CIS 467 Spring 2015

CIS 467602-01 Data Visualization

Data amp Tasks

Dr David Koop

Assignment 1bull Posted on the course web site bull Due Friday Feb 13 bull Get started soon bull Submission information will be

posted bull Useful reference Interactive Data

Visualization for the Web by Scott Murray

2CIS 467 Spring 2015

Assignment 1bull Posted on the course web site bull Due Friday Feb 13 bull Get started soon bull Submission information will be

posted bull Useful reference Interactive Data

Visualization for the Web by Scott Murray

2CIS 467 Spring 2015

Recap

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

mdash T Munzner

3CIS 467 Spring 2015

Databull What is this data

bull Semantics real-world meaning of the data bull Type structural or mathematical interpretation bull Both often require metadata

- Sometimes we can infer some of this information - Line between data and metadata isnrsquot always clear

4CIS 467 Spring 2015

Data

5CIS 467 Spring 2015

Data Typesbull Items

- An item is an individual discrete entity - eg row in a table node in a network

bull Attributes - An attribute is some specific property that can be measured

observed or logged - aka variable (data) dimension

6CIS 467 Spring 2015

22

Fieldattribute

item

Items amp Attributes

7CIS 467 Spring 2015

Data Typesbull Nodes

- Synonym for item but in the context of networks (graphs) bull Links

- A link is a relation between two items - eg social network friends computer network links

8CIS 467 Spring 2015

Items amp Links

9CIS 467 Spring 2015

[Bostock 2011]

Item

Links

Data Typesbull Positions

- A position is a location in space (usually 2D or 3D) - May be subject to projections - eg cities on a map a sampled region in an CT scan

bull Grids - A grid specifies how data is sampled both geometrically and

topologically - eg how CT scan data is stored

10CIS 467 Spring 2015

Positions and Grids

11CIS 467 Spring 2015

Position Grid

Dataset Types

12CIS 467 Spring 2015

Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)

Attributes (columns)

Value in cell

Cell

Multidimensional Table

Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types

[Munzner (ill Maguire) 2014]

Fieldattribute

itemcell

Tables

13CIS 467 Spring 2015

Networksbull Why networks instead of graphs bull Tables can represent networks

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

graph databases or files

14CIS 467 Spring 2015

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

15CIS 467 Spring 2015

[Holten amp van Wijk 2009]

Networks

16CIS 467 Spring 2015

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

[Holten amp van Wijk 2009]

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields

Each point in space has an associated

Vector Fields

s0

2

400 01 02

10 11 12

20 21 22

3

5

2

4v0

v1

v2

3

5

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields(Order-1 Tensor Fields)(Order-0 Tensor Fields) (Order-2+)

Each point in space has an associated

Scalar

Vector Fields

Vector Tensor

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

Databull What is this data

bull Semantics real-world meaning of the data bull Type structural or mathematical interpretation bull Both often require metadata

- Sometimes we can infer some of this information - Line between data and metadata isnrsquot always clear

4CIS 467 Spring 2015

Data

5CIS 467 Spring 2015

Data Typesbull Items

- An item is an individual discrete entity - eg row in a table node in a network

bull Attributes - An attribute is some specific property that can be measured

observed or logged - aka variable (data) dimension

6CIS 467 Spring 2015

22

Fieldattribute

item

Items amp Attributes

7CIS 467 Spring 2015

Data Typesbull Nodes

- Synonym for item but in the context of networks (graphs) bull Links

- A link is a relation between two items - eg social network friends computer network links

8CIS 467 Spring 2015

Items amp Links

9CIS 467 Spring 2015

[Bostock 2011]

Item

Links

Data Typesbull Positions

- A position is a location in space (usually 2D or 3D) - May be subject to projections - eg cities on a map a sampled region in an CT scan

bull Grids - A grid specifies how data is sampled both geometrically and

topologically - eg how CT scan data is stored

10CIS 467 Spring 2015

Positions and Grids

11CIS 467 Spring 2015

Position Grid

Dataset Types

12CIS 467 Spring 2015

Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)

Attributes (columns)

Value in cell

Cell

Multidimensional Table

Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types

[Munzner (ill Maguire) 2014]

Fieldattribute

itemcell

Tables

13CIS 467 Spring 2015

Networksbull Why networks instead of graphs bull Tables can represent networks

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

graph databases or files

14CIS 467 Spring 2015

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

15CIS 467 Spring 2015

[Holten amp van Wijk 2009]

Networks

16CIS 467 Spring 2015

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

[Holten amp van Wijk 2009]

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields

Each point in space has an associated

Vector Fields

s0

2

400 01 02

10 11 12

20 21 22

3

5

2

4v0

v1

v2

3

5

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields(Order-1 Tensor Fields)(Order-0 Tensor Fields) (Order-2+)

Each point in space has an associated

Scalar

Vector Fields

Vector Tensor

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

Data

5CIS 467 Spring 2015

Data Typesbull Items

- An item is an individual discrete entity - eg row in a table node in a network

bull Attributes - An attribute is some specific property that can be measured

observed or logged - aka variable (data) dimension

6CIS 467 Spring 2015

22

Fieldattribute

item

Items amp Attributes

7CIS 467 Spring 2015

Data Typesbull Nodes

- Synonym for item but in the context of networks (graphs) bull Links

- A link is a relation between two items - eg social network friends computer network links

8CIS 467 Spring 2015

Items amp Links

9CIS 467 Spring 2015

[Bostock 2011]

Item

Links

Data Typesbull Positions

- A position is a location in space (usually 2D or 3D) - May be subject to projections - eg cities on a map a sampled region in an CT scan

bull Grids - A grid specifies how data is sampled both geometrically and

topologically - eg how CT scan data is stored

10CIS 467 Spring 2015

Positions and Grids

11CIS 467 Spring 2015

Position Grid

Dataset Types

12CIS 467 Spring 2015

Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)

Attributes (columns)

Value in cell

Cell

Multidimensional Table

Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types

[Munzner (ill Maguire) 2014]

Fieldattribute

itemcell

Tables

13CIS 467 Spring 2015

Networksbull Why networks instead of graphs bull Tables can represent networks

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

graph databases or files

14CIS 467 Spring 2015

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

15CIS 467 Spring 2015

[Holten amp van Wijk 2009]

Networks

16CIS 467 Spring 2015

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

[Holten amp van Wijk 2009]

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields

Each point in space has an associated

Vector Fields

s0

2

400 01 02

10 11 12

20 21 22

3

5

2

4v0

v1

v2

3

5

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields(Order-1 Tensor Fields)(Order-0 Tensor Fields) (Order-2+)

Each point in space has an associated

Scalar

Vector Fields

Vector Tensor

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

Data Typesbull Items

- An item is an individual discrete entity - eg row in a table node in a network

bull Attributes - An attribute is some specific property that can be measured

observed or logged - aka variable (data) dimension

6CIS 467 Spring 2015

22

Fieldattribute

item

Items amp Attributes

7CIS 467 Spring 2015

Data Typesbull Nodes

- Synonym for item but in the context of networks (graphs) bull Links

- A link is a relation between two items - eg social network friends computer network links

8CIS 467 Spring 2015

Items amp Links

9CIS 467 Spring 2015

[Bostock 2011]

Item

Links

Data Typesbull Positions

- A position is a location in space (usually 2D or 3D) - May be subject to projections - eg cities on a map a sampled region in an CT scan

bull Grids - A grid specifies how data is sampled both geometrically and

topologically - eg how CT scan data is stored

10CIS 467 Spring 2015

Positions and Grids

11CIS 467 Spring 2015

Position Grid

Dataset Types

12CIS 467 Spring 2015

Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)

Attributes (columns)

Value in cell

Cell

Multidimensional Table

Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types

[Munzner (ill Maguire) 2014]

Fieldattribute

itemcell

Tables

13CIS 467 Spring 2015

Networksbull Why networks instead of graphs bull Tables can represent networks

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

graph databases or files

14CIS 467 Spring 2015

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

15CIS 467 Spring 2015

[Holten amp van Wijk 2009]

Networks

16CIS 467 Spring 2015

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

[Holten amp van Wijk 2009]

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields

Each point in space has an associated

Vector Fields

s0

2

400 01 02

10 11 12

20 21 22

3

5

2

4v0

v1

v2

3

5

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields(Order-1 Tensor Fields)(Order-0 Tensor Fields) (Order-2+)

Each point in space has an associated

Scalar

Vector Fields

Vector Tensor

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

22

Fieldattribute

item

Items amp Attributes

7CIS 467 Spring 2015

Data Typesbull Nodes

- Synonym for item but in the context of networks (graphs) bull Links

- A link is a relation between two items - eg social network friends computer network links

8CIS 467 Spring 2015

Items amp Links

9CIS 467 Spring 2015

[Bostock 2011]

Item

Links

Data Typesbull Positions

- A position is a location in space (usually 2D or 3D) - May be subject to projections - eg cities on a map a sampled region in an CT scan

bull Grids - A grid specifies how data is sampled both geometrically and

topologically - eg how CT scan data is stored

10CIS 467 Spring 2015

Positions and Grids

11CIS 467 Spring 2015

Position Grid

Dataset Types

12CIS 467 Spring 2015

Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)

Attributes (columns)

Value in cell

Cell

Multidimensional Table

Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types

[Munzner (ill Maguire) 2014]

Fieldattribute

itemcell

Tables

13CIS 467 Spring 2015

Networksbull Why networks instead of graphs bull Tables can represent networks

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

graph databases or files

14CIS 467 Spring 2015

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

15CIS 467 Spring 2015

[Holten amp van Wijk 2009]

Networks

16CIS 467 Spring 2015

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

[Holten amp van Wijk 2009]

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields

Each point in space has an associated

Vector Fields

s0

2

400 01 02

10 11 12

20 21 22

3

5

2

4v0

v1

v2

3

5

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields(Order-1 Tensor Fields)(Order-0 Tensor Fields) (Order-2+)

Each point in space has an associated

Scalar

Vector Fields

Vector Tensor

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

Data Typesbull Nodes

- Synonym for item but in the context of networks (graphs) bull Links

- A link is a relation between two items - eg social network friends computer network links

8CIS 467 Spring 2015

Items amp Links

9CIS 467 Spring 2015

[Bostock 2011]

Item

Links

Data Typesbull Positions

- A position is a location in space (usually 2D or 3D) - May be subject to projections - eg cities on a map a sampled region in an CT scan

bull Grids - A grid specifies how data is sampled both geometrically and

topologically - eg how CT scan data is stored

10CIS 467 Spring 2015

Positions and Grids

11CIS 467 Spring 2015

Position Grid

Dataset Types

12CIS 467 Spring 2015

Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)

Attributes (columns)

Value in cell

Cell

Multidimensional Table

Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types

[Munzner (ill Maguire) 2014]

Fieldattribute

itemcell

Tables

13CIS 467 Spring 2015

Networksbull Why networks instead of graphs bull Tables can represent networks

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

graph databases or files

14CIS 467 Spring 2015

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

15CIS 467 Spring 2015

[Holten amp van Wijk 2009]

Networks

16CIS 467 Spring 2015

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

[Holten amp van Wijk 2009]

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields

Each point in space has an associated

Vector Fields

s0

2

400 01 02

10 11 12

20 21 22

3

5

2

4v0

v1

v2

3

5

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields(Order-1 Tensor Fields)(Order-0 Tensor Fields) (Order-2+)

Each point in space has an associated

Scalar

Vector Fields

Vector Tensor

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

Items amp Links

9CIS 467 Spring 2015

[Bostock 2011]

Item

Links

Data Typesbull Positions

- A position is a location in space (usually 2D or 3D) - May be subject to projections - eg cities on a map a sampled region in an CT scan

bull Grids - A grid specifies how data is sampled both geometrically and

topologically - eg how CT scan data is stored

10CIS 467 Spring 2015

Positions and Grids

11CIS 467 Spring 2015

Position Grid

Dataset Types

12CIS 467 Spring 2015

Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)

Attributes (columns)

Value in cell

Cell

Multidimensional Table

Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types

[Munzner (ill Maguire) 2014]

Fieldattribute

itemcell

Tables

13CIS 467 Spring 2015

Networksbull Why networks instead of graphs bull Tables can represent networks

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

graph databases or files

14CIS 467 Spring 2015

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

15CIS 467 Spring 2015

[Holten amp van Wijk 2009]

Networks

16CIS 467 Spring 2015

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

[Holten amp van Wijk 2009]

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields

Each point in space has an associated

Vector Fields

s0

2

400 01 02

10 11 12

20 21 22

3

5

2

4v0

v1

v2

3

5

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields(Order-1 Tensor Fields)(Order-0 Tensor Fields) (Order-2+)

Each point in space has an associated

Scalar

Vector Fields

Vector Tensor

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

Data Typesbull Positions

- A position is a location in space (usually 2D or 3D) - May be subject to projections - eg cities on a map a sampled region in an CT scan

bull Grids - A grid specifies how data is sampled both geometrically and

topologically - eg how CT scan data is stored

10CIS 467 Spring 2015

Positions and Grids

11CIS 467 Spring 2015

Position Grid

Dataset Types

12CIS 467 Spring 2015

Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)

Attributes (columns)

Value in cell

Cell

Multidimensional Table

Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types

[Munzner (ill Maguire) 2014]

Fieldattribute

itemcell

Tables

13CIS 467 Spring 2015

Networksbull Why networks instead of graphs bull Tables can represent networks

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

graph databases or files

14CIS 467 Spring 2015

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

15CIS 467 Spring 2015

[Holten amp van Wijk 2009]

Networks

16CIS 467 Spring 2015

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

[Holten amp van Wijk 2009]

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields

Each point in space has an associated

Vector Fields

s0

2

400 01 02

10 11 12

20 21 22

3

5

2

4v0

v1

v2

3

5

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields(Order-1 Tensor Fields)(Order-0 Tensor Fields) (Order-2+)

Each point in space has an associated

Scalar

Vector Fields

Vector Tensor

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

Positions and Grids

11CIS 467 Spring 2015

Position Grid

Dataset Types

12CIS 467 Spring 2015

Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)

Attributes (columns)

Value in cell

Cell

Multidimensional Table

Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types

[Munzner (ill Maguire) 2014]

Fieldattribute

itemcell

Tables

13CIS 467 Spring 2015

Networksbull Why networks instead of graphs bull Tables can represent networks

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

graph databases or files

14CIS 467 Spring 2015

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

15CIS 467 Spring 2015

[Holten amp van Wijk 2009]

Networks

16CIS 467 Spring 2015

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

[Holten amp van Wijk 2009]

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields

Each point in space has an associated

Vector Fields

s0

2

400 01 02

10 11 12

20 21 22

3

5

2

4v0

v1

v2

3

5

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields(Order-1 Tensor Fields)(Order-0 Tensor Fields) (Order-2+)

Each point in space has an associated

Scalar

Vector Fields

Vector Tensor

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

Dataset Types

12CIS 467 Spring 2015

Tables

Attributes (columns)

Items (rows)

Cell containing value

Networks

Link

Node (item)

Trees

Fields (Continuous)

Attributes (columns)

Value in cell

Cell

Multidimensional Table

Value in cell

Grid of positions

Geometry (Spatial)

Position

Dataset Types

[Munzner (ill Maguire) 2014]

Fieldattribute

itemcell

Tables

13CIS 467 Spring 2015

Networksbull Why networks instead of graphs bull Tables can represent networks

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

graph databases or files

14CIS 467 Spring 2015

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

15CIS 467 Spring 2015

[Holten amp van Wijk 2009]

Networks

16CIS 467 Spring 2015

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

[Holten amp van Wijk 2009]

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields

Each point in space has an associated

Vector Fields

s0

2

400 01 02

10 11 12

20 21 22

3

5

2

4v0

v1

v2

3

5

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields(Order-1 Tensor Fields)(Order-0 Tensor Fields) (Order-2+)

Each point in space has an associated

Scalar

Vector Fields

Vector Tensor

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

Fieldattribute

itemcell

Tables

13CIS 467 Spring 2015

Networksbull Why networks instead of graphs bull Tables can represent networks

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

graph databases or files

14CIS 467 Spring 2015

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

15CIS 467 Spring 2015

[Holten amp van Wijk 2009]

Networks

16CIS 467 Spring 2015

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

[Holten amp van Wijk 2009]

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields

Each point in space has an associated

Vector Fields

s0

2

400 01 02

10 11 12

20 21 22

3

5

2

4v0

v1

v2

3

5

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields(Order-1 Tensor Fields)(Order-0 Tensor Fields) (Order-2+)

Each point in space has an associated

Scalar

Vector Fields

Vector Tensor

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

Networksbull Why networks instead of graphs bull Tables can represent networks

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

graph databases or files

14CIS 467 Spring 2015

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

15CIS 467 Spring 2015

[Holten amp van Wijk 2009]

Networks

16CIS 467 Spring 2015

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

[Holten amp van Wijk 2009]

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields

Each point in space has an associated

Vector Fields

s0

2

400 01 02

10 11 12

20 21 22

3

5

2

4v0

v1

v2

3

5

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields(Order-1 Tensor Fields)(Order-0 Tensor Fields) (Order-2+)

Each point in space has an associated

Scalar

Vector Fields

Vector Tensor

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

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

15CIS 467 Spring 2015

[Holten amp van Wijk 2009]

Networks

16CIS 467 Spring 2015

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

[Holten amp van Wijk 2009]

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields

Each point in space has an associated

Vector Fields

s0

2

400 01 02

10 11 12

20 21 22

3

5

2

4v0

v1

v2

3

5

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields(Order-1 Tensor Fields)(Order-0 Tensor Fields) (Order-2+)

Each point in space has an associated

Scalar

Vector Fields

Vector Tensor

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

Networks

16CIS 467 Spring 2015

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

[Holten amp van Wijk 2009]

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields

Each point in space has an associated

Vector Fields

s0

2

400 01 02

10 11 12

20 21 22

3

5

2

4v0

v1

v2

3

5

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields(Order-1 Tensor Fields)(Order-0 Tensor Fields) (Order-2+)

Each point in space has an associated

Scalar

Vector Fields

Vector Tensor

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields

Each point in space has an associated

Vector Fields

s0

2

400 01 02

10 11 12

20 21 22

3

5

2

4v0

v1

v2

3

5

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields(Order-1 Tensor Fields)(Order-0 Tensor Fields) (Order-2+)

Each point in space has an associated

Scalar

Vector Fields

Vector Tensor

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

s0

2

400 01 02

10 11 12

20 21 22

3

5

2

4v0

v1

v2

3

5

Fields

17CIS 467 Spring 2015

Scalar Fields Vector Fields Tensor Fields(Order-1 Tensor Fields)(Order-0 Tensor Fields) (Order-2+)

Each point in space has an associated

Scalar

Vector Fields

Vector Tensor

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

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

18CIS 467 Spring 2015

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

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

Spatial Data Example MRI

19CIS 467 Spring 2015

[slide via Levine 2014]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

Scivis and Infovisbull Two subfields of visualization bull Scivis deals with data where the spatial position is given with data

- Usually continuous data - Often displaying physical phenonema - Techniques like isosurfacing volume rendering vector field vis

bull In Infovis the data has no set spatial representation designer chooses how to visually represent data

20CIS 467 Spring 2015

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

Sets amp Lists

21CIS 467 Spring 2015

[Daniels httpexperimentsundercurrentcom]

Attribute Types

22CIS 467 Spring 2015

Attribute Types

Ordering Direction

Categorical Ordered

Ordinal Quantitative

Sequential Diverging Cyclic

[Munzner (ill Maguire) 2014]

231 = Quantitative2 = Nominal3 = Ordinal

quantitative ordinal categorical

Categorial Ordinal and Quantitative

23CIS 467 Spring 2015

241 = Quantitative2 = Nominal3 = Ordinal

quantitative ordinal categorical

Categorial Ordinal and Quantitative

24CIS 467 Spring 2015

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

25CIS 467 Spring 2015

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

Flat

Multidimensional

Tabl

es

Fiel

ds

[Munzner (ill Maguire) 2014]

Tasks

26CIS 467 Spring 2015

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

[Munzner (ill Maguire) 2014]

Actions Analyzebull Consume

ndashExploration ndashExplanation ndashEnjoyment

bull Produce ndashAnnotation ndashRecord ndashDerivation

bullLeads to new directionsideas

27CIS 467 Spring 2015

Analyze

ConsumePresent EnjoyDiscover

ProduceAnnotate Record Derive

tag

[Munzner (ill Maguire) 2014]

Actions Search and Querybull Search based on what

a user knows - Target - Location

bull Query depends onwhat data matters - One - Some (Often Two) - All

28CIS 467 Spring 2015

Search

Query

Identify Compare Summarize

Target known Target unknown

Location known

Location unknown

Lookup

Locate

Browse

Explore

[Munzner (ill Maguire) 2014]

Targets

29CIS 467 Spring 2015

Trends

ALL DATA

Outliers Features

ATTRIBUTES

One ManyDistribution Dependency Correlation Similarity

Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths

[Munzner (ill Maguire) 2014]

How do we do visualization

30CIS 467 Spring 2015

Encode

ArrangeExpress Separate

Order Align

Use

Manipulate Facet Reduce

Change

Select

Navigate

Juxtapose

Partition

Superimpose

Filter

Aggregate

Embed

How

Encode Manipulate Facet

Map

Color

Motion

Size Angle Curvature

Hue Saturation Luminance

Shape

Direction Rate Frequency

from categorical and ordered attributes

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example Derivationbull Strahler number

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

32CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Next Classbull Implementation How do we actually create visualizations bull Tools

- HTML - CSS - SVG - JavaScript

33CIS 467 Spring 2015

231 = Quantitative2 = Nominal3 = Ordinal

quantitative ordinal categorical

Categorial Ordinal and Quantitative

23CIS 467 Spring 2015

241 = Quantitative2 = Nominal3 = Ordinal

quantitative ordinal categorical

Categorial Ordinal and Quantitative

24CIS 467 Spring 2015

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

25CIS 467 Spring 2015

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

Flat

Multidimensional

Tabl

es

Fiel

ds

[Munzner (ill Maguire) 2014]

Tasks

26CIS 467 Spring 2015

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

[Munzner (ill Maguire) 2014]

Actions Analyzebull Consume

ndashExploration ndashExplanation ndashEnjoyment

bull Produce ndashAnnotation ndashRecord ndashDerivation

bullLeads to new directionsideas

27CIS 467 Spring 2015

Analyze

ConsumePresent EnjoyDiscover

ProduceAnnotate Record Derive

tag

[Munzner (ill Maguire) 2014]

Actions Search and Querybull Search based on what

a user knows - Target - Location

bull Query depends onwhat data matters - One - Some (Often Two) - All

28CIS 467 Spring 2015

Search

Query

Identify Compare Summarize

Target known Target unknown

Location known

Location unknown

Lookup

Locate

Browse

Explore

[Munzner (ill Maguire) 2014]

Targets

29CIS 467 Spring 2015

Trends

ALL DATA

Outliers Features

ATTRIBUTES

One ManyDistribution Dependency Correlation Similarity

Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths

[Munzner (ill Maguire) 2014]

How do we do visualization

30CIS 467 Spring 2015

Encode

ArrangeExpress Separate

Order Align

Use

Manipulate Facet Reduce

Change

Select

Navigate

Juxtapose

Partition

Superimpose

Filter

Aggregate

Embed

How

Encode Manipulate Facet

Map

Color

Motion

Size Angle Curvature

Hue Saturation Luminance

Shape

Direction Rate Frequency

from categorical and ordered attributes

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example Derivationbull Strahler number

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

32CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Next Classbull Implementation How do we actually create visualizations bull Tools

- HTML - CSS - SVG - JavaScript

33CIS 467 Spring 2015

241 = Quantitative2 = Nominal3 = Ordinal

quantitative ordinal categorical

Categorial Ordinal and Quantitative

24CIS 467 Spring 2015

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

25CIS 467 Spring 2015

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

Flat

Multidimensional

Tabl

es

Fiel

ds

[Munzner (ill Maguire) 2014]

Tasks

26CIS 467 Spring 2015

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

[Munzner (ill Maguire) 2014]

Actions Analyzebull Consume

ndashExploration ndashExplanation ndashEnjoyment

bull Produce ndashAnnotation ndashRecord ndashDerivation

bullLeads to new directionsideas

27CIS 467 Spring 2015

Analyze

ConsumePresent EnjoyDiscover

ProduceAnnotate Record Derive

tag

[Munzner (ill Maguire) 2014]

Actions Search and Querybull Search based on what

a user knows - Target - Location

bull Query depends onwhat data matters - One - Some (Often Two) - All

28CIS 467 Spring 2015

Search

Query

Identify Compare Summarize

Target known Target unknown

Location known

Location unknown

Lookup

Locate

Browse

Explore

[Munzner (ill Maguire) 2014]

Targets

29CIS 467 Spring 2015

Trends

ALL DATA

Outliers Features

ATTRIBUTES

One ManyDistribution Dependency Correlation Similarity

Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths

[Munzner (ill Maguire) 2014]

How do we do visualization

30CIS 467 Spring 2015

Encode

ArrangeExpress Separate

Order Align

Use

Manipulate Facet Reduce

Change

Select

Navigate

Juxtapose

Partition

Superimpose

Filter

Aggregate

Embed

How

Encode Manipulate Facet

Map

Color

Motion

Size Angle Curvature

Hue Saturation Luminance

Shape

Direction Rate Frequency

from categorical and ordered attributes

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example Derivationbull Strahler number

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

32CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Next Classbull Implementation How do we actually create visualizations bull Tools

- HTML - CSS - SVG - JavaScript

33CIS 467 Spring 2015

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

25CIS 467 Spring 2015

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

Flat

Multidimensional

Tabl

es

Fiel

ds

[Munzner (ill Maguire) 2014]

Tasks

26CIS 467 Spring 2015

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

[Munzner (ill Maguire) 2014]

Actions Analyzebull Consume

ndashExploration ndashExplanation ndashEnjoyment

bull Produce ndashAnnotation ndashRecord ndashDerivation

bullLeads to new directionsideas

27CIS 467 Spring 2015

Analyze

ConsumePresent EnjoyDiscover

ProduceAnnotate Record Derive

tag

[Munzner (ill Maguire) 2014]

Actions Search and Querybull Search based on what

a user knows - Target - Location

bull Query depends onwhat data matters - One - Some (Often Two) - All

28CIS 467 Spring 2015

Search

Query

Identify Compare Summarize

Target known Target unknown

Location known

Location unknown

Lookup

Locate

Browse

Explore

[Munzner (ill Maguire) 2014]

Targets

29CIS 467 Spring 2015

Trends

ALL DATA

Outliers Features

ATTRIBUTES

One ManyDistribution Dependency Correlation Similarity

Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths

[Munzner (ill Maguire) 2014]

How do we do visualization

30CIS 467 Spring 2015

Encode

ArrangeExpress Separate

Order Align

Use

Manipulate Facet Reduce

Change

Select

Navigate

Juxtapose

Partition

Superimpose

Filter

Aggregate

Embed

How

Encode Manipulate Facet

Map

Color

Motion

Size Angle Curvature

Hue Saturation Luminance

Shape

Direction Rate Frequency

from categorical and ordered attributes

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example Derivationbull Strahler number

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

32CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Next Classbull Implementation How do we actually create visualizations bull Tools

- HTML - CSS - SVG - JavaScript

33CIS 467 Spring 2015

Tasks

26CIS 467 Spring 2015

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

[Munzner (ill Maguire) 2014]

Actions Analyzebull Consume

ndashExploration ndashExplanation ndashEnjoyment

bull Produce ndashAnnotation ndashRecord ndashDerivation

bullLeads to new directionsideas

27CIS 467 Spring 2015

Analyze

ConsumePresent EnjoyDiscover

ProduceAnnotate Record Derive

tag

[Munzner (ill Maguire) 2014]

Actions Search and Querybull Search based on what

a user knows - Target - Location

bull Query depends onwhat data matters - One - Some (Often Two) - All

28CIS 467 Spring 2015

Search

Query

Identify Compare Summarize

Target known Target unknown

Location known

Location unknown

Lookup

Locate

Browse

Explore

[Munzner (ill Maguire) 2014]

Targets

29CIS 467 Spring 2015

Trends

ALL DATA

Outliers Features

ATTRIBUTES

One ManyDistribution Dependency Correlation Similarity

Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths

[Munzner (ill Maguire) 2014]

How do we do visualization

30CIS 467 Spring 2015

Encode

ArrangeExpress Separate

Order Align

Use

Manipulate Facet Reduce

Change

Select

Navigate

Juxtapose

Partition

Superimpose

Filter

Aggregate

Embed

How

Encode Manipulate Facet

Map

Color

Motion

Size Angle Curvature

Hue Saturation Luminance

Shape

Direction Rate Frequency

from categorical and ordered attributes

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example Derivationbull Strahler number

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

32CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Next Classbull Implementation How do we actually create visualizations bull Tools

- HTML - CSS - SVG - JavaScript

33CIS 467 Spring 2015

Actions Analyzebull Consume

ndashExploration ndashExplanation ndashEnjoyment

bull Produce ndashAnnotation ndashRecord ndashDerivation

bullLeads to new directionsideas

27CIS 467 Spring 2015

Analyze

ConsumePresent EnjoyDiscover

ProduceAnnotate Record Derive

tag

[Munzner (ill Maguire) 2014]

Actions Search and Querybull Search based on what

a user knows - Target - Location

bull Query depends onwhat data matters - One - Some (Often Two) - All

28CIS 467 Spring 2015

Search

Query

Identify Compare Summarize

Target known Target unknown

Location known

Location unknown

Lookup

Locate

Browse

Explore

[Munzner (ill Maguire) 2014]

Targets

29CIS 467 Spring 2015

Trends

ALL DATA

Outliers Features

ATTRIBUTES

One ManyDistribution Dependency Correlation Similarity

Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths

[Munzner (ill Maguire) 2014]

How do we do visualization

30CIS 467 Spring 2015

Encode

ArrangeExpress Separate

Order Align

Use

Manipulate Facet Reduce

Change

Select

Navigate

Juxtapose

Partition

Superimpose

Filter

Aggregate

Embed

How

Encode Manipulate Facet

Map

Color

Motion

Size Angle Curvature

Hue Saturation Luminance

Shape

Direction Rate Frequency

from categorical and ordered attributes

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example Derivationbull Strahler number

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

32CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Next Classbull Implementation How do we actually create visualizations bull Tools

- HTML - CSS - SVG - JavaScript

33CIS 467 Spring 2015

Actions Search and Querybull Search based on what

a user knows - Target - Location

bull Query depends onwhat data matters - One - Some (Often Two) - All

28CIS 467 Spring 2015

Search

Query

Identify Compare Summarize

Target known Target unknown

Location known

Location unknown

Lookup

Locate

Browse

Explore

[Munzner (ill Maguire) 2014]

Targets

29CIS 467 Spring 2015

Trends

ALL DATA

Outliers Features

ATTRIBUTES

One ManyDistribution Dependency Correlation Similarity

Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths

[Munzner (ill Maguire) 2014]

How do we do visualization

30CIS 467 Spring 2015

Encode

ArrangeExpress Separate

Order Align

Use

Manipulate Facet Reduce

Change

Select

Navigate

Juxtapose

Partition

Superimpose

Filter

Aggregate

Embed

How

Encode Manipulate Facet

Map

Color

Motion

Size Angle Curvature

Hue Saturation Luminance

Shape

Direction Rate Frequency

from categorical and ordered attributes

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example Derivationbull Strahler number

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

32CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Next Classbull Implementation How do we actually create visualizations bull Tools

- HTML - CSS - SVG - JavaScript

33CIS 467 Spring 2015

Targets

29CIS 467 Spring 2015

Trends

ALL DATA

Outliers Features

ATTRIBUTES

One ManyDistribution Dependency Correlation Similarity

Extremes

NETWORK DATA

SPATIAL DATA

Shape

Topology

Paths

[Munzner (ill Maguire) 2014]

How do we do visualization

30CIS 467 Spring 2015

Encode

ArrangeExpress Separate

Order Align

Use

Manipulate Facet Reduce

Change

Select

Navigate

Juxtapose

Partition

Superimpose

Filter

Aggregate

Embed

How

Encode Manipulate Facet

Map

Color

Motion

Size Angle Curvature

Hue Saturation Luminance

Shape

Direction Rate Frequency

from categorical and ordered attributes

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example Derivationbull Strahler number

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

32CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Next Classbull Implementation How do we actually create visualizations bull Tools

- HTML - CSS - SVG - JavaScript

33CIS 467 Spring 2015

How do we do visualization

30CIS 467 Spring 2015

Encode

ArrangeExpress Separate

Order Align

Use

Manipulate Facet Reduce

Change

Select

Navigate

Juxtapose

Partition

Superimpose

Filter

Aggregate

Embed

How

Encode Manipulate Facet

Map

Color

Motion

Size Angle Curvature

Hue Saturation Luminance

Shape

Direction Rate Frequency

from categorical and ordered attributes

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example Derivationbull Strahler number

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

32CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Next Classbull Implementation How do we actually create visualizations bull Tools

- HTML - CSS - SVG - JavaScript

33CIS 467 Spring 2015

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example Derivationbull Strahler number

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

32CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Next Classbull Implementation How do we actually create visualizations bull Tools

- HTML - CSS - SVG - JavaScript

33CIS 467 Spring 2015

Analysis Example

31CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Analysis Example Derivationbull Strahler number

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

32CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Next Classbull Implementation How do we actually create visualizations bull Tools

- HTML - CSS - SVG - JavaScript

33CIS 467 Spring 2015

Analysis Example Derivationbull Strahler number

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

32CIS 467 Spring 2015

[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

[Munzner (ill Maguire) 2014]

Next Classbull Implementation How do we actually create visualizations bull Tools

- HTML - CSS - SVG - JavaScript

33CIS 467 Spring 2015

Next Classbull Implementation How do we actually create visualizations bull Tools

- HTML - CSS - SVG - JavaScript

33CIS 467 Spring 2015