed. Colin Ware, Morgan Kaufmann, 2004, ISBN 1-55860-819-2.

� B. Shneiderman, "Dynamic Queries for Visual Information Seeking", IEEE Software, Vol. 11, No. 6, pp. 70-77, 1994.

Definitions

� The communication between user and the

system - Dix et al., 1998

� Direct manipulation and instantaneous change

- Becker et al., 1987

“HCI research is far from having solid (and

falsifiable) theories of interaction”

- Beaudouin-Lafon, 2004

John Stasko

Two Pillars of InfoVis

� Representation & Interaction

� Representation gets all the attention

� very challenging to come up with innovative, new

visual representations

� Interaction is where the action is

�we can do interesting work with how user

interacts with the view or views

�what distinguishes InfoVis from static visual

representations on paper

Jinwook Seo/John Stasko

� Different feedback time scales� Shall I wait for that task to finish or go for coffee?

� Different feedback techniques� Short transaction: hour glass cursor

� Longer transaction: estimate of time left

� An overestimate is always better!

Being Interactive

> 10s User will switch to another task while waiting

10s Difficult to stay focused, cognitive response

1s Delay but user’s flow of thought is uninterrupted, conversation break

.1s Causality, visual continuity, animation, sliders

cancel

Contacting host (10-60 seconds)

Simple but Powerful Interaction

� Dancing Histograms

� Digital History

� social, political

and cultural events

in a life span

http://www.hiraeth.com/alan/topics/vis/hist.html

http://www.digitalhistory.uh.edu/timeline/timelineO.cfm

Types of Interaction� Dix and Ellis (AVI ’98) propose

�Highlighting and focus

� Accessing extra info – drill down and hyperlinks

�Overview and context – zooming and fisheyes

� Same representation, changing parameters

� Same data, changing representation

� Linking representations – temporal fusion� successive images, moving images, simultaneous change

3444

33

811

639

89

7

209

111

223

10

531

1200

220

UK

Channel Islands

Midlands

North England

Cumbria

Isle of Man

North West

Northumbria

Yorkshire & Humberside

Northern Ireland

Scotland

South England

Wales

hotels region

tourist board areas

A. Dix and G. Ellis, "Starting Simple - adding value to static visualisation through

simple interaction." Proc. of AVI '98, May 1998, pp. 124-134.

Types of Interaction

� Keim’s taxonomy (TVCG ’02) includes

� Projection

� Filtering

� Zooming

�Distortion

� Linking and brushing

John Stasko

Tasks by Shneiderman

� Overview � Gain an overview of the entire collection

� Zoom � Zoom in on items of interest

� Filter� Filter out uninteresting items

� Details-on-demand

� Select an item or group and get details when needed

� Relate� View relationships among items

� History� Keep a history of actions to support undo, replay, and progressive refinement

� Extract� Allow extraction of sub-collections and of the query parameters

Taxonomy

� Low-level

� system centric

� High-level

� user goal

� Bridge two levels?

� user intent

J.S. Yi, Y.A. Kang, J.T. Stasko and J.A. Jacko, "Toward a Deeper Understanding of

the Role of Interaction in Information Visualization", IEEE Transactions on

Visualization and Computer Graphics, Vol. 13, No. 6, Nov/Dec 2007, pp. 1224-1231.

Categories of Interaction

� Yi et al. (InfoVis 07) propose 7 categories

� Select

� Explore

�Reconfigure

� Encode

� Abstract/Elaborate

� Filter

�Connect

John Stasko

1. Select

“Mark something as interesting”

� Mark items of interest to keep track

� Seems to often work as a preceding action to

subsequent operations

� Selecting a placemark

in Google Map

� The spotlight feature

in TableLens

Table Lens

Dust & Magnet

Labeling of the Selected

� Each label for a data point should:

� Be readable

�Non-ambiguously relate to its graphical object

�Not hide other pertinent information

� Completeness (labeling of all objects) is desired but not always possible

http://graphs.gapminder.org/world/

Spotfire

Excentric Labeling

� Circular focus area

� Description boxes containing the name of the data point

� Line and box color match the color of the data point

� “Invisible” – Does not appear until user hovers over data

points

http://www.cs.umd.edu/hcil/excentric

Fekete, J.-D., Plaisant, C., Excentric Labeling: Dynamic Neighborhood Labeling for Data Visualization, CHI'99, pp. 512-519.

John Stasko

2. Explore

“Show me something different”

� Examine a different subset of data

� Overcome the limitation of display size

� Following hyperlinks

on web pages

� Panning in Google Earth

� Direct Walking in

Visual Thesaurus

http://www.visualthesaurus.com/ John Stasko

3. Reconfigure

� “Show me a different arrangement”

� Provide different perspectives by changing the

spatial arrangement of representation

� Sorting and rearranging columns in TableLens

� Changing the attributes in a scatter plot

� The baseline adjustment feature in Stacked Histogram

John Stasko

4. Encode“Show me a different representation”

� Change visual appearances� Looking for new perspective

� Limited real estate may force change� semantic zooming

� Changing � color encoding

� size

� orientation

� font

� shape

Lam, H. and Baudisch, P.

Summary Thumbnails: Readable Overviews for Small Screen Web Browsers.

In Proceedings of CHI 2005, Portland, OR, Apr 2005, pp. 681-690.

5. Abstract/Elaborate

“Show me more or less detail”

� Adjust the level of abstraction (detail)

� overview and details

� Unfolding sub-categories in an interactive pie chart

� Drill-down in Treemap

� Details-on-demand in Sunburst

� The tool-tip operation in SeeIT

� Zooming (geometric zooming)

Jinwook Seo/John Stasko

Examples

SeeIT

Table Lens

Sunburst

Treemap

6. Filter

“Show me something conditionally”

� Change the set of data items being presented based on some specific conditions

� Focusing

� Narrowing/widening

� Dynamic query (range sliders) in HomeFinder and Spotfire

� Attribute Explorer & Influence Explorer

� Keystoke based filtering in NameVoyager

� QuerySketch

Jinwook Seo/John Stasko

7. Connect

“Show me related items”

� Highlight associations and relationships

� Show hidden data items that are relevant to a

specified item

� Highlighting directly connected

nodes in Vizster

� Brushing

� multiple view coordination (MVC)

Brushing

� Selecting or highlighting a case in one view

generates highlighting the case in the other

views

� Applies when you have multiple views of the

same data

� Very common technique in InfoVis

John Stasko

Example

Laws for Low-level Interaction - Ware

� Choice reaction time

� depends on number of choices

� Selection time: Fitts' Law

� depends on distance, target size

� Path tracing: continuous ongoing control

� speed depends on width ( v=W/c )

� Learning: power law of practice

� also subtask chunking

� Two-handed interaction: Guiard's theory

� coarse vs. fine control

� e.g. paper vs. pen positioning

Jinwook Seo/Tamara Munzner

Hick’s law� Time to choose one out of n actions : H=a + b log2(n+1)

� log rather than linear

• One menu of eight items vs. Two menus of four items

Fitts’ Law for position selection

� Describe an iterative process of eye-hand coordination

� MeanTime = C1 + C2log2(D/W + 0.5) (1)

� MeanTime = C1 + C2log2(D/W + 1.0) (2)

� Index of difficulty = log2(D/W + 1.0)

� Index of performance = 1/C2

� About 5-8 bits per second (many devices)

Colin Ware

Fitts’s Law

)/2(log2 SDITM

=

T: movement time

S: target width

D: distance to target

IM: index of performance

63 msec/bit [22~122 ms/bit], (fastman~slowman)

: index of difficulty)/2(log2 SD

)1/(log2 += SDITM

)5.0/(log2 += SDITM

Closed Loop vs. Open Loop

� Closed Loop

� Feedback from perception through cognitive to

motor

� Examples?

� Open Loop

�Control is planned in advance and motor

executes without perception or cognitive

� Examples?

Closed Control Loop

Update displayMeasurehand position

Judge distanceto target

Effect handmovement

Yes

No

Human Processing

Machine Processing

Detect startsignal

InTarget?

On to next task

Colin Ware

Fitts’s Law

� Relies on “Closed Loop” control of Motor System

� Implications:� Larger (Closer) targets are easier to click

� Macintosh menu bar is faster to use (correction time)

� Pie menu is faster than popup menu

Images from Wikipedia

Power Law of Practice

� Time to perform a task is proportional to the log of the number of times the task has been carried out.

log(Tn) = log(T1) - αlog(n)

� The law is usually expressed in terms of blocks of trials since the time to perform a single trial is too variable.

Colin Ware

Learning (“Learning and memory” Anderson)

� Power law of learning

� Law of Diminishing Returns

Problem solving

Writing books

Manual skill

Two-handed Interaction

� Guiad’s kinematic chain theory (1987)

� left and right hands form a kinematic chain

� left providing a frame of reference for movements

with the right

� template on left hand, paint with right hand

� left hand to position tool palettes, right hand to do

drawing operations

Example

� Toolglass and Magic Lens

� interactive filter as transparent window

� left hand to move the window

� right hand to control cursor

K. Fishkin and M. Stone, "Enhanced dynamic queries via movable filters."

Proc. of CHI '95, pp. 415-420.

Toolglass and magic lenses: the see-through interface Eric A. Bier, Maureen

C. Stone, Ken Pier, William Buxton, and Tony D. DeRose, Proc.

SIGGRAPH'93, pp. 73-76.

Credits

� John Stasko, Georgia Tech

�www.cc.gatech.edu/~stasko/7450/

� Tamara Munzner, UBC

� people.cs.ubc.ca/~tmm/courses/533-09/

� Colin Ware, U of NH

� http://ccom.unh.edu/vislab/VisCourse/index.html