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CS-5630 / CS-6630 Visualization Tables Alexander Lex - - PowerPoint PPT Presentation

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CS-5630 / CS-6630 Visualization Tables Alexander Lex alex@sci.utah.edu [xkcd] dataset types spatial channels are the most effective for all attribute types recall: attribute semantics when we arrange tabular data, attributes are chosen to

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CS-5630 / CS-6630 Visualization Tables

Alexander Lex alex@sci.utah.edu

[xkcd]

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dataset types

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spatial channels are the most effective for all attribute types

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recall: attribute semantics

when we arrange tabular data, attributes are chosen to be keys and values

multidimensional

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Scale of Tables

Need different approaches for “normal” and “high- dimensional” tables.

Homogeneity

Same data type? Same scales?

Age Gender Height Bob 25 M 181 Alice 22 F 185 Chris 19 M 175 BPM 1 BPM 2 BPM 3 Bob 65 120 145 Alice 80 135 185 Chris 45 115 135

How many dimensions?

~50 – tractable with “just” vis ~1000 – need analytical methods

How many records?

~ 1000 – “just” vis is fine >> 10,000 – need analytical methods

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Analytic Component

no / little analytics strong analytics 
 component

Scatterplot Matrices


[Bostock]

Parallel Coordinates


[Bostock]

Pixel-based visualizations /
 heat maps Multidimensional Scaling

[Doerk 2011] [Chuang 2012]

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Express Values

No Keys

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encode using zero keys: scatterplots

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Encode one Key Attribute

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encode one key attribute:
 bar, dot, & line charts

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Encode Multiple Key Attributes

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Stacked Bar Chart

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Comparison of bar chart types

Small 
 Multiples Stacked bar chart Pie Chart Layered
 Bar
 Chart Grouped
 Bar 
 Chart

Streit & Gehlenborg, PoV, Nature Methods, 2014

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Stacked Area Chart

http://stackoverflow.com/questions/2225995/how-can-i-create-stacked-line-graph-with-matplotlib

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100% Stacked Area Chart

http://stackoverflow.com/questions/16875546/create-a-100-stacked-area-chart-with-matplotlib

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Stacked Area vs. Line Graphs

leancrew.com & Practically Efficient

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VizWiz, A. Kriebel

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Table Lens

Rao & Card 1994

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Bertifier

Matrix/Table representation Authoring Interface

http://www.aviz.fr/bertifier Charles Perin, Pierre Dragicevic and Jean-Daniel Fekete

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LineUp

Video at http://lineup.caleydo.org

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Rankings are popular

23

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University Harvard, ¡USA Oxford, ¡UK Cambridge, ¡UK Princeton, ¡USA MIT, ¡USA Rank 2. 5. 4. 3. 1. Score 84.2 44.0 64.3 73.8 89.4 Score

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25

Support Multiple Attributes

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University Harvard, ¡USA Oxford, ¡UK Cambridge, ¡UK Princeton, ¡USA MIT, ¡USA Rank 2. 5. 4. 3. 1. Score A B C

Score ¡= ¡f(A, ¡B, ¡C)

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Combiner functions: f(A,B,C)

(Weighted) sum
 Score = wa A + wb B + wc C Maximum
 Score = max(A, B, C) Product Nesting …

àSerial ¡ àParallel ¡ àComplex
 ¡Combiners ¡

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Serial Combiner

University Harvard, ¡USA Oxford, ¡UK Cambridge, ¡UK Princeton, ¡USA MIT, ¡USA Rank 2. 5. 4. 3. 1. A B C

¡ ¡ ¡ ¡ ¡ ¡wa ¡A ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡+ ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡wb ¡B ¡ ¡ ¡ ¡ ¡ ¡ ¡+ ¡ ¡ ¡ ¡ ¡ ¡ ¡wc ¡C

(as Stacked Bar)

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Serial Combiner

University Harvard, ¡USA Oxford, ¡UK Cambridge, ¡UK Princeton, ¡USA MIT, ¡USA Rank 2. 5. 4. 3. 1. A B C (as Stacked Bar)

wa ¡A + + wb ¡B wc ¡C

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Serial Combiner

University Harvard, ¡USA MIT, ¡USA Rank 2. 5. 4. 3. 1. Oxford, ¡UK Cambridge, ¡UK Princeton, ¡USA A B C (as Stacked Bar)

wa ¡A + + wb ¡B wc ¡C

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Flexible Mapping of
 Attributes to Scores

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Min Max 100

1

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100

1

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100

1

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Compare Rankings

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Bump Charts

Rank

2. 5. 4. 3. 1. Score University Harvard, ¡USA Oxford, ¡UK Cambridge, ¡UK Princeton, ¡USA MIT, ¡USA Rank 2. 5. 4. 3. 1. Score Score

(+1) (-­‑2) (+1)

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Bump Charts

Rank

2. 5. 3. 1. Score University Oxford, ¡UK Cambridge, ¡UK Princeton, ¡USA MIT, ¡USA Rank 5. 4. 3. 1. Score Score

(+1)

4. Harvard, ¡USA 2.

(-­‑2) (+1)

4. Harvard, ¡USA 2.

(-­‑2)

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Video showing:

  • Creating snapshot for comparison
  • Play with weights
  • Show delta
  • Select by clicking on slopegraph
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http:/ /lineup.caleydo.org

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Pixel Based Displays

Each cell is a “pixel”, value 
 encoded in color / value Ordering critical for interpretation If no ordering inherent, 
 clustering is used Scalable – 1 px per item Good for homogeneous data

same scale & type

[Gehlenborg & Wong 2012]

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3D Pitfall: Occlusion & Perspective

[Gehlenborg and Wong, Nature Methods, 2012]

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3D Pitfall: Occlusion & Perspective

[Gehlenborg and Wong, Nature Methods, 2012]

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Heterogeneous Data?

[Verhaak 2012]

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Bad Color Mapping

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Good Color Mapping

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Color is relative!

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Clustered Heat Map

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Multiple Line Charts

http://square.github.io/cubism/

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Combining Various Charts

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Design Critique

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Document: https://goo.gl/W6w0iI Website: http://goo.gl/D3mIsy

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Spatial Axis Orientation

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spatial axis orientation

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Spatial Axis Orientation

Scatterplot Matrix

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Scatterplot Matrices (SPLOM)

Matrix of size d*d Each row/column is one dimension Each cell plots a scatterplot of two dimensions

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Scatterplot Matrices

Limited scalability (~20 dimensions, ~500-1k records) Brushing is important Often combined with “Focus Scatterplot” as F+C technique Algorithmic approaches: Clustering & aggregating records Choosing dimensions Choosing order

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SPLOM Aggregation - Heat Map

Datavore: http://vis.stanford.edu/projects/datavore/splom/

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SPLOM F+C, Navigation

[Elmqvist]

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Spatial Axis Orientation

Parallel Coordinates

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Parallel Coordinates (PC)

Axes represent attributes Lines connecting axes represent items

Inselberg 1985

A B X Y X Y A B A B

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Parallel Coordinates

Each axis represents dimension Lines connecting axis represent records Suitable for

all tabular data types heterogeneous data

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PC Limitation: 
 Scalability to Many Dimensions

500 axes

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PC Limitation: Scalability to Many Items

Solutions:

Transparency Bundling, Clustering Sampling

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PC Limitations 


Correlations only between adjacent axes

Solution: Interaction

Brushing Let user change order

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PC Limitation: 
 Ambiguity

Solutions:

Brushing Curves

Graham and Kennedy 2003

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Parallel Coordinates

Shows primarily relationships between adjacent axis Limited scalability (~50 dimensions, ~1-5k records)

Transparency of lines

Interaction is crucial

Axis reordering Brushing Filtering

Algorithmic support: Choosing dimensions Choosing order Clustering & aggregating records

http://bl.ocks.org/jasondavies/1341281

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HIERARCHICAL PARALLEL COORDINATES

goal: scale up parallel coordinates to large datasets

challenge: overplotting/occlusion

Fua 1999

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HPC: ENCODING DERIVED DATA

visual representation: variable- width opacity bands

show whole cluster, not just single item min / max: spatial position cluster density: transparency mean: opaque

Fua 1999

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HPC: INTERACTING WITH DERIVED DATA

interactively change level of detail to navigate cluster hierarchy

Fua 1999

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Star Plot

Similar to parallel coordinates Radiate from a common origin

[Coekin1969]

http://www.itl.nist.gov/div898/handbook/eda/section3/starplot.htm http://start1.jpl.nasa.gov/caseStudies/autoTool.cfm

http://bl.ocks.org/kevinschaul/raw/8833989/

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Data Reduction

Sampling

Don’t show every element, show a (random) subset Efficient for large dataset Apply only for display purposes Outlier-preserving approaches

Filtering

Define criteria to remove data, e.g.,

minimum variability > / < / = specific value for one dimension consistency in replicates, …

Can be interactive, combined with 
 sampling

[Ellis & Dix, 2006]

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Spatial Axis Orientation

Hybrids

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Flexible Linked Axes (FLINA)

Claessen & van Wijk 2011

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Web-based implementation of 
 FLINA concept

http://vis.pku.edu.cn/mddv/val/ ¡

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Connected Charts

Viau ¡& ¡McGuffin ¡2012 ¡

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  • rigin

ARTISTS Australia Europe North America studio albums WcountH continent first album WyearH number one hits

5 Countries 5 Artists

start of career WyearH career status in business at first album inactive gender gender ∩ inactive sold albums WabsoluteH COUNTRIES population WmillionH Barbados Ireland Sweden UK US

Rihanna U2 ABBA Elton John The Beatles Whitney Houston The Black Eyed Peas Britney Spears Eminem Michael Jackson Madonna Elvis Presley Australia France Italy Sweden Span Austria Germany Netherlands Ireland UK US Canada

inactive active male group female

Artists Countries 12 12 1

Domino

Gratzl ¡et ¡al. ¡2014 ¡

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Spatial Axis Orientation

Parallel Sets

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Parallel Sets

builds on PC to better handle categorical data

discrete small number of values no implied ordering between attributes

task: find relationship between attributes interaction driven technique

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Visual Encoding

boxes scaled by frequency color coded by values for current active dimension

Bendix, Kosara, Hauser, 2005

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Bendix, Kosara, Hauser, 2005

Visual Encoding

  • boxes expand to show histogram
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Bendix, Kosara, Hauser, 2005

Interaction: Reorder

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Bendix, Kosara, Hauser, 2005

Interaction: Aggregate

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Bendix, Kosara, Hauser, 2005

Interaction: Filter

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Bendix, Kosara, Hauser, 2005

Interaction: Highlight

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Filling Space

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filling space

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Dense pixel display: VisDB

represent each data item, or each attribute in an item as a single pixel can fit as many items on the screen as there are pixels,

  • n the order of millions

relies heavily on color coding challenge: what’s the layout?

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The data…

large database where each item has multiple attributes (on the order of 10) goal: visualize the relevance of set of items which satisfy a query plot out data items in a spiral pattern,

  • rdered by relevance

Keim, Kreigel, 1994

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relevance

  • dim. 1
  • dim. 2
  • dim. 3
  • dim. 4
  • dim. 5

factor

Keim, Kreigel, 1994

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  • c. Grouping Arrangement
  • a. Basic Visualization Technique

Keim, Kreigel, 1994