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![Page 1: School of Computer Science The craft of Information Visualization NCRM Research Methods Festival 2008 Jonathan C. Roberts School of Computer Science Bangor.](https://reader030.fdocuments.in/reader030/viewer/2022033100/56649e445503460f94b39002/html5/thumbnails/1.jpg)
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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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2 J.C.Roberts
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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3 J.C.Roberts
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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4 J.C.Roberts
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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5 J.C.Roberts
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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6 J.C.Roberts
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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7 J.C.Roberts
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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8 J.C.Roberts
Bertin COMPONENT analysis
Bertin’s component analysis• invariant and variational components • number of Components • length of Components • organisation of Components
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9 J.C.Roberts
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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10 J.C.Roberts
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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11 J.C.Roberts
Different Mappings
• 3 ..4 variables
independent
dependent
The values are extra dependent values on the same independent parameter.
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12 J.C.Roberts
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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13 J.C.Roberts
2D .. 3D
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14 J.C.Roberts
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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15 J.C.Roberts
Scatter Plot Matrices
Reorderable matrix Scatter Plot Matrices
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16 J.C.Roberts
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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17 J.C.Roberts
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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18 J.C.Roberts
Point line duality
Line in Euclidean The line is represented by the crossing
l
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19 J.C.Roberts
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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20 J.C.Roberts
Interacting with ||- Coordinates
http://software.fujitsu.com/en/symfoware/visualminer/vmpcddemo.pdf
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21 J.C.Roberts
Selecting a range of records
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22 J.C.Roberts
Selecting records
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23 J.C.Roberts
Verifying a hypothesis
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24 J.C.Roberts
Highlighting relationships
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25 J.C.Roberts
Separating different record groups
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26 J.C.Roberts
Another observation
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27 J.C.Roberts
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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28 J.C.Roberts
Multiple Views
• Display different information in different views
[Waltz, Roberts]
cdv - Cartographic Visualization for Enumerated Data [Dykes]
Same color
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29 J.C.Roberts
Dual views – focus+context
Dual views [Roberts]
Table Lens
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30 J.C.Roberts
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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31 J.C.Roberts
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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32 J.C.Roberts
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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33 J.C.Roberts
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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34 J.C.Roberts
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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35 J.C.Roberts
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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36 J.C.Roberts
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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37 J.C.Roberts
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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38 J.C.Roberts
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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39 J.C.Roberts
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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40 J.C.Roberts
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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41 J.C.Roberts
Techniques for Level
DeleteRe-orderClusterClassPromoteAverageAbstract/SummarizeInstantiateExtractComposeOrganize
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42 J.C.Roberts
Things to remember…
Six important aspects of an Information Visualization:
• Data• Visual Structures• Multiple Views• Interaction & Exploration• Tasks (& Management of tasks)• Level & organization
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Sch
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The craft of The craft of Information VisualizationInformation Visualization
Jonathan C. RobertsSchool of Computer ScienceBangor University
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