Robert S. Laramee [email protected] 1 Visualization: An Introduction and Applications Robert...

1 Visualization: An Introduction and Applications

Robert S. [email protected]

Visualization:An Introduction and

Applications

Robert S. Laramee

Department of Computer Science

Swansea University



Overview Introduction Visualization Volume Visualization (+Application) Flow Visualization (+Application)

Information Visualization (+Application)

Conclusions



Introduction: Who is Bob? July 2006: Joined Computer Science Dept at Swansea University

2001-2006: worked at VRVis Research Center (VRVis.at)–the bridge between academia and industry in Austria

2005: PhD, Computer Science, Vienna University of Technology (TUWien)

2000: Msc., Computer Science, University of New Hampshire, Durham, NH

1997: BSc., Physics, University of Massachusetts (ZooMass), Amherst, MA

Research in Data visualization Software Engineering Human-computer interaction



Visualization: What is it?

“The purpose of computing is insight, not numbers”

[Richard W. Hamming, 1962]

Visualization: A tool that allows the user to gain insight into data.

To form a mental vision, image, or picture of (something not visible or present to the sight, or of an abstraction); to make visible to the mind or imagination [Oxford English Dictionary, 1989]

The non-fiction version of computer graphics



Visualization – Background Visualization is very old

Often an intuitive step to make

something clearer, e.g., a graph

Data set sizes are ever-increasing making a graphical approach necessary

Classical (easy) approaches known from business graphics (Excel, etc.)

Visualization = its own scientific discipline since ~1987

First visualization conference: 1990

L. da Vinci (1452-1519)

1997:



User & Task

What is the problem?

User task/questionsVisualization goals

Examples: surgery planning filter analysis stock market vis.

[Oeltze et al., 2004]

[Shneiderman/Wattenberg, 2001]

[VRVis, 2004]



Three

examples

Data & Domain Application

domain Data origin Data

organization Data extent Form of data

[TIANI, 2001]

[illustration by Kirby et al., 1999]

[Bischi et al., 2004]



Typical Visualization TasksVisualization is good for:

exploration find the unknown, unexpected hypothesis generation

analysis confirm or reject hypotheses information drill-down

presentation communicate/disseminate

results

[Seo/Shneiderman 2004]

[Doleisch et al., 2003]

[Bruckner/Gröller

2005]



Visualization: 3 FociThree main sub-fields:

Volume Visualization

Flow Visualization

Information Visualization

Scientific Visualization

3D

nD

Inherent geometry

Usually no inherent geometry



From the Data Point of View

Data with inherent location (and time) – “SciVis“ 3D, 3D+time, 2D, etc. scalar, vector, multi-variate, etc. data organization: Cartesian grid, simulation

grid, etc. continuous

Tabular data (lines rows) – “InfoVis“ multi-variate data heterogeneous data (nominal, categorical, etc.) discontinuous (or discrete)



What is Volume Visualization?Volume Visualization is the depiction of Volume

data Picture/image 3D2D Projection (e.g., MIP), slicing, surface

extraction, volume rendering, … Volume Data =

3D1D Data scalar data, 3D data space, space filling

(dense-as opposed to sparse) User goals:

to gain insight into 3D Data depends strongly on what user is interested in

(focus + context)



About Volume VisualizationExample a density volume from CT etc.

perceptual challenge (3D→2D) selection (slices) vs. projection

(rendering) feature selection

performance challenge (billions of data items) data structures (grids) utilizing the GPU



Volume Visualization: An Application

Medical Visualization



What is Flow Visualization? a classic topic within scientific visualization depiction of vector quantities (as opposed to scalar

quantities) applications include: automotive design, aerodynamics,

astronomy, engineering, fluid mechanics, meteorology, oceanography, medicine, simulation, turbomachinery,

Challenges:

1. to effectively visualize both magnitude + direction often simultaneously

2. large data sets

3. time-dependent data

4. What should be visualized? (data filtering/feature extraction)



What is Flow Visualization? Challenge: to effectively visualize both

magnitude + direction often simultaneously

magnitude only direction only



swirl motion: characterized by motion about cylinder-aligned axis

more stable (easier)

tumble motion: characterized by motion about axis orthogonal to cylinder

unstable, more difficult

Flow Visualization: An Application



Achieving ideal patterns of motion leads to optimal mixing (of air and fuel) conditions e.g., higher exhaust/gas ratio (EGR) decrease in fuel consumption lower emissions

1. Can visualization provide insight into or verify characteristic shape/behavior of flow?

2. What tools help to visualize swirl/tumble motion?

3. Where (in the combustion chamber) are ideal ideal flow pattern not being realized?




InfoVis – Categorical Data Vis. Example: set of returned questionaires

(99 questions, most binary or categorical; ≈100.000 lines)

lots of columns

many lines

binary attribute,values just “0” or “1”



Project: Visualisation of Sensor Data from Animal Movement

Edward GrundyMark W. Jones

Robert S. LarameeRory P. Wilson

Emily L.C. Shepard

Visual Computing Group

Institute of Environmental Sustainability



Background

...to animals in the wild...

To gather data on:• acceleration,• temperature,• pressure,• etc

Biologists at Swanseahave attached sensors...

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Old Visualization Technique

2D line plots of the acceleration data is difficult to interpret,

• Large time domain makes relating different periods difficult• Three channels (possibly more) need to be correlated mentally by user• Relating intensity plots to orientation or movement is difficult

walking washing flying flying flyingdiving



BackgroundUnderstanding animal behaviour helps us to understand the environment, but animals are difficult to follow (for good reason).

We can follow them easily in some places...

Or not at all...

With more difficulty in others...

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Objectives

Given accelerometry data, it is useful to:

a) identify extraordinary events,

a) identify similarity,

a) relate to other attributes to form hypotheses

a) identify commonly adopted postures

While reducing cognitive effort required by line plots.



Acceleration data can be correlated with other attributes.

Visualisation of Multiple Attributes

• Replacing the magnitude of acceleration vector with pressure attribute improves readability of visualisation.

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Visualisation now achieves objective (c); relating accelerometry to other attributes to form a hypothesis for behaviour.



Visualisation of DistributionFrequently adopted orientations indicate

common behaviours. These can be observed with a histogram

over sphere; achieving objective (d)(identifying commonly

adopted postures).

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Visualisation of Data Distribution

This can be used to decide whether a point is “in-posture” or not.

Allowing us to quantify the tortuosity of the transition between postures, and visualise this in a graph.

Density based c-means clustering of the accelerometry produces a degree of membership value.



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Domain Expert ReviewScatterplot and overlay are valuable tools for both exploration and communication of results.Open research problem in marine biology: “Why don’t diving birds get decompression sickness (the bends)?”

Visual correlation of:

• Acceleration• Pressure

Resulted in hypothesis:“Diving birds slow their ascent toprevent nitrogen bubbles buildingup in their blood.”

strata in ascent

absent during descent



State Transition DiagramsData clustering gives further insight into postures and energy expenditure.

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Posture spheres present new interaction possibilities for statistical analysis.



Results: Animal Tracking Video



Summary

Driven by / driving the strongest human sense

Lots of different application fields Serves different purposes

Exploration, analysis, presentation

Depends on data characteristics, e.g., dimensionality data model (continuous vs. discrete)

Rapidly developing research field



Acknowledgements Thank you for your

attention! Any Questions?Thanks to the following people:

Helmut Doleisch, Christoph Garth, Edward Grundy, Markus Hadwiger, Helwig Hauser, Mark W Jones, Robert Kosara, Lukas Mroz, Juergen Schneider, Emily Shepard, Xavier Tricoche, Rory P Wilson



Visualization Application: Virtual Reality

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