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Marti HearstSIMS 247
SIMS 247 Lecture 24 SIMS 247 Lecture 24 Course RecapCourse Recap
April 30, 1998April 30, 1998
Marti HearstSIMS 247
Where Have We Been?Where Have We Been?
• Information VisualizationInformation Visualization– recent surge of interest
• more online information• more computing power
– a developing area• many exciting new ideas, but• little theory• little empirical validation or evaluation
• Tufte’s InfluenceTufte’s Influence
Marti HearstSIMS 247
Graphic Display of Abstract DataGraphic Display of Abstract Data
• Data typesData types– nominal, ordered, quantitative
• Anatomy of a graphAnatomy of a graph– framework, content, labels, background– graphs, charts, maps, diagrams
• Conventional graphsConventional graphs– when to use which– how not to mislead
Marti HearstSIMS 247
Hypothetical GraphsHypothetical Graphs
length of page
leng
th o
f ac
cess
URL
# of
acc
esse
s
length of access#
of a
cces
ses
length of access
leng
th o
f pa
ge05
1015202530354045
shor
t
med
ium
long
very
long
days
# of
acc
esse
s
url 1url 2url 3url 4url 5url 6url 7
# of accesses
Marti HearstSIMS 247
Mapping Types in ChartsMapping Types in Charts
one-to-one
one-to-many many-to-many
Marti HearstSIMS 247
How to show link patterns in web How to show link patterns in web access example? access example?
Problem: only shows one stepThink about this for next time.
Marti HearstSIMS 247
Graphing Multivariate InformationGraphing Multivariate Information
• How to handle more than 3 How to handle more than 3 variables?variables?– multifunctioning elements– multiple views– brushing and linking– animation
Marti HearstSIMS 247
Multiple Views: Star PlotMultiple Views: Star Plot(Discussed in Feinberg 79. Works better with animation. Example (Discussed in Feinberg 79. Works better with animation. Example
taken from Behrans & Yu 95.)taken from Behrans & Yu 95.)
Marti HearstSIMS 247
Linked ScatterplotsLinked Scatterplots
Marti HearstSIMS 247
Chernoff Experiment Chernoff Experiment (Marchette)(Marchette)
Marti HearstSIMS 247
Overlaying Space and TimeOverlaying Space and Time(Minard’s graph of Napolean’s march through Russia)(Minard’s graph of Napolean’s march through Russia)
Marti HearstSIMS 247
Multiple Dimensions: Parallel CoordinatesMultiple Dimensions: Parallel Coordinates(earthquake data, color indicates longitude, y axis severity (earthquake data, color indicates longitude, y axis severity
of earthquake, from Schall 95)of earthquake, from Schall 95)
Marti HearstSIMS 247
Baseball data:Baseball data:Scatterplots and histograms and barsScatterplots and histograms and bars
(from Wills 95)(from Wills 95)
select highsalaries
avg careerHRs vs avg career hits(batting ability)
avg assists vsavg putouts (fielding ability)
how longin majors
distributionof positionsplayed
Marti HearstSIMS 247
Restrict the range of parameter Restrict the range of parameter settings. How many constraints settings. How many constraints
away from success? away from success? (Tweedie et al. 96)(Tweedie et al. 96)
Coding seems complex initially, but suits the designers’ needs and is easily learned.
Marti HearstSIMS 247
Dynamic QueriesDynamic Queries
• Instead of a formal database languageInstead of a formal database language• Explore a dataset interactivelyExplore a dataset interactively• Use graphical devices to interactively Use graphical devices to interactively
update a visualizationupdate a visualization– Examples
• Ahlberg & Shneiderman 93 Filmfinder, etc.• Roth et al. 96 VISAGE• Woodruff et al. DataSplash• Fishkin, Stone, Bier et al. Magic Lenses/Toolglass
Marti HearstSIMS 247
VISAGE display VISAGE display (Roth et al. 96)(Roth et al. 96)
Marti HearstSIMS 247
Click-through operatorsClick-through operatorsExample: change underlying colorExample: change underlying color
(Bier et al. 93)(Bier et al. 93)
Original Change Fill Color
Change Outline Color
Marti HearstSIMS 247
Directly View and Change Font Directly View and Change Font CharacteristicsCharacteristics
(Bier et al. 93)(Bier et al. 93)
Marti HearstSIMS 247
Viewing Huge DatasetsViewing Huge Datasets
• Problem: Problem: – The computer display is a small window
through which to view huge datasets
• Standard Solution:Standard Solution:– Display a portion at a time
Problems: lose the context, get lost, comparisons are difficult, ...
• Alternative Solution:Alternative Solution:– Focus + Context
Marti HearstSIMS 247
Focus + ContextFocus + Context
• Another solution:Another solution:– Use pixels more carefully
• Focus + ContextFocus + Context– Show a lot of information at once
• Details are too small to be visible
– Focus on a subset of interest• Make this subset large enough to be
viewed
Marti HearstSIMS 247
Focus + Context Data TypesFocus + Context Data Types
• TablesTables• HierarchiesHierarchies• NetworksNetworks• MapsMaps• Artificial “worlds”Artificial “worlds”
Marti HearstSIMS 247
Viewing Huge Tables:Viewing Huge Tables:Table Lens Table Lens (Rao & Card 94)(Rao & Card 94)
Marti HearstSIMS 247
Distortion TypesDistortion Types
• Different distortions for different Different distortions for different data types yield different effectsdata types yield different effects– cartesian, polar coordinates,
hyperbolic
• Leung & Apperley’s TaxonomyLeung & Apperley’s Taxonomy– distinguish focus+context from
distortion• f+c requires a POI function
Marti HearstSIMS 247
Distortion TechniquesDistortion Techniques
• Computation must take care not to Computation must take care not to let the magnified part overlap or let the magnified part overlap or cover up the de-magnified partcover up the de-magnified part
• The boundary between the magnified The boundary between the magnified and the demagnified parts of the and the demagnified parts of the viewview– Some techniques have an abrupt
boundary– Some are more gradual
Marti HearstSIMS 247
Noik’s DemonstrationNoik’s Demonstration
Marti HearstSIMS 247
Elements of Fisheye ViewsElements of Fisheye Views
• Focus, or Point of Interest (POI)Focus, or Point of Interest (POI)– user-selected
• Importance Function (API)Importance Function (API)– user-specified or pre-determined
• number of railway connections• height in hierarchy• population of city
• Function for measuring distance Function for measuring distance between objectsbetween objects
Marti HearstSIMS 247
Properties of GraphsProperties of Graphs
• Edges can be Edges can be directeddirected– can go from A to B, but not from B to
A– use arrows to show directedness
• Graphs can have Graphs can have cyclescycles– can get back to B when starting from
B
A
B C
DE
Marti HearstSIMS 247
Sar
kar
& B
row
n 9
4S
ark
ar &
Bro
wn
94
Par
is M
etro
, im
port
ance
cor
resp
onds
to
num
ber
of c
onne
ctio
ns
Par
is M
etro
, im
port
ance
cor
resp
onds
to
num
ber
of c
onne
ctio
ns
Marti HearstSIMS 247
Sar
kar
& B
row
n 9
4S
ark
ar &
Bro
wn
94
dis
tort
ion
leve
l 5 v
s. 2
dis
tort
ion
leve
l 5 v
s. 2
Marti HearstSIMS 247
Perspective Wall Perspective Wall (Mackinlay et al. 91)(Mackinlay et al. 91)
Marti HearstSIMS 247
Force-Directed PlacementForce-Directed Placement(Amir 93, based on Fruchterman and Rheingold 90)(Amir 93, based on Fruchterman and Rheingold 90)
Marti HearstSIMS 247
All About TreesAll About Trees
Marti HearstSIMS 247
Hyperbolic BrowserHyperbolic Browser
• Focus + Context TechniqueFocus + Context Technique– detailed view blended with a global view
• First lay out the hierarchy on Poincare’ First lay out the hierarchy on Poincare’ mapping of the hyperbolic planemapping of the hyperbolic plane
• Then map this plane to a diskThen map this plane to a disk• Use animation to navigate along this Use animation to navigate along this
representation of the planerepresentation of the plane• Start with the tree’s root at the centerStart with the tree’s root at the center
Marti HearstSIMS 247
Hyperbolic Tree Browser Hyperbolic Tree Browser (Lamping et al. 95)(Lamping et al. 95)
Marti HearstSIMS 247
Cluster-graphs Cluster-graphs (Eades & Qingwen 96)(Eades & Qingwen 96)
tree-like between planes graph-like within planes
Marti HearstSIMS 247
Con
eTre
es
Con
eTre
es (R
ober
tson
et
al. 9
1)(R
ober
tson
et
al. 9
1)
Marti HearstSIMS 247
Con
eTre
es
Con
eTre
es (R
ober
tson
et
al. 9
1)(R
ober
tson
et
al. 9
1)
Marti HearstSIMS 247
Hyp
erb
olic
Con
eTre
esH
yper
bol
ic C
oneT
rees
(Mu
nzn
er e
t al
. 96)
(Mu
nzn
er e
t al
. 96)
Marti HearstSIMS 247
Multi-Trees Multi-Trees (Furnas & Zachs 94)(Furnas & Zachs 94)
• Often we want more than one view on a Often we want more than one view on a treetree
• Multi-trees convert the view of a dag Multi-trees convert the view of a dag (directed acyclic graph) into a set of (directed acyclic graph) into a set of overlapping treesoverlapping trees
Marti HearstSIMS 247
Why do Evaluation?Why do Evaluation?
• To tell how good or bad a visualization isTo tell how good or bad a visualization is– People must use it to evaluate it– Must compare against the status quo– Something that looks useful to the designer might
be too complex or superfluous for real users
• For iterative designFor iterative design– Interface might be almost right but require
adjustments– The interactive components might have problems
• To advance our knowledge of how people To advance our knowledge of how people understand and use technologyunderstand and use technology
Marti HearstSIMS 247
Visual PropertiesVisual Properties
Hue based boundary determined preattentively regardlessof variation in form (left). However, the converse is not true (right).
Marti HearstSIMS 247
Accuracy Ranking of Quantitative Perceptual TasksAccuracy Ranking of Quantitative Perceptual Tasks(Mackinlay 88 from Cleveland & McGill)(Mackinlay 88 from Cleveland & McGill)
Position
Length
Angle Slope
Area
Volume
Color Density
More Accurate
Less Accurate
Marti HearstSIMS 247
Visual IllusionsVisual Illusions
• Mueller-Lyon (off by 25-30%)Mueller-Lyon (off by 25-30%)
• Horizontal-VerticalHorizontal-Vertical
Marti HearstSIMS 247
Pan and ZoomPan and Zoom
Marti HearstSIMS 247
Space-Scale DiagramsSpace-Scale Diagrams(Furnas & Bederson 95)(Furnas & Bederson 95)
• We can think of this in terms of 1D tooWe can think of this in terms of 1D too• When zoomed out, you can see wider When zoomed out, you can see wider
set of pointsset of points
Marti HearstSIMS 247
Why Text is ToughWhy Text is Tough
As the man walks the cavorting dog, thoughtsarrive unbidden of the previous spring, so unlikethis one, in which walking was marching anddogs were baleful sentinals outside unjust halls.
How do we visualize this?
Marti HearstSIMS 247
BE
AD
(C
hal
mer
s 96
)B
EA
D (
Ch
alm
ers
96)
An example layout produced by Bead, seen in over-view,of 831 bibliography entries from CHI, CSCW and UISTconferences. The dimensionality (the number of unique words inthe set) is 6925 and the layout stress is 0.16. After a search for‘cscw or collaborative’ we see the pattern of occurrencescoloured dark blue, mostly to the right. The central rectangle isthe visualiser’s motion control.
Marti HearstSIMS 247
Example: ThemescapesExample: Themescapes(Wise et al. 95)(Wise et al. 95)
Themescapes (Wise et al. 95)
Marti HearstSIMS 247
Koh
onen
Fea
ture
Map
sK
ohon
en F
eatu
re M
aps
(Lin
92,
Ch
en e
t al
. 97)
(Lin
92,
Ch
en e
t al
. 97)
(594 docs)
Marti HearstSIMS 247
InfoCrystal InfoCrystal (Spoerri 93)(Spoerri 93)
A
C
B
D
1
34
27
9
201
# of docscontaingA, C, and B
# of docscontaing A
# of docscontaingB and D
Marti HearstSIMS 247
Marti HearstSIMS 247
See
Sof
t: C
han
ges
of L
ines
of
Cod
e ov
er T
ime
See
Sof
t: C
han
ges
of L
ines
of
Cod
e ov
er T
ime
(Eic
k 9
4)(E
ick
94)
Marti HearstSIMS 247
Guest LecturesGuest Lectures
• Color: Maureen StoneColor: Maureen Stone• DB Pan & Zoom: Allison WoodruffDB Pan & Zoom: Allison Woodruff• Design: Delle MaxwellDesign: Delle Maxwell• 3D Interaction: Tamara Munzner3D Interaction: Tamara Munzner• Animation: Bay-wei ChangAnimation: Bay-wei Chang• Interactive Design: Robert ReimannInteractive Design: Robert Reimann• Automated Graph Layout: Mike SchiffAutomated Graph Layout: Mike Schiff• Data Mining and Viz: Ronny KohaviData Mining and Viz: Ronny Kohavi• Texture and Visual Search: Ruth RosenholtzTexture and Visual Search: Ruth Rosenholtz
Marti HearstSIMS 247
… … and Finallyand Finally
Class Projects!!!Class Projects!!!
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