Chapter 13 Facet into Multiple Viewscggm · –Multiform design choice » Uses different encoding in each view to show the same data – no single encoding is optimal for all possible

Chapter 13

Facet into Multiple Views

Vis/Visual Analytics, Chap 13 Multiple Views

CGGM Lab., CS Dept., NCTU Jung Hong Chuang 1

The Big Picture

• Datasets are large and complex

– Showing everything in a view visual clutter

– There are five options for handling complexity

• Change view over time

– most obvious, most popular, and most flexible one

• Derive new data (chap 4)

• Facet into multiple views (chap. 13)

• Reduce items and attributes (chap 14)

• Embed: Focus + Context in a single view (chap 15)

– Are not mutually exclusive, and various combinations of them are common



The Big Picture

• Handle visual complexity

– No single visual encoding optimal for all tasks

• Covers choices about how to facet data across multiple views

• Options for showing views and design choices

– Juxtapose

• Juxtapose views side by side

• How to coordinate them?

– Which visual encoding channels are shared between them?

– How much of the data is shared between them?

– Whether the navigation is synchronized?

– When to show each view and how to arrange them?

Vis/Visual Analytics, Chap 13 Multiple Views CGGM Lab., CS Dept., NCTU Jung Hong Chuang 3

The Big Picture

– Superimpose

• Views are put as layers on top of each other

• Design choices

– How elements are partitioned between layers?

– How many layers to use?

– How to distinguish them from each other?

– Whether the layers are static or dynamically constructed?


The Big Picture


Design choices of how to facet information between multiple views.

Why Facet?

• Facet into multiple views

– Split up the display into multiple views or layers

• Juxtapose multiple views side by side

– Multiple views vs. changing views

» Multiple views: comparing multiple views is relatively easy

» Changing views: requires users to consult their working memory

– Multiform design choice

» Uses different encoding in each view to show the same data – no single encoding is optimal for all possible tasks

» Supports more tasks

» Coordinate multiform views with linked highlighting is important



Why Facet?

• Facet into multiple views

– Split up the display into multiple views or layers

• Juxtapose multiple views side by side

– Small Multiples design choice

» Partition the data between views

» Choice of which attributes to partition vs. which to directly encode with, and the partitioning order

– Display area for each view is smaller!

• Superimposing layers

– Does not require more screen space!

– A way to control visual clutter in complex visual encoding

– Can work with changing the view over time

– Creating visually distinguishable layers imposes serious constraints on visual encoding choices

– # of layers: 2 is very feasible, 3 is possible with care!



Juxtaposed views

• An alternative to a changing view

– Save time

– Relatively easy to compare

• Significant cost

– More display area

– More working memory

• Be able to accommodate a much larger number of views



Top 10 baby names



http://www.visualcinnamon.com/babynamesus

Superimposing layers

• Not require more screen space

• Control visual clutter in complex visual encoding

– Less clutter view than single view

– Need an interactive view for dynamically layer construction

• Limitation

– Less visual encoding choices

– The number of views and Visual clutter



Superimposing layers



Share Encoding: Same/Different

• Visual encoding

– Some aspects same, not necessarily all in multiform views

• Linked highlighting

– The Central benefit is to see distributed within all views

• Single view has strong limits on the number of attributes to show

– multiform encoding should show simultaneously without introducing too much visual clutter



Exploratory Data Visualizer(EDV)



Linked highlighting between views shows how regions that are contiguous in one view are distributed within another

Share Data: All, Subset, None

• Overview-detail

– One shows a subset of what is in the other

• Design choice

– How many views to use in total

– Data sharing with multiform views or not

• Overview-detail case

– Large for detail, small one be the zoomed-out overview

– Large for overview, a smaller for detail

– different partitions of the dataset, called small multiples



Multiform Overview-Detail Microarrays



Multiform overview–detail vis tool for microarray exploration features a central scatterplot linked with the graph view in the upper left

Small-multiple

• The inverse of multiform views in some sense

– The encoding is identical but the data differs

• Weakness

– show all of these views simultaneously

• Strength

– Allow user to glance different partitions of the dataset quickly

– As an alternative to animations



Drought’s Footprint

Vis/Visual Analytics, Chap 11 Map Color/Channels CGGM Lab., CS Dept., NCTU Jung Hong Chuang 17

Areas under moderate to extreme drought in June of each year are shown in orange below.

Share Navigation: Synchronize

• Linked navigation

– Common with map views

– interaction in the small window and change in the large one

• Coordinating views

– How interact with each other

– Data are shared or different



Bird’s-Eye Maps



↑Overview–detail example with geographic maps, where the

views have the same encoding and dataset; they differ in viewpoint and size.

Combinations

• Encoding

– same or different

• Data

– same, a subset, or a partition



Improvise



Improvise toolkit was used to create this census vis that has many forms of coordination between views. Multiform views, some of which use small multiples, and some of which provide additional detail information.

Juxtapose Views

• Two addition design choices

– When to show each view

– How to arrange them

• Appears timing

– usually permanently

– pops up in response to a user action

• Arrangement

– Sometimes not under the control of vis designer

– burdensome load when views are too much

– Arrange themselves automatically is sophisticated



Partition into Views

• Design choice of how to partition a multiattribute dataset into meaningful group

– Has major implications for what kind of patterns are visible to the user!

– Encodes association between items using spatial proximity

– Design choices

• How many splits to carry out?

• The order in which attributes are used to split things up

• How many views to use?



Partition into Views

• Partitioning attribute

– Typically a categorical attribute that has only a limited number of unique values; i.e., levels

– Derived attribute

• A quantitative attribute -> dividing it up into a limited number of bins

– Key attribute

– Value attribute



Partition into Views Regions, Glyphs, and Views • Partitioning data -> data in group

• Visual encoding choice for partitioning

– A partitioned group can be placed within a region of space

• These regions need to be ordered and often aligned

– Aligned and ordered within a 1D list, or 2D matrix.

– Recursive subdivision allows these regions to nest inside each other

• Nested regions may be arranged using the same choices as their enclosing regions or different choices



Partition into Views Regions, Glyphs, and Views • Dataset has only one key attribute

– Use that key to separate into one region per item

• Dataset has multiple keys

– Several possibilities for partitioning

• Given two keys X and Y

– First separate by X and then by Y

– First separate by Y and then by X

– Separate by only one key

• The complexity of what is encoded in a region falls along a continuum

– Could be just a single mark, more complex glyph, could be a full view



Partition into Views Regions, Glyphs, and Views • No strict dividing line between a region, a

view, and a glyph

– View: large, stand-alone, highly detailed regions

• Ex: A single bar chart that is 800 pixels wide and 400 pixels high, with axes that have labels and tick marks

– Glyph: small, nested, schematic regions

• Ex: A set of bar charts that are each 50 by 25 pixels, each with a few schematic bars and unlabeled axes. Each appears within a geographic region on a map

• Macroglyph

– Previous example

• Microglyph



Partition into Views List Alignments • How different partitioning decisions enable

different tasks

– Example

• Comparing grouped bar charts to small-multiple aligned bar charts, see Fig. on next slide

– Grouped bar chart

» A multibar glyph is drawn within each region, using seven-mark glyphs within each region

» Facilitates comparison between the attributes

– Small-multiple aligned bar charts

» Shows several standard bar charts, one in each vi

» Facilitates comparison within a single attributes



Partition into Views List Alignments

– Two unified interpretations

• Both as glyphs

– Grouped bar chart idiom uses a smaller multibar glyph

– Small-multiple bar chart idiom uses a larger bar-chart glyph

• Both in terms of partitions

– Both idioms use two levels of partitioning

» At the high level by a first key

» At a lower level by a second key

» Finally a single mark is drawn within each subregion

– Grouped bars

» The second-level regions are interleaved within the first-level region

– Small-multiple bars

» The second-level regions are contiguous within a single first-level region



Partition into Views List Alignments



Partitioning and bar charts. (a) Single bar chart with grouped bars: separated by state key into regions, using seven-mark glyphs within each region. (b) Four aligned small-multiple bar chart views: separated by group key into vertically aligned list of regions, with a full bar chart in each region.

Partition into Views Matrix Alignments Trellis system

• Partition a multi-attribute dataset into multiple views and order them within a matrix alignment

• Dataset – barley yield

– Three categorical attributes act as keys

• Site – six unique levels

• Variety – ten levels

• Year – two levels

– One quantitative attribute

• yield



Partition into Views Matrix Alignments Trellis system

• Matrix of dot charts views. Partition

– By year for matrix columns

– By site for matrix rows

– Within the dot chart views

• Vertical axis is separated by variety

• Horizontal axis

– with yield as the quantitative value expressed as spatial position

– Ordering idiom: main-effects ordering

• The derived attribute of the median value is computed for each group and used to order the views

– See views for matrix rows



Partition into Views Matrix Alignments • The value of main-effects ordering

– Outliers countervailing to the general trends are visible

• The Morris plots in 3rd row of the figure do not match up with the others

• Suggest that perhaps the years had been switched!

• Alphabetical ordering of plots and axes

– Does not provide any useful hints of outliers versus the trends since no particular general trend is visible at all



Partition into Views Matrix Alignments



Partition into Views Matrix Alignments • Another plot

– Partitioned vertically by site, but no further

– Both years are thus included within the same view and distinguished by color

• The switch in color patterns in the 3rd row -> Morris data is incorrect!



Partition into Views Matrix Alignments



Partition into Views Recursive Subdivision • Partitioning can be used in an exploratory

way

– The user can reconfigure the display to see different choices of partitioning and encoding variables



Partition into Views Recursive Subdivision Hierarchical Visual Expression (HiVE)

• Dataset: Property transactions in London area

– Categorical attribute “residence type”: 4 levels

• Flat: Flats (apartment)

• Ter: terrace (露臺) houses

• Semi: semidetached (雙併) houses

• Det: fully detached (獨棟) houses

– Quantitative attribute “price”

– Ordered attribute “time of sale”

– “neighborhood” attribute: 33 levels

• Can be considered as categorical or as spatial



Partition into Views Recursive Subdivision • First partition choice

• Top-level partition

– Split into 4 regions based on “type”

• Next-split

– Use the “neighborhood” attribute

• Final-split

– Split by “time of sale”

» Ordered with year from left to right and month from top to bottom

• Base-level

– Each square is color coded by the derived attribute of price variation within the group

• Patterns within the top-level are very different!

• Coloring with each second-level square is more consistent! (similar prices within the same nhd)



Partition into Views Recursive Subdivision • Second partition choice

• Top-level partition

– Use the “neighborhood” attribute

• Next-split

– Split into 4 regions based on “type”

• Final-split

– Split by “time of sale”

• Base-level

– Each square is color coded by the derived attribute of average price within the group

• Easy to spot expensive neighborhoods

– The views near the center

• Easy to see that detached houses are more expensive!

– In the lower right corner of each view



Partition into Views Recursive Subdivision



Patterns within the top-level are very different! Coloring with each second-level square is more consistent! (similar prices within the same nhd)

Easy to spot expensive neighborhoods The views near the center

Easy to see that detached houses are more expensive!

In the lower right corner of each view

Partition into Views Recursive Subdivision • Third partition choice

• Same order of partitioning as the first choice

• Different in the spatial arrangement

– The regions are sized according to the # of sales, yielding variably sized rectangles

– Can be interpreted as a treemap

» Tree structure is implicitly derived by the partitioning order

• Fourth partition choice • Top level

– By type

• Second level

– Shows the information geographically using choropleth maps



Partition into Views Recursive Subdivision



HiVE with different arrangements. (a) Sizing regions according to sale counts yields a treemap. (b) Arranging the second-level regions as choropleth maps.

Superimpose Layers

• Combine multiple layers together by stacking them on top of each other in a single view

– A visual layer is simply a set of objects spread out over a region

• Set of objects in each layer is a visually distinguishable group

– Design choices

• How many layers are used?

• How are the layers visually distinguishable from each other?

• Is there a small static set of layers that do not change, or are the layers constructed dynamically in response to user selection?



Superimpose Layers Visually Distinguishable Layers • One good way to make distinguishable

layers

– To ensure that each layer uses a different and non-overlapping range of the visual channels active in the encoding

• A common choice

– A foreground + a background layers

– How many layers?

• Two is feasible

• Three is possible with careful design

• Many layers is only feasible if each contains very little, such as a single line



Superimpose Layers Static Layers • Design choice: all layers are displayed

simultaneously

• Cartographic layering • Lets the viewer easily shift attention between layers

• Area marks form a background layer

– with 3 different unsaturated colors distinguishing water, parks, and other land

• Line marks form a foreground layer

– for the road network, with wide lines for main roads in a fully saturated red color and small roads with thinner black lines

• Work well because of the luminance contrast between the elements on different layers



Superimpose Layers Static Layers



Static visual layering in maps. (a) The map layers are created by differences in the hue, saturation, luminance, and size channels on both area and line marks. (b) The grayscale view shows that each layer uses a different range in the luminance channel, providing luminance contrast.




Superimpose Layers Static Layers • Superimposed line charts

– Several lines representing different data items are superimposed to create a combined charts

– Amount of occlusion

• Three lines: very small. Nearly 12 lines: still usable

• Many dozens of lines: does not scale!






Multiple line charts can be superimposed within the same global frame. (a) A small number of items is easily readable. (b) Up to a few dozen lines can still be understood. (c) This technique does not scale to hundreds of items.

Superimpose Layers Static Layers • Compared to juxtaposed filled-area line

chart

– Local maximum task

• Find the time series with the highest value at a specific point in time

– Global slope task

• Find the time series with the highest increase during the entire time period

– Global discrimination task

• Check whether values were higher at different time points across the series






Empirical study comparing superimposed line charts to juxtaposed filled-area line charts. (a) Superimposed line charts performed best for local tasks carried out within a local visual span. (b) Juxtaposed filled area charts were best for global tasks that require large visual spans, especially as the number of time series increased.

Superimpose Layers Static Layers • Hierarchical edge bundles

– A more complex example

• Work on a compound network

• Dataset

– Call graph network

» Which functions call what other functions in a software system, in conjunction with the hierarchical structure of the source code in which these function calls are defined

• Two easily distinguishable layers using color

– The source code hierarchy layer is gray

» Shown with circular containment marks

– Semitransparent red-green layer with the call graph network edges

» Edge bundling






The hierarchical edge bundles idiom shows a compound network in three layers: the tree structure in back with containment circle marks, the red–green graph edges with connection marks in a middle layer, and the graph nodes in a front layer.




Superimpose Layers Dynamic Layers • With dynamic layers

– A layer with different salience than the rest of the view is constructed interactively, typically in response to user selection

• # of layers

– can be huge since they are constructed on the fly than chosen from a very small set of possibilities



Superimpose Layers Dynamic Layers • Cerebral system

– Network data

– Dynamically create a foreground layer that updates constantly as the user moves the curser

• When the curser is directly over a node

– The foreground layer shows its one-hop neighborhoods + the links, with a distinctive fully saturated red color



Superimpose Layers Dynamic Layers



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Chapter 13 Facet into Multiple Viewscggm · –Multiform design choice » Uses different encoding in each view to show the same data – no single encoding is optimal for all possible