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View Manipulation and Reduction - Lecture 9 - Information Visualisation (4019538FNR)

View Manipulation and Reduction - Lecture 9 - Information Visualisation (4019538FNR)

This lecture forms part of the course Information Visualisation given at the Vrije Universiteit Brussel.

Beat Signer
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May 04, 2023
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  1. 2 December 2005
    Information Visualisation
    View Manipulation and Reduction
    Prof. Beat Signer
    Department of Computer Science
    Vrije Universiteit Brussel
    beatsigner.com

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  2. Beat Signer - Department of Computer Science - [email protected] 2
    April 27, 2023
    View Manipulation

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  3. Beat Signer - Department of Computer Science - [email protected] 3
    April 27, 2023
    View Manipulation …

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  4. Beat Signer - Department of Computer Science - [email protected] 4
    April 27, 2023
    View Manipulation
    ▪ Why to manipulate and change the view?
    ▪ datasets might be too large to show everything at once
    - reduce complexity of single view
    ▪ single static view might lead to visual clutter
    ▪ How to manipulate/change a view over time?
    ▪ select specific elements (items or attributes)
    ▪ reordering (sorting) of items
    - find patterns by ordering based on different attributes
    ▪ change parameters of a particular idiom
    - e.g. range of possible mark sizes
    ▪ semantic zooming
    ▪ switch between idioms
    ▪ …

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  5. Beat Signer - Department of Computer Science - [email protected] 5
    April 27, 2023
    Change Between Visual Encoding Idioms

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  6. Beat Signer - Department of Computer Science - [email protected] 6
    April 27, 2023
    LineUp Example With Reordering
    ▪ Slope graphs (bump charts) with connecting line marks
    linking the same items together

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  7. Beat Signer - Department of Computer Science - [email protected] 7
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    LineUp
    LineUp
    What(Data) Table.
    What(Derived) Ordered attribute: weighted combination of selected attributes.
    Why(Task) Compare rankings, distributions.
    How(Encode) Stacked bar charts, slope graphs.
    How (Manipulate) Reorder, realign, animated transitions.

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  8. Beat Signer - Department of Computer Science - [email protected] 8
    April 27, 2023
    Animated Transitions Example
    ▪ Maintain a sense of context between two states
    Animated Transitions
    What(Data) Compound network.
    How (Manipulate) Change with animated transition. Navigation between aggregation
    levels.

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  9. Beat Signer - Department of Computer Science - [email protected] 9
    April 27, 2023
    Element Selection
    ▪ Different design choices for element selection
    ▪ which elements can be selection targets?
    - data items, links, data attributes, levels within a data attribute, …
    ▪ one kind of selection vs. multiple kinds of selection (e.g.via hover)
    - multiple mouse buttons or combination with key presses for more advanced
    types of selections
    ▪ selection of single elements vs. selection of many elements
    ▪ selection of primary and secondary target
    - e.g. for path traversal from source to target in a directed graph
    ▪ Selection often defines the target of a next action

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  10. Beat Signer - Department of Computer Science - [email protected] 10
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    Selection Highlighting
    ▪ Provide immediate visual feedback to users about
    element selection
    ▪ different possibilities for highlighting of data items
    - changing colour (hue, luminance or saturation) for visual popout
    - add or change existing outline
    - change the size of a data item
    - motion coding (e.g. slightly moving items of moving pattern)
    ▪ different possibilities for highlighting link marks
    - changing colour
    - changing linewidth, shape (e.g. dashed)
    - …
    ▪ multiple highlighting design choices can be combined
    ▪ selected items might be connected via explicit visual links
    (connection marks)

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  11. Beat Signer - Department of Computer Science - [email protected] 11
    April 27, 2023
    Context-preserving Visual Links Example

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  12. Beat Signer - Department of Computer Science - [email protected] 12
    April 27, 2023
    Context-preserving Visual Links
    Context-preserving Visual Links
    What(Data) Any data.
    How(Encode) Any encoding. Highlight with link marks connecting items across
    views.
    How (Manipulate) Select any element.
    How (Coordinate) Juxtaposed multiple views.

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  13. Beat Signer - Department of Computer Science - [email protected] 13
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    Navigate: Changing Viewpoint
    ▪ Navigation can help to see a large and complex dataset
    from different points of view
    ▪ changing viewpoint of virtual camera changes the set of items
    visible in the camera frame
    ▪ often leads to a combination of filtering and aggregation
    ▪ Three main aspects of navigation
    ▪ zooming
    - moves camera closer (less items but with more details) or further away
    (more items but less details) from the image plane
    - geometric zooming vs. semantic zooming
    ▪ panning (translating)
    - moves camera parallel to the image plane (up and down or from side to side)
    ▪ rotating
    - spins camera around its axis (rarely used in 2D navigation)

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  14. Beat Signer - Department of Computer Science - [email protected] 14
    April 27, 2023
    Semantic Zooming Example

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  15. Beat Signer - Department of Computer Science - [email protected] 15
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    Semantic Zooming
    ▪ In contrast to geometric zooming, the fundamental
    appearance of objects is no longer fixed
    ▪ object visualisation changes based on number of available pixels
    ▪ details added or removed based on the semantic zoom level
    ▪ different idioms might be used at different semantic zooms levels
    ▪ Constrained navigation limits the possible motion of the
    virtual camera
    ▪ avoids that user get lost by for example pointing the camera to an
    empty space or zooming out too much
    ▪ systems might also automatically compute the best viewpoint to
    view a selected item
    - smooth animated transition to the new viewpoint
    - powerful when combined with linked navigation between multiple views

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  16. Beat Signer - Department of Computer Science - [email protected] 16
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    Navigate: Reduce Attributes
    ▪ Number of attributes can be reduced in three different
    ways
    ▪ slice
    - single attribute value defines which items should be extracted
    - e.g. intuitive metaphor when reducing spatial data from 3D to 2D
    - possible to have higher dimensional slicing planes (hyperplanes)
    ▪ cut
    - plane dividing the viewing volume and everything on the side of the plane
    closer to camera viewpoint is not shown
    ▪ project
    - all items are shown but without the information for specific attributes
    - projections often used via multiple views
    • e.g. 2D views of a 3D XYZ scene (XY floor plan, YZ side view and XZ front view)
    • e.g. Mercator map projections from the surface of the earth to 2D maps

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  17. Beat Signer - Department of Computer Science - [email protected] 17
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    3D Scan Slice Example
    Axis-aligned slice
    Axis-aligned cut

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  18. Beat Signer - Department of Computer Science - [email protected] 18
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    Reducing Items and Attributes

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  19. Beat Signer - Department of Computer Science - [email protected] 19
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    Reducing Items and Attributes …
    ▪ Reduction is one of the strategies for dealing with
    complexity in visualisations
    ▪ filtering eliminates elements
    - challenge: people might forget about the filtered elements
    ("out of sight, out of mind")
    ▪ aggregation combines many elements together
    - challenge: how and what to summarise (aggregate) in order to support
    a task (and match well with the dataset)
    ▪ filtering and aggregation can be applied to items or attributes
    ▪ Bidirectional operation
    ▪ reduce or increase the number of visible elements

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  20. Beat Signer - Department of Computer Science - [email protected] 20
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    Filtering
    ▪ Filtering often accomplished through dynamic queries
    ▪ tightly coupled loop between visual encoding and interaction
    ▪ e.g. user can interactively chose a range for the values of an
    attribute via graphical UI widgets
    ▪ Item filtering
    ▪ reduce number of items based on their values for specific
    attributes
    ▪ Attribute filtering
    ▪ keep number of items but reduce the number of shown attributes
    ▪ often used with attributes that can be ordered to filter out the low
    or high scoring ones
    ▪ Item filtering and attribute filtering can be combined

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  21. Beat Signer - Department of Computer Science - [email protected] 21
    April 27, 2023
    FilmFinder Example
    Overview of all movies Filtering the actor 'Sean Connery'
    Details after clicking on a movie mark

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  22. Beat Signer - Department of Computer Science - [email protected] 22
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    DOSFA Example
    ▪ Dimensional Ordering, Spacing and Filtering Approach
    (DOSFA)
    ▪ 215 attributes (representing word counts) and 298 points
    representing documents in the example
    Full dataset After filtering

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  23. Beat Signer - Department of Computer Science - [email protected] 23
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    DOSFA
    DOSFA
    What(Data) Table: many values and attributes.
    How(Encode) Star plots.
    How (Facet) Small multiples with matrix alignment.
    How (Reduce) Attribute filtering.

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  24. Beat Signer - Department of Computer Science - [email protected] 24
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    Aggregation
    ▪ Group of elements represented by a derived element
    (aggregation)
    ▪ elements are merged rather than eliminated as with filtering
    ▪ challenge: aggregation (summary) might eliminate interesting
    signal in the dataset
    - e.g. see Anscombe's Quartet example presented earlier
    ▪ Item aggregation
    ▪ interactive aggregation and deaggregation of item sets
    ▪ Attribute aggregation
    ▪ group attributes by similarity measure and synthesize a new
    attribute based on average across the set
    ▪ dimensionality reduction (DR)

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  25. Beat Signer - Department of Computer Science - [email protected] 25
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    Histogram Example
    Histograms
    What(Data) Table: one quantitative value attribute.
    What (Derived) Derived table: one derived ordered key attribute (bin), one derived
    quantitative value attribute (item count per bin).
    How (Encode) Rectilinear Layout. Line mark with aligned position to express
    derived value attribute. Position: derived key attribute

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  26. Beat Signer - Department of Computer Science - [email protected] 26
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    Continous Scatterplot Example

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  27. Beat Signer - Department of Computer Science - [email protected] 27
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    Continuous Scatterplots
    Continuous Scatterplots
    What(Data) Table: two quantitative value attributes.
    What (Derived) Derived table: two ordered key attributes (x,y pixel locations), one
    quantitative attribute (overplot density).
    How (Encode) Dense space-filling 2D matrix alignment, sequential categorical
    hue and ordered luminance colourmap.
    How (Reduce) Item aggregation.

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  28. Beat Signer - Department of Computer Science - [email protected] 28
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    Boxplot Charts Example
    ▪ Boxplots show the spread and skew of the distribution
    Standard boxplots Vase plots

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  29. Beat Signer - Department of Computer Science - [email protected] 29
    April 27, 2023
    Boxplot Charts
    Boxplot Charts
    What(Data) Table: many quantitative value attributes.
    What (Derived) Five quantitative attributes for each original attribute, representing
    its distribution.
    Why (Tasks) Characterise distribution; find outliers, extremes, averages; identify
    skew.
    How (Encode) One glyph per original attribute expressing derived attribute values
    using vertical spatial position, with 1D list alignment of glyphs into
    horizontally separated regions.
    How (Reduce) Item aggregation.
    Scale Items: unlimited. Attributes: dozens.

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  30. Beat Signer - Department of Computer Science - [email protected] 30
    April 27, 2023
    Spatial Aggregation
    ▪ Challenge in spatial aggregation is to take the spatial
    nature of aggregation into account when aggregating it
    ▪ changing the boundaries can lead to very different
    results → modifiable areal unit problem (MAUP)
    Central region with high density Central region with medium density Central region with low density

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  31. Beat Signer - Department of Computer Science - [email protected] 31
    April 27, 2023
    Dimensionality Reduction (DR)
    ▪ Preserve the meaningful structure of a dataset while
    using fewer attributes to represent the items
    ▪ assumes that there is hidden structure and redundancy in the
    original dataset
    ▪ multidimensional scaling (MDS) for more complex forms (not just
    a straightforward combination) of dimensionality reduction
    ▪ Dimensionally reduced data can be visualised as
    scatterplot (two attributes) or as scatterplot matrix (more
    than two attributes)
    ▪ only large clusters should be considered relevant
    ▪ fine-grained structure should not be considered reliable

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  32. Beat Signer - Department of Computer Science - [email protected] 32
    April 27, 2023
    Dimensionality Reduction (DR) Example
    2D scatterplot of large document collection

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  33. Beat Signer - Department of Computer Science - [email protected] 33
    April 27, 2023
    DR for Document Collections
    Dimensionality Reduction for Document Collections
    What(Data) Text document collection.
    What (Derived) Table with 10'000 attributes.
    What (Derived) Table with two attributes.
    How (Encode) Scatterplot, coloured by conjectured clustering.
    How (Reduce) Attribute aggregation (dimensionality reduction) with
    multidimensional scaling (MDS)
    Scale Original attributes: 10'000. Derived attributes: two. Items: 100'000

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  34. Beat Signer - Department of Computer Science - [email protected] 34
    April 27, 2023
    Exercise 8
    ▪ Interaction with D3.js

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  35. Beat Signer - Department of Computer Science - [email protected] 35
    April 27, 2023
    Further Reading
    ▪ This lecture is mainly based on the
    book Visualization Analysis & Design
    ▪ chapter 11
    - Manipulate View
    ▪ chapter 13
    - Reduce Items and Attributes

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  36. Beat Signer - Department of Computer Science - [email protected] 36
    April 27, 2023
    References
    ▪ Visualization Analysis & Design, Tamara
    Munzner, Taylor & Francis Inc, (Har/Psc edition),
    May, November 2014,
    ISBN-13: 978-1466508910

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  37. 2 December 2005
    Next Lecture
    Interaction

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