Data Visualization (DSC 530/CIS 602-01)dkoop/dsc530-2016sp/lectures/...D. Koop, DSC 530, Spring 2016...

Data Visualization (DSC 530CIS 602-01)

Tasks amp Design

Dr David Koop

D Koop DSC 530 Spring 2016

Recap (Data)bull Basic Data Types

- Items Attributes Links Positions Grids bull Dataset Types

- Tabular Networks Fields Geometry Sets bull Attribute Types

- Categorical and Ordered (Ordinal and Quantitative) - Ordered Sequential Diverging and Cyclic

bull Tables Keys and Values bull Fields Scalar Vector and Tensor bull Time-Varying Data

2D Koop DSC 530 Spring 2016

22

Fieldattribute

item

Items amp Attributes


Items (Nodes) amp Links


[Bostock 2011]

Item

Links

Positions and Grids


Position Grid

Dataset Types


Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)


Value in cell

Cell

Multidimensional Table

Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types

[Munzner (ill Maguire) 2014]

Fieldattribute

itemcell

Tables


0

0

0

0

0

0

00

5

5

5

5

5

5

55

10

10

10

10

10

10

1010

15

15

15

15

15

15

1515

20

20

20

20

20

20

2020

25

25

25

25

25

25

2525

30

30

30

30

30

30

3030

35

35

35

35

35

35

3535

40

40

40

40

40

40

4040

45

45

45

45

45

45

4545

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)economy (mpg)

30

30

30

30

30

30

3030

35

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35

3535

40

40

40

40

40

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45

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4545

50

50

50

50

50

50

5050

55

55

55

55

55

55

5555

60

60

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60

6060

65

65

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65

6565

70

70

70

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7070

75

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7575

80

80

80

80

80

80

8080cylinders

cylinders

cylinders

cylinders

cylinders

cylinders

cylinderscylinders

100

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100100

150

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150150

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300300

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400400

450

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450

450450

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)displacement (cc)

0

0

0

0

0

0

00

20

20

20

20

20

20

2020

40

40

40

40

40

40

4040

60

60

60

60

60

60

6060

80

80

80

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80

80

8080

100

100

100

100

100

100

100100

120

120

120

120

120

120

120120

140

140

140

140

140

140

140140

160

160

160

160

160

160

160160

180

180

180

180

180

180

180180

200

200

200

200

200

200

200200

220

220

220

220

220

220

220220

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)power (hp)

2000

2000

2000

2000

2000

2000

20002000

2500

2500

2500

2500

2500

2500

25002500

3000

3000

3000

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30003000

3500

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35003500

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40004000

4500

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5000

50005000

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)weight (lb)

8

8

8

8

8

8

88

10

10

10

10

10

10

1010

12

12

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12

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1212

14

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1414

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20

20

20

20

20

20

2020

22

22

22

22

22

22

2222

24

24

24

24

24

24

2424

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)0-60 mph (s)

70

70

70

70

70

70

7070

71

71

71

71

71

71

7171

72

72

72

72

72

72

7272

73

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73

7373

74

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7474

75

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7676

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7777

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7878

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7979

80

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8080

81

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8181

82

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82

8282year

year

year

year

year

year

yearyear

Table Visualizations


[M Bostock 2011]

Networksbull Why networks instead of graphs bull Tables can represent networks

- Many-many relationships - Also can be stored as specific

graph databases or files


Danny Holten amp Jarke J van Wijk Force-Directed Edge Bundling for Graph Visualization

Figure 7 US airlines graph (235 nodes 2101 edges) (a) not bundled and bundled using (b) FDEB with inverse-linear model(c) GBEB and (d) FDEB with inverse-quadratic model

Figure 8 US migration graph (1715 nodes 9780 edges) (a) not bundled and bundled using (b) FDEB with inverse-linearmodel (c) GBEB and (d) FDEB with inverse-quadratic model The same migration flow is highlighted in each graph

Figure 9 A low amount of straightening provides an indication of the number of edges comprising a bundle by widening thebundle (a) s = 0 (b) s = 10 and (c) s = 40 If s is 0 color more clearly indicates the number of edges comprising a bundle

we generated use the rendering technique described in Sec-tion 41 To facilitate the comparison of migration flow inFigure 8 we use a similar rendering technique as the onethat Cui et al [CZQ08] used to generate Figure 8c

The airlines graph is comprised of 235 nodes and 2101edges It took 19 seconds to calculate the bundled airlinesgraphs (Figures 7b and 7d) using the calculation scheme pre-

sented in Section 33 The migration graph is comprised of1715 nodes and 9780 edges It took 80 seconds to calculatethe bundled migration graphs (Figures 8b and 8d) using thesame calculation scheme All measurements were performedon an Intel Core 2 Duo 266GHz PC running Windows XPwith 2GB of RAM and a GeForce 8800GT graphics cardOur prototype was implemented in Borland Delphi 7

c 2009 The Author(s)Journal compilation c 2009 The Eurographics Association and Blackwell Publishing Ltd

Networks


[Holten amp van Wijk 2009]

Fieldsbull Difference between continuous and discrete values bull Examples temperature pressure density bull Grids necessary to sample continuous data

bull Interpolation ldquohow to show values between the sampled points in ways that do not misleadrdquo


Grids (Meshes)bull Meshes combine positional information (geometry) with

topological information (connectivity)

bull Mesh type can differ substantial depending in the way mesh cells are formed

From Weiskopf Machiraju Moumlllercopy WeiskopfMachirajuMoumlller

Data Structures

bull Grid typesndash Grids differ substantially in the cells (basic

building blocks) they are constructed from and in the way the topological information is given

scattered uniform rectilinear structured unstructured[Weiskopf Machiraju Moumlller]

Spatial Data Example MRI


[slide via Levine 2014]

Sets amp Lists


[Daniels httpexperimentsundercurrentcom]

Attribute Types


Attribute Types

Ordering Direction

Categorical Ordered

Ordinal Quantitative

Sequential Diverging Cyclic


Data Model vs Conceptual Modelbull Data Model raw data that has a specific data type (eg floats)

- Temperature Example [325 540 -173] (floats)bull Conceptual Model how we think about the data

- Includes semantics reasoning- Temperature Example

bull Quantitative [3250 5400 -1730]


[via A Lex 2015]




bull Quantitative [3250 5400 -1730]bull Ordered [warm hot cold]


[via A Lex 2015]




bull Quantitative [3250 5400 -1730]bull Ordered [warm hot cold]bull Categorical [not burned burned not burned]


[via A Lex 2015]

Sequential and Diverging Databull Sequential homogenous range

from a minimum to a maximum - Examples Land elevations ocean

depths bull Diverging can be deconstructed

into two sequences pointing in opposite directions - Has a zero point (not necessary 0) - Example Map of both land

elevation and ocean depth


[Rogowitz amp Treinish 1998]

lar structure of spirals allows for an easy detection of cyclesand for the comparison of periodic data sets Furthermorethe continuity of the data is expressed by using a spiral in-stead of a circle

31 Mathematical description and types of spirals

A spiral is easy to describe and understand in polar coor-dinates ie in the form The distinctive feature ofa spiral is that f is a monotone function In this work we as-sume a spiral is described by

Several simple functions f lead to well-known types ofspirals bull Archimedeslsquo spiral has the form It has the spe-

cial property that a ray emanating from the origincrosses two consecutive arcs of the spiral in a constantdistance

bull The Hyperbolic spiral has the form It is theinverse of Archimedeslsquo spiral with respect to the origin

bull More generally spirals of the form are calledArchimedean spirals

bull The logarithmic spiral has the form It has thespecial property that all arcs cut a ray emanating fromthe origin under the same angleFor the visualization of time-dependent data Archimedeslsquo

spiral seems to be the most appropriate In most applicationsdata from different periods are equally important Thisshould be reflected visually in that the distance to other peri-ods is always the same

32 Mapping data to the spiral

In general markers bars and line elements can be usedto visualize time-series data similar to standard point barand line graphs on Spiral Graphs For instance quantitativediscrete data can be presented as bars on the spiral or bymarks with a corresponding distance to the spiral Howeversince the x and y coordinate are needed to achieve the generalform of the spiral their use is limited for the display of datavalues One might consider to map data values to small ab-solute changes in the radius ie

Yet we have found this way of visualizing to be ineffec-tive We conclude that the general shape of the spiral shouldbe untouched and other attributes should be used such asbull colourbull texture including line styles and patterns

Figure 1Two visualizations of sunshine intensity using about the same screen real estate and thesame color coding scheme In the spiral visualization it is much easier to compare days to spotcloudy time periods or to see events like sunrise and sunset

r f ϕ( )=

r f ϕ( ) dfdϕ------ 0 ϕ R+isingt=

r aϕ=

2πar a ϕfrasl=

r aϕk=

r aekϕ=

r aϕ bv ϕ( ) b πamax v ϕ( )( )---------------------------lt+=

Cyclic Data


[Sunlight intensity Weber et al 2001]

Semanticsbull The type of data does not tell us what the data means or how it

should be interpreted bull Tables have keysvalues fields have independentdependent vars


Attribute SemanticsKeys vs Values (Tables) or Independent vs Dependent (Fields)

Flat

Multidimensional

Tabl

es

Fiel

ds


Assignment 1bull httpwwwcisumassdedu

~dkoopdsc530assignment1html bull HTML CSS SVG and JavaScript bull Part 3 will likely take significantly

more time than the other parts bull Turn in a1html (and a1css a1js)

via myCourses bull Due Friday February 12 1159pm bull Late Policy


Part 3 New Bedford Average Temperature by Month (2015)

Assignment 1 Notesbull Grading will be done via Chrome v48 bull For Part 3a you do not need to use all of the functions I list just

avoid loops bull Other Questions


ldquoComputer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectivelyrdquo

mdash T Munzner


Tasksbull Why Understand data but what do I want to do with it bull Levels High (ProduceConsume) Mid (Search) Low (Queries) bull Another key concern Who

- Designer lt-gt User (A spectrum) - Complex lt-gt Easy to Use - General lt-gt Context-Specific - Flexible lt-gt Constrained - Varied Data lt-gt Specific Data


Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes

One ManyDistribution Dependency Correlation Similarity

Network Data

Spatial DataShape

Topology

Paths

Extremes

ConsumePresent EnjoyDiscover

ProduceAnnotate Record Derive

Identify Compare Summarize

tag

Target known Target unknown

Location knownLocation unknown

Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag


Visualization for Consumptionbull Discover new knowledge

- Generate new hypothesis or verify existing one - Designer doesnrsquot know what users need to see

bull Present known information - Presenter already knows what the data says - Wants to communicate this to an audience

bull Enjoy - Similar to discover but without concrete goals


Name Voyager


[Wattenberg 2005 wwwbabynamewizardcom]

Visualization for Productionbull Generate new material bull Annotate

- Add more to a visualization - Usually associated with text but can be graphical

bull Record - Persist visualizations for historical record - Provenance (graphical histories) how did I get here

bull Derive (Transform) - Create new data - Create derived attributes (eg mathematical operations

aggregation)


MTA Fare Data Exploration


Actions Search

bull What does a user know bull Lookup check bearings bull Locate find on a map bull Browse whatrsquos nearby bull Explore where to go (patterns)


Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query

bull Number of targets One Some (Often 2) or All bull Identify characteristics or references bull Compare similarities and differences bull Summarize overview of everything


Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths


Analysis Example Different ldquoIdiomsrdquo


[SpaceTree Grosjean et al] [TreeJuxtaposer Munzner et al]

ldquoIdiomrdquo Comparison


[SpaceTree Supporting Exploration in Large Node Link Tree Design Evolution and Empirical Evaluation Grosjean Plaisant and Bederson Proc InfoVis 2002 p 57ndash64]

SpaceTree

[TreeJuxtaposer Scalable Tree Comparison Using Focus+Context With Guaranteed Visibility ACM Trans on Graphics (Proc SIGGRAPH) 22453ndash 462 2003]

TreeJuxtaposer

Present Locate Identify

Path between two nodes

Actions

Targets

SpaceTree

TreeJuxtaposer

Encode Navigate Select Filter AggregateTree

Arrange

Why What How

Encode Navigate Select





SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How



Analysis Example Derivationbull Strahler number

ndash centrality metric for treesnetworks ndash derived quantitative attribute ndash draw top 5K of 500K for good skeleton


[Using Strahler numbers for real time visual exploration of huge graphs Auber Proc Intl Conf Computer Vision and Graphics pp 56ndash69 2002]

Task 1

58

54

64

84

24

74

6484

84

94

74

OutQuantitative attribute on nodes

58

54

64

84

24

74

6484

84

94

74

InQuantitative attribute on nodes

Task 2

Derive

WhyWhat

In Tree ReduceSummarize

HowWhyWhat

In Quantitative attribute on nodes TopologyIn Tree

Filter

InTree

OutFiltered TreeRemoved unimportant parts

InTree +

Out Quantitative attribute on nodes Out Filtered Tree



mdash T Munzner


Design Iteration


httpchartsnthingstumblrcompost6267976658819-sketches-of-quarterback-timelines

Design Iteration



Design Iteration



Designbull Unlike a math problem there are many different approaches for the

visualization of some data bull Need to have some way to discuss how to determine whether a

visualization is doing what we want bull Validation Understand why a design is effective

- What problems can be effective - Do this at different levels


Four Nested Levels of Design


Datatask abstraction

Visual encodinginteraction idiom

Algorithm

Domain situation

Potential problems at each level


Domain situationYou misunderstood their needs

Yoursquore showing them the wrong thing

Visual encodinginteraction idiomThe way you show it doesnrsquot work

AlgorithmYour code is too slow


Validation at each level


Threat Wrong problem

Threat Wrong taskdata abstraction

Threat Ineffective encodinginteraction idiom

Threat Slow algorithm

Validate Observe and interview target users

Validate Analyze computational complexity

Validate Measure system timememory

Validate Observe adoption rates

Validate Test on target users collect anecdotal evidence of utilityValidate Field study document human usage of deployed system

Validate Qualitativequantitative result image analysis

Validate Lab study measure human timeerrors for task

Validate Justify encodinginteraction design

Implement system

Test on any users informal usability study

Visualization Design Principles

Slides from A Lex


Examples of Bad Design


[httpvisuallywhat-your-brand-color-says-about-your-business via wtfviz]

Recap (Data)bull Basic Data Types

- Items Attributes Links Positions Grids bull Dataset Types

- Tabular Networks Fields Geometry Sets bull Attribute Types

- Categorical and Ordered (Ordinal and Quantitative) - Ordered Sequential Diverging and Cyclic

bull Tables Keys and Values bull Fields Scalar Vector and Tensor bull Time-Varying Data


22

Fieldattribute

item





[Bostock 2011]

Item

Links

Positions and Grids


Position Grid

Dataset Types


Tables


Items (rows)


Networks

Link

Node (item)

Trees

Fields (Continuous)


Value in cell

Cell


Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types


Fieldattribute

itemcell

Tables


0

0

0

0

0

0

00

5

5

5

5

5

5

55

10

10

10

10

10

10

1010

15

15

15

15

15

15

1515

20

20

20

20

20

20

2020

25

25

25

25

25

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2525

30

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35

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35

35

35

3535

40

40

40

40

40

40

4040

45

45

45

45

45

45

4545

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)


30

30

30

30

30

30

3030

35

35

35

35

35

35

3535

40

40

40

40

40

40

4040

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4545

50

50

50

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50

50

5050

55

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55

55

5555

60

60

60

60

60

60

6060

65

65

65

65

65

65

6565

70

70

70

70

70

70

7070

75

75

75

75

75

75

7575

80

80

80

80

80

80

8080cylinders

cylinders

cylinders

cylinders

cylinders

cylinders

cylinderscylinders

100

100

100

100

100

100

100100

150

150

150

150

150

150

150150

200

200

200

200

200

200

200200

250

250

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250

250

250

250250

300

300

300

300

300

300

300300

350

350

350

350

350

350

350350

400

400

400

400

400

400

400400

450

450

450

450

450

450

450450

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)


0

0

0

0

0

0

00

20

20

20

20

20

20

2020

40

40

40

40

40

40

4040

60

60

60

60

60

60

6060

80

80

80

80

80

80

8080

100

100

100

100

100

100

100100

120

120

120

120

120

120

120120

140

140

140

140

140

140

140140

160

160

160

160

160

160

160160

180

180

180

180

180

180

180180

200

200

200

200

200

200

200200

220

220

220

220

220

220

220220

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)


2000

2000

2000

2000

2000

2000

20002000

2500

2500

2500

2500

2500

2500

25002500

3000

3000

3000

3000

3000

3000

30003000

3500

3500

3500

3500

3500

3500

35003500

4000

4000

4000

4000

4000

4000

40004000

4500

4500

4500

4500

4500

4500

45004500

5000

5000

5000

5000

5000

5000

50005000

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)


8

8

8

8

8

8

88

10

10

10

10

10

10

1010

12

12

12

12

12

12

1212

14

14

14

14

14

14

1414

16

16

16

16

16

16

1616

18

18

18

18

18

18

1818

20

20

20

20

20

20

2020

22

22

22

22

22

22

2222

24

24

24

24

24

24

2424

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)0-60 mph (s)

70

70

70

70

70

70

7070

71

71

71

71

71

71

7171

72

72

72

72

72

72

7272

73

73

73

73

73

73

7373

74

74

74

74

74

74

7474

75

75

75

75

75

75

7575

76

76

76

76

76

76

7676

77

77

77

77

77

77

7777

78

78

78

78

78

78

7878

79

79

79

79

79

79

7979

80

80

80

80

80

80

8080

81

81

81

81

81

81

8181

82

82

82

82

82

82

8282year

year

year

year

year

year

yearyear



[M Bostock 2011]













Networks










Data Structures







Sets amp Lists



Attribute Types


Attribute Types

Ordering Direction

Categorical Ordered









[via A Lex 2015]






[via A Lex 2015]






[via A Lex 2015]





















r f ϕ( )=


r aϕ=

2πar a ϕfrasl=

r aϕk=

r aekϕ=


Cyclic Data







Flat

Multidimensional

Tabl

es

Fiel

ds












mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex





22

Fieldattribute

item





[Bostock 2011]

Item

Links

Positions and Grids


Position Grid

Dataset Types


Tables


Items (rows)


Networks

Link

Node (item)

Trees

Fields (Continuous)


Value in cell

Cell


Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types


Fieldattribute

itemcell

Tables


0

0

0

0

0

0

00

5

5

5

5

5

5

55

10

10

10

10

10

10

1010

15

15

15

15

15

15

1515

20

20

20

20

20

20

2020

25

25

25

25

25

25

2525

30

30

30

30

30

30

3030

35

35

35

35

35

35

3535

40

40

40

40

40

40

4040

45

45

45

45

45

45

4545

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)


30

30

30

30

30

30

3030

35

35

35

35

35

35

3535

40

40

40

40

40

40

4040

45

45

45

45

45

45

4545

50

50

50

50

50

50

5050

55

55

55

55

55

55

5555

60

60

60

60

60

60

6060

65

65

65

65

65

65

6565

70

70

70

70

70

70

7070

75

75

75

75

75

75

7575

80

80

80

80

80

80

8080cylinders

cylinders

cylinders

cylinders

cylinders

cylinders

cylinderscylinders

100

100

100

100

100

100

100100

150

150

150

150

150

150

150150

200

200

200

200

200

200

200200

250

250

250

250

250

250

250250

300

300

300

300

300

300

300300

350

350

350

350

350

350

350350

400

400

400

400

400

400

400400

450

450

450

450

450

450

450450

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)


0

0

0

0

0

0

00

20

20

20

20

20

20

2020

40

40

40

40

40

40

4040

60

60

60

60

60

60

6060

80

80

80

80

80

80

8080

100

100

100

100

100

100

100100

120

120

120

120

120

120

120120

140

140

140

140

140

140

140140

160

160

160

160

160

160

160160

180

180

180

180

180

180

180180

200

200

200

200

200

200

200200

220

220

220

220

220

220

220220

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)


2000

2000

2000

2000

2000

2000

20002000

2500

2500

2500

2500

2500

2500

25002500

3000

3000

3000

3000

3000

3000

30003000

3500

3500

3500

3500

3500

3500

35003500

4000

4000

4000

4000

4000

4000

40004000

4500

4500

4500

4500

4500

4500

45004500

5000

5000

5000

5000

5000

5000

50005000

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)


8

8

8

8

8

8

88

10

10

10

10

10

10

1010

12

12

12

12

12

12

1212

14

14

14

14

14

14

1414

16

16

16

16

16

16

1616

18

18

18

18

18

18

1818

20

20

20

20

20

20

2020

22

22

22

22

22

22

2222

24

24

24

24

24

24

2424

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)0-60 mph (s)

70

70

70

70

70

70

7070

71

71

71

71

71

71

7171

72

72

72

72

72

72

7272

73

73

73

73

73

73

7373

74

74

74

74

74

74

7474

75

75

75

75

75

75

7575

76

76

76

76

76

76

7676

77

77

77

77

77

77

7777

78

78

78

78

78

78

7878

79

79

79

79

79

79

7979

80

80

80

80

80

80

8080

81

81

81

81

81

81

8181

82

82

82

82

82

82

8282year

year

year

year

year

year

yearyear



[M Bostock 2011]













Networks










Data Structures







Sets amp Lists



Attribute Types


Attribute Types

Ordering Direction

Categorical Ordered









[via A Lex 2015]






[via A Lex 2015]






[via A Lex 2015]





















r f ϕ( )=


r aϕ=

2πar a ϕfrasl=

r aϕk=

r aekϕ=


Cyclic Data







Flat

Multidimensional

Tabl

es

Fiel

ds












mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex







[Bostock 2011]

Item

Links

Positions and Grids


Position Grid

Dataset Types


Tables


Items (rows)


Networks

Link

Node (item)

Trees

Fields (Continuous)


Value in cell

Cell


Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types


Fieldattribute

itemcell

Tables


0

0

0

0

0

0

00

5

5

5

5

5

5

55

10

10

10

10

10

10

1010

15

15

15

15

15

15

1515

20

20

20

20

20

20

2020

25

25

25

25

25

25

2525

30

30

30

30

30

30

3030

35

35

35

35

35

35

3535

40

40

40

40

40

40

4040

45

45

45

45

45

45

4545

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)


30

30

30

30

30

30

3030

35

35

35

35

35

35

3535

40

40

40

40

40

40

4040

45

45

45

45

45

45

4545

50

50

50

50

50

50

5050

55

55

55

55

55

55

5555

60

60

60

60

60

60

6060

65

65

65

65

65

65

6565

70

70

70

70

70

70

7070

75

75

75

75

75

75

7575

80

80

80

80

80

80

8080cylinders

cylinders

cylinders

cylinders

cylinders

cylinders

cylinderscylinders

100

100

100

100

100

100

100100

150

150

150

150

150

150

150150

200

200

200

200

200

200

200200

250

250

250

250

250

250

250250

300

300

300

300

300

300

300300

350

350

350

350

350

350

350350

400

400

400

400

400

400

400400

450

450

450

450

450

450

450450

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)


0

0

0

0

0

0

00

20

20

20

20

20

20

2020

40

40

40

40

40

40

4040

60

60

60

60

60

60

6060

80

80

80

80

80

80

8080

100

100

100

100

100

100

100100

120

120

120

120

120

120

120120

140

140

140

140

140

140

140140

160

160

160

160

160

160

160160

180

180

180

180

180

180

180180

200

200

200

200

200

200

200200

220

220

220

220

220

220

220220

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)


2000

2000

2000

2000

2000

2000

20002000

2500

2500

2500

2500

2500

2500

25002500

3000

3000

3000

3000

3000

3000

30003000

3500

3500

3500

3500

3500

3500

35003500

4000

4000

4000

4000

4000

4000

40004000

4500

4500

4500

4500

4500

4500

45004500

5000

5000

5000

5000

5000

5000

50005000

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)


8

8

8

8

8

8

88

10

10

10

10

10

10

1010

12

12

12

12

12

12

1212

14

14

14

14

14

14

1414

16

16

16

16

16

16

1616

18

18

18

18

18

18

1818

20

20

20

20

20

20

2020

22

22

22

22

22

22

2222

24

24

24

24

24

24

2424

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)0-60 mph (s)

70

70

70

70

70

70

7070

71

71

71

71

71

71

7171

72

72

72

72

72

72

7272

73

73

73

73

73

73

7373

74

74

74

74

74

74

7474

75

75

75

75

75

75

7575

76

76

76

76

76

76

7676

77

77

77

77

77

77

7777

78

78

78

78

78

78

7878

79

79

79

79

79

79

7979

80

80

80

80

80

80

8080

81

81

81

81

81

81

8181

82

82

82

82

82

82

8282year

year

year

year

year

year

yearyear



[M Bostock 2011]













Networks










Data Structures







Sets amp Lists



Attribute Types


Attribute Types

Ordering Direction

Categorical Ordered









[via A Lex 2015]






[via A Lex 2015]






[via A Lex 2015]





















r f ϕ( )=


r aϕ=

2πar a ϕfrasl=

r aϕk=

r aekϕ=


Cyclic Data







Flat

Multidimensional

Tabl

es

Fiel

ds












mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex





Positions and Grids


Position Grid

Dataset Types


Tables


Items (rows)


Networks

Link

Node (item)

Trees

Fields (Continuous)


Value in cell

Cell


Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types


Fieldattribute

itemcell

Tables


0

0

0

0

0

0

00

5

5

5

5

5

5

55

10

10

10

10

10

10

1010

15

15

15

15

15

15

1515

20

20

20

20

20

20

2020

25

25

25

25

25

25

2525

30

30

30

30

30

30

3030

35

35

35

35

35

35

3535

40

40

40

40

40

40

4040

45

45

45

45

45

45

4545

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)


30

30

30

30

30

30

3030

35

35

35

35

35

35

3535

40

40

40

40

40

40

4040

45

45

45

45

45

45

4545

50

50

50

50

50

50

5050

55

55

55

55

55

55

5555

60

60

60

60

60

60

6060

65

65

65

65

65

65

6565

70

70

70

70

70

70

7070

75

75

75

75

75

75

7575

80

80

80

80

80

80

8080cylinders

cylinders

cylinders

cylinders

cylinders

cylinders

cylinderscylinders

100

100

100

100

100

100

100100

150

150

150

150

150

150

150150

200

200

200

200

200

200

200200

250

250

250

250

250

250

250250

300

300

300

300

300

300

300300

350

350

350

350

350

350

350350

400

400

400

400

400

400

400400

450

450

450

450

450

450

450450

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)


0

0

0

0

0

0

00

20

20

20

20

20

20

2020

40

40

40

40

40

40

4040

60

60

60

60

60

60

6060

80

80

80

80

80

80

8080

100

100

100

100

100

100

100100

120

120

120

120

120

120

120120

140

140

140

140

140

140

140140

160

160

160

160

160

160

160160

180

180

180

180

180

180

180180

200

200

200

200

200

200

200200

220

220

220

220

220

220

220220

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)


2000

2000

2000

2000

2000

2000

20002000

2500

2500

2500

2500

2500

2500

25002500

3000

3000

3000

3000

3000

3000

30003000

3500

3500

3500

3500

3500

3500

35003500

4000

4000

4000

4000

4000

4000

40004000

4500

4500

4500

4500

4500

4500

45004500

5000

5000

5000

5000

5000

5000

50005000

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)


8

8

8

8

8

8

88

10

10

10

10

10

10

1010

12

12

12

12

12

12

1212

14

14

14

14

14

14

1414

16

16

16

16

16

16

1616

18

18

18

18

18

18

1818

20

20

20

20

20

20

2020

22

22

22

22

22

22

2222

24

24

24

24

24

24

2424

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)0-60 mph (s)

70

70

70

70

70

70

7070

71

71

71

71

71

71

7171

72

72

72

72

72

72

7272

73

73

73

73

73

73

7373

74

74

74

74

74

74

7474

75

75

75

75

75

75

7575

76

76

76

76

76

76

7676

77

77

77

77

77

77

7777

78

78

78

78

78

78

7878

79

79

79

79

79

79

7979

80

80

80

80

80

80

8080

81

81

81

81

81

81

8181

82

82

82

82

82

82

8282year

year

year

year

year

year

yearyear



[M Bostock 2011]













Networks










Data Structures







Sets amp Lists



Attribute Types


Attribute Types

Ordering Direction

Categorical Ordered









[via A Lex 2015]






[via A Lex 2015]






[via A Lex 2015]





















r f ϕ( )=


r aϕ=

2πar a ϕfrasl=

r aϕk=

r aekϕ=


Cyclic Data







Flat

Multidimensional

Tabl

es

Fiel

ds












mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex





Dataset Types


Tables


Items (rows)


Networks

Link

Node (item)

Trees

Fields (Continuous)


Value in cell

Cell


Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types


Fieldattribute

itemcell

Tables


0

0

0

0

0

0

00

5

5

5

5

5

5

55

10

10

10

10

10

10

1010

15

15

15

15

15

15

1515

20

20

20

20

20

20

2020

25

25

25

25

25

25

2525

30

30

30

30

30

30

3030

35

35

35

35

35

35

3535

40

40

40

40

40

40

4040

45

45

45

45

45

45

4545

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)


30

30

30

30

30

30

3030

35

35

35

35

35

35

3535

40

40

40

40

40

40

4040

45

45

45

45

45

45

4545

50

50

50

50

50

50

5050

55

55

55

55

55

55

5555

60

60

60

60

60

60

6060

65

65

65

65

65

65

6565

70

70

70

70

70

70

7070

75

75

75

75

75

75

7575

80

80

80

80

80

80

8080cylinders

cylinders

cylinders

cylinders

cylinders

cylinders

cylinderscylinders

100

100

100

100

100

100

100100

150

150

150

150

150

150

150150

200

200

200

200

200

200

200200

250

250

250

250

250

250

250250

300

300

300

300

300

300

300300

350

350

350

350

350

350

350350

400

400

400

400

400

400

400400

450

450

450

450

450

450

450450

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)


0

0

0

0

0

0

00

20

20

20

20

20

20

2020

40

40

40

40

40

40

4040

60

60

60

60

60

60

6060

80

80

80

80

80

80

8080

100

100

100

100

100

100

100100

120

120

120

120

120

120

120120

140

140

140

140

140

140

140140

160

160

160

160

160

160

160160

180

180

180

180

180

180

180180

200

200

200

200

200

200

200200

220

220

220

220

220

220

220220

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)


2000

2000

2000

2000

2000

2000

20002000

2500

2500

2500

2500

2500

2500

25002500

3000

3000

3000

3000

3000

3000

30003000

3500

3500

3500

3500

3500

3500

35003500

4000

4000

4000

4000

4000

4000

40004000

4500

4500

4500

4500

4500

4500

45004500

5000

5000

5000

5000

5000

5000

50005000

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)


8

8

8

8

8

8

88

10

10

10

10

10

10

1010

12

12

12

12

12

12

1212

14

14

14

14

14

14

1414

16

16

16

16

16

16

1616

18

18

18

18

18

18

1818

20

20

20

20

20

20

2020

22

22

22

22

22

22

2222

24

24

24

24

24

24

2424

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)0-60 mph (s)

70

70

70

70

70

70

7070

71

71

71

71

71

71

7171

72

72

72

72

72

72

7272

73

73

73

73

73

73

7373

74

74

74

74

74

74

7474

75

75

75

75

75

75

7575

76

76

76

76

76

76

7676

77

77

77

77

77

77

7777

78

78

78

78

78

78

7878

79

79

79

79

79

79

7979

80

80

80

80

80

80

8080

81

81

81

81

81

81

8181

82

82

82

82

82

82

8282year

year

year

year

year

year

yearyear



[M Bostock 2011]













Networks










Data Structures







Sets amp Lists



Attribute Types


Attribute Types

Ordering Direction

Categorical Ordered









[via A Lex 2015]






[via A Lex 2015]






[via A Lex 2015]





















r f ϕ( )=


r aϕ=

2πar a ϕfrasl=

r aϕk=

r aekϕ=


Cyclic Data







Flat

Multidimensional

Tabl

es

Fiel

ds












mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex





Fieldattribute

itemcell

Tables


0

0

0

0

0

0

00

5

5

5

5

5

5

55

10

10

10

10

10

10

1010

15

15

15

15

15

15

1515

20

20

20

20

20

20

2020

25

25

25

25

25

25

2525

30

30

30

30

30

30

3030

35

35

35

35

35

35

3535

40

40

40

40

40

40

4040

45

45

45

45

45

45

4545

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)


30

30

30

30

30

30

3030

35

35

35

35

35

35

3535

40

40

40

40

40

40

4040

45

45

45

45

45

45

4545

50

50

50

50

50

50

5050

55

55

55

55

55

55

5555

60

60

60

60

60

60

6060

65

65

65

65

65

65

6565

70

70

70

70

70

70

7070

75

75

75

75

75

75

7575

80

80

80

80

80

80

8080cylinders

cylinders

cylinders

cylinders

cylinders

cylinders

cylinderscylinders

100

100

100

100

100

100

100100

150

150

150

150

150

150

150150

200

200

200

200

200

200

200200

250

250

250

250

250

250

250250

300

300

300

300

300

300

300300

350

350

350

350

350

350

350350

400

400

400

400

400

400

400400

450

450

450

450

450

450

450450

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)


0

0

0

0

0

0

00

20

20

20

20

20

20

2020

40

40

40

40

40

40

4040

60

60

60

60

60

60

6060

80

80

80

80

80

80

8080

100

100

100

100

100

100

100100

120

120

120

120

120

120

120120

140

140

140

140

140

140

140140

160

160

160

160

160

160

160160

180

180

180

180

180

180

180180

200

200

200

200

200

200

200200

220

220

220

220

220

220

220220

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)


2000

2000

2000

2000

2000

2000

20002000

2500

2500

2500

2500

2500

2500

25002500

3000

3000

3000

3000

3000

3000

30003000

3500

3500

3500

3500

3500

3500

35003500

4000

4000

4000

4000

4000

4000

40004000

4500

4500

4500

4500

4500

4500

45004500

5000

5000

5000

5000

5000

5000

50005000

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)


8

8

8

8

8

8

88

10

10

10

10

10

10

1010

12

12

12

12

12

12

1212

14

14

14

14

14

14

1414

16

16

16

16

16

16

1616

18

18

18

18

18

18

1818

20

20

20

20

20

20

2020

22

22

22

22

22

22

2222

24

24

24

24

24

24

2424

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)0-60 mph (s)

70

70

70

70

70

70

7070

71

71

71

71

71

71

7171

72

72

72

72

72

72

7272

73

73

73

73

73

73

7373

74

74

74

74

74

74

7474

75

75

75

75

75

75

7575

76

76

76

76

76

76

7676

77

77

77

77

77

77

7777

78

78

78

78

78

78

7878

79

79

79

79

79

79

7979

80

80

80

80

80

80

8080

81

81

81

81

81

81

8181

82

82

82

82

82

82

8282year

year

year

year

year

year

yearyear



[M Bostock 2011]













Networks










Data Structures







Sets amp Lists



Attribute Types


Attribute Types

Ordering Direction

Categorical Ordered









[via A Lex 2015]






[via A Lex 2015]






[via A Lex 2015]





















r f ϕ( )=


r aϕ=

2πar a ϕfrasl=

r aϕk=

r aekϕ=


Cyclic Data







Flat

Multidimensional

Tabl

es

Fiel

ds












mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex





0

0

0

0

0

0

00

5

5

5

5

5

5

55

10

10

10

10

10

10

1010

15

15

15

15

15

15

1515

20

20

20

20

20

20

2020

25

25

25

25

25

25

2525

30

30

30

30

30

30

3030

35

35

35

35

35

35

3535

40

40

40

40

40

40

4040

45

45

45

45

45

45

4545

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)

economy (mpg)


30

30

30

30

30

30

3030

35

35

35

35

35

35

3535

40

40

40

40

40

40

4040

45

45

45

45

45

45

4545

50

50

50

50

50

50

5050

55

55

55

55

55

55

5555

60

60

60

60

60

60

6060

65

65

65

65

65

65

6565

70

70

70

70

70

70

7070

75

75

75

75

75

75

7575

80

80

80

80

80

80

8080cylinders

cylinders

cylinders

cylinders

cylinders

cylinders

cylinderscylinders

100

100

100

100

100

100

100100

150

150

150

150

150

150

150150

200

200

200

200

200

200

200200

250

250

250

250

250

250

250250

300

300

300

300

300

300

300300

350

350

350

350

350

350

350350

400

400

400

400

400

400

400400

450

450

450

450

450

450

450450

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)

displacement (cc)


0

0

0

0

0

0

00

20

20

20

20

20

20

2020

40

40

40

40

40

40

4040

60

60

60

60

60

60

6060

80

80

80

80

80

80

8080

100

100

100

100

100

100

100100

120

120

120

120

120

120

120120

140

140

140

140

140

140

140140

160

160

160

160

160

160

160160

180

180

180

180

180

180

180180

200

200

200

200

200

200

200200

220

220

220

220

220

220

220220

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)

power (hp)


2000

2000

2000

2000

2000

2000

20002000

2500

2500

2500

2500

2500

2500

25002500

3000

3000

3000

3000

3000

3000

30003000

3500

3500

3500

3500

3500

3500

35003500

4000

4000

4000

4000

4000

4000

40004000

4500

4500

4500

4500

4500

4500

45004500

5000

5000

5000

5000

5000

5000

50005000

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)

weight (lb)


8

8

8

8

8

8

88

10

10

10

10

10

10

1010

12

12

12

12

12

12

1212

14

14

14

14

14

14

1414

16

16

16

16

16

16

1616

18

18

18

18

18

18

1818

20

20

20

20

20

20

2020

22

22

22

22

22

22

2222

24

24

24

24

24

24

2424

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)

0-60 mph (s)0-60 mph (s)

70

70

70

70

70

70

7070

71

71

71

71

71

71

7171

72

72

72

72

72

72

7272

73

73

73

73

73

73

7373

74

74

74

74

74

74

7474

75

75

75

75

75

75

7575

76

76

76

76

76

76

7676

77

77

77

77

77

77

7777

78

78

78

78

78

78

7878

79

79

79

79

79

79

7979

80

80

80

80

80

80

8080

81

81

81

81

81

81

8181

82

82

82

82

82

82

8282year

year

year

year

year

year

yearyear



[M Bostock 2011]













Networks










Data Structures







Sets amp Lists



Attribute Types


Attribute Types

Ordering Direction

Categorical Ordered









[via A Lex 2015]






[via A Lex 2015]






[via A Lex 2015]





















r f ϕ( )=


r aϕ=

2πar a ϕfrasl=

r aϕk=

r aekϕ=


Cyclic Data







Flat

Multidimensional

Tabl

es

Fiel

ds












mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex

















Networks










Data Structures







Sets amp Lists



Attribute Types


Attribute Types

Ordering Direction

Categorical Ordered









[via A Lex 2015]






[via A Lex 2015]






[via A Lex 2015]





















r f ϕ( )=


r aϕ=

2πar a ϕfrasl=

r aϕk=

r aekϕ=


Cyclic Data







Flat

Multidimensional

Tabl

es

Fiel

ds












mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex












Data Structures







Sets amp Lists



Attribute Types


Attribute Types

Ordering Direction

Categorical Ordered









[via A Lex 2015]






[via A Lex 2015]






[via A Lex 2015]





















r f ϕ( )=


r aϕ=

2πar a ϕfrasl=

r aϕk=

r aekϕ=


Cyclic Data







Flat

Multidimensional

Tabl

es

Fiel

ds












mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex








Sets amp Lists



Attribute Types


Attribute Types

Ordering Direction

Categorical Ordered









[via A Lex 2015]






[via A Lex 2015]






[via A Lex 2015]





















r f ϕ( )=


r aϕ=

2πar a ϕfrasl=

r aϕk=

r aekϕ=


Cyclic Data







Flat

Multidimensional

Tabl

es

Fiel

ds












mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex





Attribute Types


Attribute Types

Ordering Direction

Categorical Ordered









[via A Lex 2015]






[via A Lex 2015]






[via A Lex 2015]





















r f ϕ( )=


r aϕ=

2πar a ϕfrasl=

r aϕk=

r aekϕ=


Cyclic Data







Flat

Multidimensional

Tabl

es

Fiel

ds












mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

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94

74


58

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64

84

24

74

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94

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Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex










[via A Lex 2015]






[via A Lex 2015]






[via A Lex 2015]





















r f ϕ( )=


r aϕ=

2πar a ϕfrasl=

r aϕk=

r aekϕ=


Cyclic Data







Flat

Multidimensional

Tabl

es

Fiel

ds












mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex










[via A Lex 2015]






[via A Lex 2015]





















r f ϕ( )=


r aϕ=

2πar a ϕfrasl=

r aϕk=

r aekϕ=


Cyclic Data







Flat

Multidimensional

Tabl

es

Fiel

ds












mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex










[via A Lex 2015]





















r f ϕ( )=


r aϕ=

2πar a ϕfrasl=

r aϕk=

r aekϕ=


Cyclic Data







Flat

Multidimensional

Tabl

es

Fiel

ds












mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex

























r f ϕ( )=


r aϕ=

2πar a ϕfrasl=

r aϕk=

r aekϕ=


Cyclic Data







Flat

Multidimensional

Tabl

es

Fiel

ds












mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex









Flat

Multidimensional

Tabl

es

Fiel

ds












mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex















mdash T Munzner





Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex








Tasks


Trends

Actions

Analyze

Search

Query

Why

All Data

Outliers Features

Attributes


Network Data

Spatial DataShape

Topology

Paths

Extremes




tag



Lookup

Locate

Browse

Explore

Targets

Why

How

What


Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

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6484

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58

54

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6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex





Actions Analyze


Analyze



tag







Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex










Name Voyager







aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex









aggregation)




Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex







Actions Search



Search


Location known

Location unknown

Lookup

Locate

Browse

Explore


Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

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64

84

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74

6484

84

94

74


58

54

64

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24

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6484

84

94

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Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex





Query



Query


Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex





Targets


Trends

ALL DATA

Outliers Features

ATTRIBUTES


Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex











SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How






SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex








SpaceTree


TreeJuxtaposer



Actions

Targets

SpaceTree

TreeJuxtaposer


Arrange

Why What How







Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex









Task 1

58

54

64

84

24

74

6484

84

94

74


58

54

64

84

24

74

6484

84

94

74


Task 2

Derive

WhyWhat


HowWhyWhat


Filter

InTree


InTree +




mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex






mdash T Munzner


Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex





Design Iteration



Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex





Design Iteration



Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex





Design Iteration












Algorithm

Domain situation






















Implement system



Slides from A Lex














Algorithm

Domain situation






















Implement system



Slides from A Lex


























Implement system



Slides from A Lex






Slides from A Lex





Data Visualization (DSC 530/CIS 602-01)dkoop/dsc530-2016sp/lectures/...D. Koop, DSC 530, Spring 2016...

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Transcript of Data Visualization (DSC 530/CIS 602-01)dkoop/dsc530-2016sp/lectures/...D. Koop, DSC 530, Spring 2016...