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

Beat Signer - Department of Computer Science - [email protected] 2 April 25, 2024 View Manipulation

Beat Signer - Department of Computer Science - [email protected] 3 April 25, 2024 View Manipulation …

Beat Signer - Department of Computer Science - [email protected] 4 April 25, 2024 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 ▪ …

Beat Signer - Department of Computer Science - [email protected] 5 April 25, 2024 Change Between Visual Encoding Idioms

Beat Signer - Department of Computer Science - [email protected] 6 April 25, 2024 LineUp Example With Reordering ▪ Slope graphs (bump charts) with connecting line marks linking the same items together

Beat Signer - Department of Computer Science - [email protected] 7 April 25, 2024 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.

Beat Signer - Department of Computer Science - [email protected] 8 April 25, 2024 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.

Beat Signer - Department of Computer Science - [email protected] 9 April 25, 2024 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

Beat Signer - Department of Computer Science - [email protected] 10 April 25, 2024 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)

Beat Signer - Department of Computer Science - [email protected] 11 April 25, 2024 Context-preserving Visual Links Example

Beat Signer - Department of Computer Science - [email protected] 12 April 25, 2024 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.

Beat Signer - Department of Computer Science - [email protected] 13 April 25, 2024 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)

Beat Signer - Department of Computer Science - [email protected] 14 April 25, 2024 Semantic Zooming Example

Beat Signer - Department of Computer Science - [email protected] 15 April 25, 2024 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

Beat Signer - Department of Computer Science - [email protected] 16 April 25, 2024 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

Beat Signer - Department of Computer Science - [email protected] 17 April 25, 2024 3D Scan Slice Example Axis-aligned slice Axis-aligned cut

Beat Signer - Department of Computer Science - [email protected] 18 April 25, 2024 Reducing Items and Attributes

Beat Signer - Department of Computer Science - [email protected] 19 April 25, 2024 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

Beat Signer - Department of Computer Science - [email protected] 20 April 25, 2024 Filtering ▪ Filtering often accomplished through dynamic queries ▪ tightly coupled loop between visual encoding and interaction ▪ e.g.user can interactively choose 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

Beat Signer - Department of Computer Science - [email protected] 21 April 25, 2024 FilmFinder Example Overview of all movies Filtering the actor 'Sean Connery' Details after clicking on a movie mark

Beat Signer - Department of Computer Science - [email protected] 22 April 25, 2024 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

Beat Signer - Department of Computer Science - [email protected] 23 April 25, 2024 DOSFA DOSFA What(Data) Table: many values and attributes. How(Encode) Star plots. How (Facet) Small multiples with matrix alignment. How (Reduce) Attribute filtering.

Beat Signer - Department of Computer Science - [email protected] 24 April 25, 2024 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 synthesise a new attribute based on average across the set ▪ dimensionality reduction (DR)

Beat Signer - Department of Computer Science - [email protected] 25 April 25, 2024 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

Beat Signer - Department of Computer Science - [email protected] 26 April 25, 2024 Continous Scatterplot Example

Beat Signer - Department of Computer Science - [email protected] 27 April 25, 2024 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.

Beat Signer - Department of Computer Science - [email protected] 28 April 25, 2024 Boxplot Charts Example ▪ Boxplots show the spread and skew of the distribution Standard boxplots Vase plots

Beat Signer - Department of Computer Science - [email protected] 29 April 25, 2024 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.

Beat Signer - Department of Computer Science - [email protected] 30 April 25, 2024 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

Beat Signer - Department of Computer Science - [email protected] 31 April 25, 2024 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

Beat Signer - Department of Computer Science - [email protected] 32 April 25, 2024 Dimensionality Reduction (DR) Example 2D scatterplot of large document collection

Beat Signer - Department of Computer Science - [email protected] 33 April 25, 2024 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

Beat Signer - Department of Computer Science - [email protected] 34 April 25, 2024 Exercise 8 ▪ Interactive Visualisations

Beat Signer - Department of Computer Science - [email protected] 35 April 25, 2024 Further Reading ▪ This lecture is mainly based on the book Visualization Analysis & Design ▪ chapter 11 - Manipulate View ▪ chapter 13 - Reduce Items and Attributes

Beat Signer - Department of Computer Science - [email protected] 36 April 25, 2024 References ▪ Visualization Analysis & Design, Tamara Munzner, Taylor & Francis Inc, (Har/Psc edition), May, November 2014, ISBN-13: 978-1466508910

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