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The craft of The craft of Information VisualizationInformation Visualization

NCRM Research Methods Festival 2008

Jonathan C. RobertsSchool of Computer ScienceBangor University

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Minard’s plot

http://www.math.yorku.ca/SCS/Gallery/re-minard.html

The French engineer, Charles Minard (1781-1870), illustrated the disastrous result of Napoleon's failed Russian campaign of 1812.

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The 1854 London Cholera Epidemic.

One of the first uses of a map to display epidemiological data was this dot chart (from Tufte, 1983, p. 24) by Dr. John Snow (1855) showing deaths from cholera (dots) in relation to the locations of public water pumps.

Tufte says, "Snow observed that cholera occurred almost entirely among those who lived near (and drank from) the Broad Street water pump. He had the handle of the contaminated pump removed, ending the neighborhood epidemic which had taken more than 500 lives."

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Advantages of Information Visualization

Visualization provides:1. The ability to comprehend huge amounts of information2. The perception of emergent properties that were not

anticipated3. problems with the data to be made apparent (e.g. errors

or artefacts of the data)4. Large/Small scale features can be seen5. facilitation of hypothesis formation

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Schematic of the visualization process

Data

Pre-processingAnd

transformation

Gra

phic

s Engin

e

Human

Physical Env.

Social Env.

Datagathering

Datamanipulation

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Things to consider…

Six important aspects of an Information Visualization:

• Data• Visual Structures• Multiple Views• Interaction & Exploration• Tasks (& Management of tasks)• Level & organization

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1. Data & Visual Structures..

• maps interesting data items to graphics objects • Bertin methodology • maps the CONTENT (information to be transmitted - filtered data) to

the CONTAINER (the properties of the display/graphic system) using a COMPONENT analysis.

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Bertin COMPONENT analysis

Bertin’s component analysis• invariant and variational components • number of Components • length of Components • organisation of Components

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Bertin CONTAINER - graphic system properties

• Representation Styles • diagrams, networks, maps, symbols • Retinal Variables • Level of organisation

– point, line, area, volume

Main retinal Variables:PositionSizeColour (Hue, saturation, value)OrientationShapeTexture

Additional retinal variables

Motion – velocityMotion – directionFlicker – frequencyFlicker – phase

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Different Mappings

• 2 variablesIndependent and DependentWhen an experiment is conducted, some variables are manipulated by the experimenter (these are called “independent variables”)

and others are measured from the subjects (these are “dependent variables” or “dependent measures.”

independent

dependent

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Different Mappings

• 3 ..4 variables

independent

dependent

The values are extra dependent values on the same independent parameter.

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The data table… (spreadsheet)

• This is ok when there is only one independent variable. But what if we have multiple independents?

2003 2002 2001DATE Number Number Number

5/12 110.523 115.741 96.80755/22 109.2965 114.821 103.69255/28 109.5315 114.651 105.2126/4 106.5815 115.1005 107.37156/24 107.176 114.8405 109.147/11 106.838 116.3 109.8287/22 109.668 116.945 110.5238/5 109.235 118.4255 109.29656/4 109.7525 118.4255 109.29656/24 110.0475 118.4255 109.2965independent

dependent

dependent

dependent

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2D .. 3D

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Multivariate, Car

Variable Car1 Car2

MPG 32 43

Weight 1000kg 1100kg

Top Speed 130 140

0-60 4 5

Cylinders 8 6

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Scatter Plot Matrices

Reorderable matrix Scatter Plot Matrices

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Parallel-coordinates (PC or ||-coords)

• Parallel coordinates yield graphical representations of multi-dimensional relations rather than just finite points sets.

• Place the axis parallel and join the dots• Euclidean 3d geometry. X,y,z coordinates

– Point in space is given by extents along the axis• ||-coordinates. Point is a line

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So what is a point…

• A n-d point is equivalent to a line in ||-coordinates

http://catt.bus.okstate.edu/jones98/parallel.html

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Point line duality

Line in Euclidean The line is represented by the crossing

l

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Cubes..

• Parallel coordinates provides a very simple representation of high dimensional objects such as hypercubes. 

• Consider the Parallel coordinate plot of the four corners of a two-dimensional square:

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Interacting with ||- Coordinates

http://software.fujitsu.com/en/symfoware/visualminer/vmpcddemo.pdf

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Selecting a range of records

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Selecting records

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Verifying a hypothesis

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Highlighting relationships

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Separating different record groups

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Another observation

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Visual Structures - Techniques

Graphical properties: placing appropriate marks

Substitute different properties with different marks

Aligning data on different axescomposing data

Overlaying data on top

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Multiple Views

• Display different information in different views

[Waltz, Roberts]

cdv - Cartographic Visualization for Enumerated Data [Dykes]

Same color

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Dual views – focus+context

Dual views [Roberts]

Table Lens

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Multiple View Techniques

Different views may be better at displaying that information

Correlations between views can be highlighted Through brushing or zooming

One view can be for Focus another for context (focus+context)

One view can be for Overview another for detail (overview+detail)

Distortion can be used to (say) place more information in a small area

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4. Interaction & Exploration

• Allow the user to change their mind and explore the data• To provide sliders/buttons/menus to choose how the data is to be

viewed• To select a subset of the information (zoom into this…)

• E.g. Brushing– a collection of techniques to dynamically

query and directly select elements on the visual display.

– Usually in dual views (or more)– Such interaction allows the user to explore

the visualization to interactively select a subset of points and see how these changesare updated in other related views.

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Zoom

• To focus, Select (or highlight) a feature set of information– Zoom: telephoto-lens, reduced field of view

– 3D clipping

– Semantic zoom

Alternate Representations[Roberts, Ryan]

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Dynamic queries

• Instant update– Direct manipulation

– Sliders/buttons

Example of a dynamic queries environment created with IVEE Measurements of heavy metals in Sweden

FilmFinder: Ahlberg, Shneiderman

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Interaction Techniques

Dynamic Queries (indirect manipulation)

Direct Manipulation

Overlays (e.g. magic lens)

Coordination of viewswhich are coordinated?how are they coordinated?

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Filter & Extract

• Visual extraction– constant quantity of information

– brush and highlight visually altered to stand out (colour, size ...)

– sliders (1 < highlight < 4 ...)• Subset (filter) of the data

– extract portions of the dataset

– Specialize semi-automatic/manual

(seed-point, selection) neighborhood / global operations

1 1 1 1 11 2 3 2 11 3 9 3 11 2 3 2 11 1 1 1 1

1 1 1 1 11 2 3 2 11 3 9 3 11 2 3 2 11 1 1 1 1

1 1 1 1 11 2 3 2 11 3 9 3 11 2 3 2 11 1 1 1 1

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Filter & Extract

• Visual extraction– constant quantity of information

– brush and highlight visually altered to stand out (colour, size ...)

– sliders (1 < highlight < 4 ...)• Subset (filter) of the data

– extract portions of the dataset

– Specialize semi-automatic/manual

(seed-point, selection) neighborhood / global operations

1 1 1 1 11 2 3 2 11 3 9 3 11 2 3 2 11 1 1 1 1

1 1 1 1 11 2 3 2 11 3 9 3 11 2 3 2 11 1 1 1 1

1 1 1 1 11 2 3 2 11 3 9 3 11 2 3 2 11 1 1 1 1

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Filter & Extract

• Visual extraction– constant quantity of information

– brush and highlight visually altered to stand out (colour, size ...)

– sliders (1 < highlight < 4 ...)• Subset (filter) of the data

– extract portions of the dataset (isolate)

– Specialize/Generalize semi-automatic/manual

(seed-point, selection) neighborhood / global operations

1 1 1 1 11 2 3 2 11 3 9 3 11 2 3 2 11 1 1 1 1

1 1 1 1 11 2 3 2 11 3 9 3 11 2 3 2 11 1 1 1 1

3 3 3 3

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5. Tasks (& Management of tasks)

• Foraging for data• Solving problems and investigating hypothesis• Searching for some data (or the lack of data)• Making quantitative/qualitative analysis• Querying and finding evidence for decision making

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Techniques to perform the Task

OverviewZoomFilterDetails on demand

BrowseSearchRead (facts or patterns)CompareManipulateExploreCreateDisseminate and present

From. Readings in information visualization - Card/Mackinlay

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6. Level & organization

• What is the right level-of-detail?– Are there too many points on display

(abstract/summarize/bin/aggregate)

• How is the information organized? • Think what is close and what is near

– Near objects are easier to compare

– E.g. re-order the axes on a ||-coord plot

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Techniques for Level

DeleteRe-orderClusterClassPromoteAverageAbstract/SummarizeInstantiateExtractComposeOrganize

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Things to remember…

Six important aspects of an Information Visualization:

• Data• Visual Structures• Multiple Views• Interaction & Exploration• Tasks (& Management of tasks)• Level & organization

Sch

ool of

Com

pu

ter

Scie

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The craft of The craft of Information VisualizationInformation Visualization

Jonathan C. RobertsSchool of Computer ScienceBangor University

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