Data Visualization

1
Data Visualization

• MSc Module
• School of Computing
• Ken Brodlie
• Semester 1 2004-2005
• Lecture 1 - Introduction

2
Visualization

• Visualization now seen as key part of modern
  computing
• High performance computing generates vast
  quantities of data ...
• High resolution measurement technology likewise
  ...
• microscopes, scanners, satellites
• Information systems involve not only large data
  sets but also complex connections...
• ... we need to harness our visual senses to help
  us understand the data

3
Getting Started

• What is Visualization? - a definition
• Where is it useful? - some applications
• What is the history?
• What tools are now available?
• How are we going to study it?
• MSc in Distributed Multimedia Systems
• MSc in Computational Fluid Dynamics

4
Data Visualization Scientific Vis
Information Vis

• Scientific Visualization
• Numerical data from science, engineering and
  medicine

• Information Visualization
• Numeric and non-numeric data

5
Scientific Visualization - What is it?
6
Applications - Meteorology
Pressure at levels in atmosphere - illustrated by
contour lines in a slice plane
Generated by the Vis5D system from University
of Wisconsin (now Vis5d)
Vis5d http//www.ssec.wisc.edu/billh/vis5d.html
Vis5d http//vis5d.sourceforge.net
7
Applications - Medicine
From scanner data, we can visualize 3D
pictures of human anatomy, using volume rendering
Generated by VOXELman software from University of
Hamburg
www.uke.uni-hamburg.de/institute/imdm/idv/index.en
.html
8
Applications Climate Prediction

• Simulation of 21st century climate evolution
• Real-time display of results
• temperature, cloud, precipitation, etc
• Massive ensemble of runs distributed
  public-resource computing project
• see www.climateprediction.net to participate!

9
Applications Computational Fluid Dynamics

• Flow of air around a car
• Vectors and particle paths illustrate flow
• Coloured slice indicates pressure

10
Applications Computational Fluid Dynamics

• Interface between immiscible fluids
• e.g. oil / water
• Loops and fingers arise when mixing starts
• Rayleigh-Taylor instability
• Simulated on ASCII Blue Pacific (Cook
  Dimotakis, 2001)
• Interface visualized using a density isosurface

11
Applications - Molecular Modelling

• 2D potential energy function
• molecule inside a zeolite channel
• Displayed as coloured surface (left)
• part also displayed using contour plot (right)

12
Applications - Molecular Modelling

• 3D potential energy function
• three atoms in a box
• Displayed as isosurface (left)
• interactive probe also shows how potential varies
  between two points (right)

13
Visualization BC

• Imagination or visualization, and in particular
  the use of diagrams, has a crucial part to play
  in scientific investigation.
• Rene Descartes, 1637
• There are many examples of the use of
  visualization Before Computers (BC)
• graph plots in 10th century
• business graphics in 18th century (Playfair)
• contour plots in 18th century (Halley)

14
The First Visualization
This and following two pictures are taken from
Brian Collins Data Visualization - Has it all
been seen before? in Animation and Scientific
Visualization, Academic Press
15
The First Business Graphics
16
The First Contour Map
17
Visual Thinkers

• Many of the great scientists were good at visual
  thinking
• Leonardo da Vinci
• James Clerk Maxwell
• Michael Faraday
• Albert Einstein
• This was often at the expense of verbal skills
• Tom West In the Minds Eye
• See also http//www.krasnow.gmu.edu/twest/maxwell_
  visual.html

Maxwells clay model now in New Cavendish
Laboratory, Cambridge (picture by Tom West)
18
Early Computer Visualization

• From early days of computing, scientists have
  carried out numerical simulation - and looked to
  visualization to help understand the results.
• Visualization systems have evolved in four
  different styles - all still in use today (so not
  really history!)

19
Subprogram Libraries

• 1960 onwards
• Libraries of subprograms to draw graphs, contour
  plots
• Scientists include calls to library routines from
  within their own code
• Leading examples from 1970-1985 era were
• GHOST (UKAEA Culham)
• NAG Graphics Library

NAG Graphics www.nag.co.uk
20
Subprogram Libraries

• This style continues today
• NAG Graphics Library still available
• Vtk C classes provide modern version of this
  style
• Great flexibility but need to program
• Application Programming Interface

Vtk www.visualizationtoolkit.org
21
Interactive Packages

• From late 1970 onwards
• Menu-driven packages allowing data to be
  visualized without need to write programs
• Example
• gnuplot
• www.gnuplot.info
• Less flexible, but no programming!

gnuplot
22
Interactive Packages

• Matlab is a powerful system for computation and
  visualization
• Has its own C-like language
• www.mathworks.com

23
Visualization Today

• Recent surge of interest in visualization was
  sparked by an NSF report
• Visualization in Scientific Computing
• McCormick, de Fanti and Brown - 1987
• Argued that investment in high performance
  computing in US was wasted unless there was
  corresponding investment in visualization
• This motivated a third style of visualization
  system...

24
Visual Programming Systems

• From late 1980s onwards
• Visualization seen as a sequence of simple
  processing steps eg contouring
• read in data
• create contour lines
• draw contour lines
• Systems provide modules implementing simple steps
  in a visualization pipeline
• Scientist uses visual programming to connect
  modules together

25
Visual Programming - IRIS Explorer
26
Visual Programming Systems

• Visual programming allows easy experimentation
  which is what one needs in visualization
• Examples are
• IRIS Explorer
• www.nag.co.uk
• AVS
• www.avs.com
• OpenDX (grown from IBM Visualization Data
  Explorer)
• www.opendx.org

27
Service-based Visualization

• The Internet era has introduced a fourth style of
  system where a visualization service is
  delivered over the internet using Web
  technologies
• Client-side with Java applets.

www.sdsc.edu/vizwiz
28
Service-based Visualization

• or server side
• Here a form on a web page is used to make a
  visualization request
• Processed by a visualization system on server and
  returned to client as VRML

IRIS Explorer SerVis www.visualization.leeds.ac.uk
/aqual
29
The Four Phases of Visualization Systems

• These four phases correlate with four phases in
  computing generally
• Subprogram libraries
• begun in era of batch computing
• Interactive packages
• begun in era of interactive computing, with
  terminals connected to host
• Visual programming systems
• begun in era of workstation computing, with
  graphical user interfaces
• Service-based visualization
• begun in era of internet computing

30
Information Visualization

• Information Visualization
• Has emerged over last decade
• Building on success of scientific visualization
• Driven by the escalating volumes of data fuelled
  by the new technologies (eg supermarket
  checkouts!) and the accessibility of data via the
  Internet
• Characterised by large quantities of data not
  necessarily numbers and search for
  relationships amongst the data
• but no absolute dividing line between SciVis
  and InfoVis

31
Outline of the Course

• Lectures
• Monday 10 (Parkinson-B9) Friday 9 (LT11)
• Practical sessions using gnuplot, IRIS Explorer
  and xmdvtool under Linux
• Background study

32
Outline of Lecture CourseData Visualization - I

• Introduction and historical view
• Fundamental concepts
• Scientific Visualization techniques
• Scalar data - one value at a point
• 1D - graphs, ..
• 2D - contour maps, ..
• 3D - isosurfaces, volume rendering
• Vector data - many related values at a point
• velocity values flow visualization

33
Outline of Lecture CourseData Visualization - II

• Publication of visualization
• VRML for 3D web presentation
• Visualization Systems
• Computational steering
• linking simulation and visualization
• Grid computing and visualization
• Collaborative Visualization
• Group working on the Internet
• this will complete the programme for CFD
  students
• but DMS students continue

34
Outline of Lecture Course Data Visualization -
III

• Web-based visualization
• using the Web as a distributed computing
  environment
• Information Visualization
• how to interpret large quantities of data using
  visualization
• multivariate data

35
Practical Work

• For DMS and CFD students - use of IRIS Explorer
• state of art visualization system
• Linux pcs
• practical sessions
• For DMS students xmdvtool (multivariate data)
• Publication using the World Wide Web
• Assessment
• assignments to visualize datasets
• Experience of other systems
• gnuplot

36
Background Study

• Reading
• mainly recent papers
• World Wide Web
• IRIS Explorer training materials
• generally ... a source of up-to-date information
  and examples

37
Books

• The Visualization Toolkit (3rd edition)
• W Shroeder, K Martin, W Lorensen Kitware Inc
• Introduction to Volume Rendering
• B. Lichtenbelt et al - Prentice Hall (1998)
• Information Visualization
• R. Spence Addison-Wesley (2001)
• Scientific Visualization Tech Applns
• K W Brodlie et al
• Springer Verlag (1992)

38
Objectives

• To be aware of the value of visualization to gain
  insight into both numeric data (from science,
  engineering and medicine for example)
• and also non-numeric information (such as
  networks and documents)
• To understand the fundamental techniques for data
  visualization
• To be skilled in the use of a state of art
  visualization system

39
Keeping in Touch

• E-mail
• kwb_at_comp.leeds.ac.uk
• Newsgroup for my postings
• local.modules.vis
• Newsgroup for your postings
• local.modules.vis.talk
• World Wide Web
• http//www.comp.leeds.ac.uk/kwb/