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2/20/2014 Comp/Phys/APSc 715 Multivariate & Ensemble Visualization Techniques 2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor 1 Preview Videos Vis 97: Visualization of Music (video) 2/20/2014 Multivariate Comp/Phys/APSc 715

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SLIDE 1

2/20/2014 1 Comp/Phys/APSc 715

Multivariate & Ensemble Visualization Techniques

2/20/2014 Multivariate 1 Comp/Phys/APSc 715 Taylor

Preview Videos

  • Vis ’97: Visualization of Music (video)

2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor 2

Administrative

  • HW2 due tonight

– Private posts to the homework page – No peeking at image files for other users before turning yours in

  • HW4 posting by tonight

2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor

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SLIDE 2

2/20/2014 2

Team Dynamics

  • Working in teams is…

– Good, because you can do more work – Hard, because of scheduling, communication, expectation management

  • Scheduling: Right After Class Find Partner
  • Communications/Expectation Management

– Default: Work together on this at the same time – Clearly split the work and provide hard deadlines – Everyone participates equally: one member is not supposed to be doing all the work

2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor 4 2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor 5

Multivariate Data Display

  • At the frontier of data visualization
  • More art than science

– Several combinations can show 2-3 data sets – Attempting combinations beyond this is difficult

  • Perceptual studies can help predict effectiveness

– Avoiding interfering techniques gets you further – Still need to try it out and see

  • Easier in 2D than 3D
  • Several techniques shown today, some with

characteristics listed

2/20/2014 Multivariate 6 Comp/Phys/APSc 715 Taylor

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SLIDE 3

2/20/2014 3

Multivariate Display Techniques

  • Glyphs
  • Heterogeneous Techniques
  • Texture
  • Layering/Subdividing
  • Data Reduction

2/20/2014 Multivariate 7 Comp/Phys/APSc 715 Taylor 2/20/2014 Multivariate 8 Comp/Phys/APSc 715 Taylor

Glyphs

  • Single graphical icon displaying multiple variables

– Shape, color, other features

  • Designed for discrete, non-spatial data
  • Can be used to display fields

– Scatter within 2D or 3D space – Display local characteristics

2/20/2014 Multivariate 9 Comp/Phys/APSc 715 Taylor

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SLIDE 4

2/20/2014 4

Classical Glyphs: Chernoff Faces

2/20/2014 Multivariate 10 Comp/Phys/APSc 715 Taylor

Glyph Techniques

  • Paul Ferry, SKIGRAPH ’99

– Profile Icon, Star Icon, Stick figure Icon

  • Ware

– Probably only 3-4 distinguishable orientations – Don’t use parallel ones (as the figure on the right above does) – Varying color polarity adds more – Varying line width adds more

2/20/2014 Multivariate 11 Comp/Phys/APSc 715 Taylor

Stick hard to use

Glyphs: Color + Size Vary

2/20/2014 Multivariate 12 Comp/Phys/APSc 715 Taylor

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SLIDE 5

2/20/2014 5

Glyph: Flow Probe

  • Wijk, J.J. van, A.J.S. Hin, W.C. deLeeuw, F.H. Post, “Three Ways to Show 3D

Fluid Flow.” IEEE Computer Graphics and Applications, vol. 14, no. 5, p. 33-39, September 1994.

2/20/2014 Multivariate 13 Comp/Phys/APSc 715 Taylor

Characteristics of Glyphs

  • Preattentive detection rules from before apply

– size, orientation, and color coding

  • Integral vs. Separable dimensions

– Integral dimensions are perceived holistically (upper) – Separable dimensions perceived independently (lower)

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  • Attributes:
  • Sirens’ Song:

2/20/2014 Multivariate 15 Comp/Phys/APSc 715 Taylor

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SLIDE 6

2/20/2014 6

Number of Displayable Values

  • Many dimensions not independent

– Texture relies on at least one color difference – Blinking and motion coding will interfere – Fortunate if you can display 8-dimensional data with color, shape, spatial position (not for glyphs in space), and motion.

  • Number of resolvable steps in each dimension

– Maybe 4 values for each – Disallowing conjunction searches leaves 32 alternatives

  • ~4 values for each of 8 channels – 6 in spatial data

– We didn’t see more than 3 work together at high density when doing combinations of different techniques

2/20/2014 Multivariate 16 Comp/Phys/APSc 715 Taylor 2/20/2014 Multivariate 17 Comp/Phys/APSc 715 Taylor

Heterogeneous Techniques

  • “Wandering in the desert”

– “Simpleton” ideas prove their worth

  • Throw a bunch of techniques together
  • Hope for the best

– Works okay for a few data sets – We found it hopeless for large numbers of sets

2/20/2014 Multivariate 18 Comp/Phys/APSc 715 Taylor

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SLIDE 7

2/20/2014 7 Heterogeneous 2D: Location + Width + Color

2/20/2014 Multivariate 19 Comp/Phys/APSc 715 Taylor

Heterogeneous 2D: Height + Texture

UNC Nanoscience

2/20/2014 Multivariate 20 Comp/Phys/APSc 715 Taylor

Heterogeneous 2D: Height + Color + Contour

Non-isoluminant color

UNC Nanoscience

2/20/2014 Multivariate 21 Comp/Phys/APSc 715 Taylor

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SLIDE 8

2/20/2014 8 Heterogeneous 2D: Color + Texture + Bump Tex

UNC Nanoscience

2/20/2014 Multivariate 22 Comp/Phys/APSc 715 Taylor

Heterogeneous 2D

  • Promise

– 1 Height dimension, 2 Color dimensions, 3+ Texture dimensions = 6+ perceptual dimensions

  • Results

– Luminance contrast in color confounds shape – High-frequency components of texture confound color – Multiple textures confound each other

UNC Nanoscience

2/20/2014 Multivariate 23 Comp/Phys/APSc 715 Taylor

Heterogeneous 2D: Height + Color + Glyph

  • Haber, Koh, Lee

– UIUC

  • Found in

– Keller & Keller p. 62

2/20/2014 Multivariate 24 Comp/Phys/APSc 715 Taylor

Rainbow

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SLIDE 9

2/20/2014 9 Heterogeneous 3D: Slice + Contour + Color + Tex.

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Heterogeneous 3D: Surface + Color + Texture

  • Vis 2001: Severance, Lazos, Keefe, “Wind Tunnel Data Fusion

and Immersive Visualization”

26

Rainbow

2/20/2014 Multivariate 27 Comp/Phys/APSc 715 Taylor

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SLIDE 10

2/20/2014 10

Texture-Based Multivariate 2D

  • Varying several characteristics to display data

– Adjusting size, density, and regularity – Adjusting size, orientation, and density – Adjusting scale, orientation, and contrast – Spot Noise: Adjusting orientation and hue/saturation

  • Varying a single characteristic to differentiate between

multiple layers, intensity in each layer (both texturing and layering technique)

– Beyond four scalar fields in the same image – Oriented Slivers – Data-Driven Spots

2/20/2014 Multivariate 28 Comp/Phys/APSc 715 Taylor

Texture Dimensions

  • Chris Healey
  • Height = cultivation level
  • Density = ground type

– Sparse = alluvial – Dense = wetland

  • Grayscale = vegetation

– Dark = plains – Light = forest – White = woodland

2/20/2014 Multivariate 29 Comp/Phys/APSc 715 Taylor

Chris Healey: Size, Density, Regularity, Hue

Sort of like glyphs + arrangement Result is ~texture

2/20/2014 Multivariate 30 Comp/Phys/APSc 715 Taylor

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SLIDE 11

2/20/2014 11 Chris Healey: Size, Density, Orientation, Color

Dense glyphs form a texture

2/20/2014 Multivariate 31 Comp/Phys/APSc 715 Taylor

Texture: Spot Noise

  • Invented by JJ van Wijk, SIGGRAPH 1991

– Spot orientation, spot size, hue – Can vary scale – Can vary shape

  • Affects texture

2/20/2014 Multivariate 32 Comp/Phys/APSc 715 Taylor

Rainbow

Quantitative Texton Sequences for Bivariate Maps (Ware)

2/20/2014 Multivariate 33 Comp/Phys/APSc 715 Taylor

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SLIDE 12

2/20/2014 12

2/20/2014 Multivariate 34 Comp/Phys/APSc 715 Taylor

Layer-Based Multivariate 2D

  • Subdividing the surface
  • Varying a single characteristic to differentiate

between multiple layers, intensity in each layer (both texturing and layering technique)

– Beyond four scalar fields in the same image – Oriented Slivers – Data-Driven Spots

– Nested and intersecting surfaces

  • Layering heterogeneous techniques

– Crawfis – Laidlaw – Urness/Interrante

2/20/2014 Multivariate 35 Comp/Phys/APSc 715 Taylor

Attribute Blocks: Visualizing Multiple Continuously Defined Attributes (James Miller)

2/20/2014 Multivariate 36 Comp/Phys/APSc 715 Taylor

Cyan isoluminant with background

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SLIDE 13

2/20/2014 13

Multivariate Visualization on Parametric Surfaces (James Miller)

2/20/2014 Multivariate 37 Comp/Phys/APSc 715 Taylor

Multivariate Visualization on Parametric Surfaces (James Miller)

2/20/2014 Multivariate 38 Comp/Phys/APSc 715 Taylor

Visualizing Multidimensional Scalar Data Using Hexagonal Tiles (Ramachandran and Healey)

Employment Affluence Bachelor’s degree level Income

New Mexico State

2/20/2014 Multivariate 39 Comp/Phys/APSc 715 Taylor

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SLIDE 14

2/20/2014 14

Visualizing Multidimensional Scalar Data Using Hexagonal Tiles (Ramachandran and Healey)

2/20/2014 Multivariate 40 Comp/Phys/APSc 715 Taylor

Oriented Slivers: Four Tube Orientations

  • Four scalar fields

– Here, 4 orientations – Each mapped to displayed

  • rientation
  • Overall intensity

shows total amount

  • f material

Chris Weigle, UNC

2/20/2014 Multivariate 41 Comp/Phys/APSc 715 Taylor

Oriented Slivers

  • Background color shows another data set

– Reveals dark slivers – Shows region boundary

  • Close-up of 3 data sets

Chris Weigle, UNC

2/20/2014 Multivariate 42 Comp/Phys/APSc 715 Taylor

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SLIDE 15

2/20/2014 15

Oriented Sliver Characteristics

  • User study shows that 15-degree orientation

difference can be easily seen

– Enables 7+ data sets to be displayed!

  • Russ claims:

– Enables relative value estimation for all data sets at a point – Difficult to see boundary of a region with a particular orientation – Easy to see where no data sets are present

2/20/2014 Multivariate 43 Comp/Phys/APSc 715 Taylor

Data-Driven Spots

  • 9 Scalar fields
  • Each mapped to color
  • r bump size
  • Shows regions well

Alexandra Bokinsky, UNC

2/20/2014 Multivariate 44 Comp/Phys/APSc 715 Taylor

DDS Characteristics

  • User studies show

– At least 9 scalar fields can be shown! – Users can attend pairwise to data sets without interference – Boundaries of shapes can be seen as well as when they are drawn explicitly

  • Animation of one or more data sets is very effective

– Reveals areas with low values – Sweeps over entire region, showing boundaries at high resolution – Highlights data set(s) of interest – link to videos (show 3a)

2/20/2014 Multivariate 45 Comp/Phys/APSc 715 Taylor

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SLIDE 16

2/20/2014 16

Evaluation of Trend Localization

  • Mark Livingston, Jonathan Decker; TVCG 2011

– Strokes (intensity, hue, orientation, width, length); DDS; Oriented slivers; Color blend; Attribute blocks tested against each other – Asked for region with largest trend – Had to compare two of the five channels

2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor 46

Evaluation of Trend Localization

  • Mark Livingston, Jonathan Decker; TVCG 2011

– Also compared against side-by-side (“juxtmap”) – County blocks provided local alignment cues

2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor 47

Scaled Data-Driven Spheres (David Feng, UNC)

2/20/2014 Multivariate 48 Comp/Phys/APSc 715 Taylor

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SLIDE 17

2/20/2014 17

2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor

3D DDS?

49

3D SDDS User Study

2/20/2014 Multivariate 50 Comp/Phys/APSc 715 Taylor

Value Estimation Task

3D SDDS User Study

SDDS Superquadrics 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16

Task 1: Value Estimation Error (p<.0001)

Error SDDS Superquadrics 5 10 15 20 25 30 35 40

Task 1: Value Estimation Response Time (p=0.0042)

Response Time(s) 2/20/2014 Multivariate 51 Comp/Phys/APSc 715 Taylor

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SLIDE 18

2/20/2014 18

3D SDDS User Study

Red Yellow Green Cyan Thick XRoundORound Color 0.1 0.2 0.3 0.4

Task 1: Variable Error

Average Error

SDDS SQ

20 40 60 0.1 0.2 0.3 0.4 Trial Number Mean Error

Task 1: Mean Error per Trial

SDDS Superquadrics

2/20/2014 Multivariate 52 Comp/Phys/APSc 715 Taylor

3D SDDS User Study

2/20/2014 Multivariate 53 Comp/Phys/APSc 715 Taylor

Correlation Detection Task

3D SDDS User Study

SDDS Superquadrics 0.2 0.4 0.6 0.8 1

Task 2: Correlation ID Error (p<.0001)

Percent Incorrect SDDS Superquadrics 10 20 30 40 50

Task 2: Correlation ID Response Time (p<.0001)

Response Time(s) 2/20/2014 Multivariate 54 Comp/Phys/APSc 715 Taylor

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SLIDE 19

2/20/2014 19

3D SDDS User Study

5 10 15 20 0.2 0.4 0.6 0.8 1 Trial Number Mean % Correct

Task 2: Mean % Correct per Trial

SDDS Superquadrics Red Yellow Green Cyan Thick XRound ORound Color 1 2 3 4 5 6

Task 2: Correlation ID Error Frequency

Average Number of Errors

SDDS SQ

2/20/2014 Multivariate 55 Comp/Phys/APSc 715 Taylor

3D SDDS Conclusions

  • Layered > Heterogeneous
  • Value Estimation:

– Spheres > Superquadrics – Error ~8% / ~13%

  • Correlation Identification:

– Sphere >> Superquadrics – Error ~20% / ~80%

  • Motion seems to help

2/20/2014 Multivariate 56 Comp/Phys/APSc 715 Taylor

Motion SDDS

  • VDA: Phadke 2012

– Nominal color by ensemble – Sinusoidal scale over time – Compares regions pairwise

2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor 57

Madhura Phadke, NCSU/UNC

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SLIDE 20

2/20/2014 20

Motion SDDS Video

  • Can also vary shape and/or color (click movie)

2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor 58

Madhura Phadke, NCSU/UNC

Nested/Intersecting Surfaces

  • Chris Weigle (UNC) dissertation

– Inner/outer factoring – Transparent outer

  • Colored
  • Surface glyphs

– Drop lines

  • Follow heat transfer

Chris Weigle, UNC

2/20/2014 Multivariate 59 Comp/Phys/APSc 715 Taylor

Intersecting-surface display

Chris Weigle, UNC

2/20/2014 Multivariate 60 Comp/Phys/APSc 715 Taylor

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SLIDE 21

2/20/2014 21

Nested/Intersecting Surfaces

Chris Weigle, UNC

2/20/2014 Multivariate 61 Comp/Phys/APSc 715 Taylor 2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor

Nested/Intersecting User Study

Chris Weigle, UNC

62

Nested/Intersecting User Study

Chris Weigle, UNC

2/20/2014 Multivariate 63 Comp/Phys/APSc 715 Taylor

Without shadows With shadows

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SLIDE 22

2/20/2014 22

Nested/Intersecting User Study

Chris Weigle, UNC Inter-surface Distance Surface Orientation

2/20/2014 Multivariate 64 Comp/Phys/APSc 715 Taylor

Accuracy (more is better) Accuracy (more is better)

Ensemble Display: ESS

  • Ensemble Surface Slicing: VDA Alabi 2012

– Multiple sims – Slice into strips – Color nominally – Animate slicing

2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor 65

Oluwafemi Alabi, UNC

ESS wildfire example

  • Four wildfire simulations
  • Same in upper left, obviously different peak
  • Subtle differences in lower right

2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor 66

Oluwafemi Alabi, UNC

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SLIDE 23

2/20/2014 23

2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor 67

Layering 2D: Crawfis Height + Color + Textures

2/20/2014 Multivariate 68 Comp/Phys/APSc 715 Taylor

Rainbow

Layering 2D: Laidlaw Color + Sparse Glyphs

  • Flow Visualization
  • Black shadow of the geometry
  • Color layer, ellipsoid layer, arrow layer

2/20/2014 Multivariate 69 Comp/Phys/APSc 715 Taylor

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SLIDE 24

2/20/2014 24 Layered 2D: Laidlaw Color + Texture + Sparse Glyphs

  • Mouse spinal cord
  • Texture underlayer
  • Color layer
  • Glyphs

– Ellipsoidal – Textured

2/20/2014 Multivariate 70 Comp/Phys/APSc 715 Taylor

Layered 2D: Texture + Color

  • Urness & Interrante, “Effectively Visualizing Multi-

Valued Flow Data using Color and Texture”

– Color each LIC stroke – Saturation scale – Round-robin colors – Red, Blue, Green, Orange – Vis 2003

2/20/2014 Multivariate 71 Comp/Phys/APSc 715 Taylor

Urness & Interrante Vis 2003 Close-Up

2/20/2014 Multivariate 72 Comp/Phys/APSc 715 Taylor

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SLIDE 25

2/20/2014 25

Urness & Interrante CGA 2006

2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor 73

Urness & Interrante CGA 2006

2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor 74

Urness & Interrante CGA 2006

  • Similar styles interact

– UL: Two textures – ML: Two glyphs – LL: Two line-based

  • Different styles separate

– UR: Glyph + texture – MR: Line + glyph – LR: Line + texture

2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor 75

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SLIDE 26

2/20/2014 26

Layering Summary

  • Layering >> Heterogeneous
  • Layering >> Varying texture parameters
  • Use sparse layers
  • Use distinct display technique for each layer

– Similar: discs of different color – Similar: Slivers of different orientation – Different: Ellipses, arrows – Different: Texture vs. line vs. glyph

2/20/2014 Multivariate 76 Comp/Phys/APSc 715 Taylor 2/20/2014 Multivariate 77 Comp/Phys/APSc 715 Taylor

Problem Reduction Techniques

  • Dimensional reduction / projection
  • Time and space multiplexing

– Multiple views with different mappings – Mapping different fields over time – Dynamic Maps – Magic Lenses

  • Adding computation

– Smart Particles – Cluster analysis / Feature mapping

2/20/2014 Multivariate 78 Comp/Phys/APSc 715 Taylor

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SLIDE 27

2/20/2014 27

Dimension Reduction

  • Principal-component analysis

determines most significant dimensions

– 2D to 1D shown here

  • Project data onto 2D subspace
  • f two largest principal

components

– Color or shape by others

2/20/2014 Multivariate 79 Comp/Phys/APSc 715 Taylor

Multiple views in Space

2/20/2014 Multivariate 80 Comp/Phys/APSc 715 Taylor

Rainbow & Repeats

Multiple views in Space

Vector != 3 scalar

2/20/2014 Multivariate 81 Comp/Phys/APSc 715 Taylor

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SLIDE 28

2/20/2014 28

Multiple views in Space

2/20/2014 Multivariate 82 Comp/Phys/APSc 715 Taylor

Multiple views in Time

  • Cycle data sets through different representations

– Animated – User controlled

  • Overlaid on same spatial location

2/20/2014 Multivariate 83 Comp/Phys/APSc 715 Taylor 2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor 84

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SLIDE 29

2/20/2014 29

Dynamic Maps

  • http://www.geog.le.ac.uk/argus/ICA/J.Dykes/
  • Clicking on 2D (or ND) mapping highlights values

– Column, row, or individual entries in covariance matrix show where on map – Map region highlights entries

2/20/2014 Multivariate 85 Comp/Phys/APSc 715 Taylor

Example Magic Lenses

  • Local Scaling Lens

– Adjusts geometry – Also could be wireframe

  • Gaussian Curvature

– Pseudo-color map – Numeric value overlay

2/20/2014 Multivariate 86 Comp/Phys/APSc 715 Taylor

Magic Lenses

  • Enable viewing a subset of the data sets, and

select others to be viewed in certain areas

– Toolglass and Magic Lenses

  • Eric Bier, Maureen Stone, Ken Pier, William Buxton,

Tony DeRose; Xerox Parc; SIGGRAPH 93

  • Filter the data

– 3D magic lenses: X-ray vision

2/20/2014 Multivariate 87 Comp/Phys/APSc 715 Taylor

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SLIDE 30

2/20/2014 30

Cluster Analysis

  • Map from image (left) to feature space (upper

right)

– Compute statistics on pixels (convolution with Gaussian derivatives) – Produces scatter plot in N-D

  • Look for clusters (or ranges) in feature space

(may be high dimensional space)

– Group these clusters (here by color) – Map back into image space (lower right)

  • “Neighbors in feature space” relationship is

shown

2/20/2014 Multivariate 88 Comp/Phys/APSc 715 Taylor 2/20/2014 Multivariate Comp/Phys/APSc 715 Taylor

Pattern Matching

  • Julia Ebling, “Clifford Convolution And Pattern Matching

On Vector Fields”, Vis 2003

– Select canonical field shape – Find local best orientation – Dot-product-like sum – Produces scalar field

89

Ebling Vis2003 Matching in 3D

2/20/2014 Multivariate 90 Comp/Phys/APSc 715 Taylor

slide-31
SLIDE 31

2/20/2014 31 Interactive Vector Field Feature Identification

  • Joel Haniels II, Arik W. Anderson, Luis Gustavo

Nonato, Claudio Silva, Utah

  • Link to movie

2/20/2014 Multivariate 91 Comp/Phys/APSc 715 Taylor

Conclusions

  • Several example techniques
  • Perceptual analysis of some of them
  • Characteristics known for some of them
  • Still an open area of research

2/20/2014 Multivariate 92 Comp/Phys/APSc 715 Taylor

Run ScalarStack

  • NSRG/CISMM Scalar Stack Viewer
  • Load Census data
  • C:\Program Files (x86)\CISMM\...
  • Show Colored Slivers
  • Show DDS
  • Show Oriented Slivers

2/20/2014 Multivariate 93 Comp/Phys/APSc 715 Taylor

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SLIDE 32

2/20/2014 32

2/20/2014 Multivariate 94 Comp/Phys/APSc 715 Taylor

References

  • Texture Dimensions: Chris Healey

(http://www.csc.ncsu.edu/faculty/healey/HTML_pap ers/plankton/plankton.html)

  • Typhoon visualization: Chris Healey

(http://www.csc.ncsu.edu/faculty/healey/download/ tvcg.99.pdf)

  • Dense Glyphs/Textons: Chris Healey
  • Three Icons: Paul Ferry, SKIGRAPH 99:

http://pages.cpsc.ucalgary.ca/~jungle/skigraph99/pa pers/ferry.pdf

2/20/2014 Multivariate 95 Comp/Phys/APSc 715 Taylor

References

  • Spot noise image: Wim de Leeuw:

http://www.cwi.nl/~wimc/SN_intro.html

  • Glyph characteristics and use for multidimensional

display: Colin Ware’s book, “Information Visualization.”

  • Cluster Analysis: James Coggins, UNC-CH
  • Oriented Slivers: Chris Weigle, UNC-CH
  • Nested/Intersecting Surfaces: Chris Weigle, UNC.
  • Data-Driven Spots: Alexandra Bokinsky, UNC-CH

2/20/2014 Multivariate 96 Comp/Phys/APSc 715 Taylor