A Rough Guide to Data Visualization VizNET 2007 Annual Event Ken Brodlie School of Computing University of Leeds Data Visualization Visualization now seen as key part of modern computing High performance computing generates vast quantities of data ...
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A Rough Guide to Data Visualization
VizNET 2007 Annual Event
School of Computing
University of Leeds
Pressure at levels
- illustrated by
contour lines in a
the Vis5D system
from University of
Vis5d+ : http://vis5d.sourceforge.net
From scanner data, we can
visualize 3D pictures
of human anatomy, using
Generated by Anatomy.TV
used by Leeds medical students
to learn anatomy
Interface between immiscible fluids
e.g. oil / water
Loops and fingers arise when mixing starts
Simulated on ASCII Blue Pacific (Cook & Dimotakis, 2001)
Interface visualized using a density isosurface
For history of
Developed over many years by Ben Schneiderman and colleagues
The Humble Graph
This picture is taken from Brian Collins
‘Data Visualization - Has it all been seen before?’
in ‘Animation and Scientific Visualization’, Academic Press
Simple data tables are often presented as line graphs, bar graphs, pie charts, dot graphs, histograms…
Which should we use and when?
Fundamental technique of data presentation
Used to compare two continuous variables
X-axis is often the control variable
Y-axis is the response variable
Predicting values where data not given
Often (dubiously) used for trends when control is a categorical variable
Students participating in sporting activities
Presents categorical variables
Height of bar indicates value
Double bar graph allows comparison
Note spacing between bars
Can be horizontal (when would you use this?)
Number of police officers
Internet use at a school
Very simple but effective…
Horizontal to give more space for labelling
Pie chart summarises a set of categorical/nominal data
But use with care…
… too many segments are harder to compare than in a bar chart
Should we have a long lecture?
Favourite movie genres
Histograms summarise discrete or continuous data that are measured on an interval scale
No gaps if variable is continuous
Distribution of salaries
in a company
Used to present measurements of two variables
Effective if a relationship exists between the two variables
Example taken from
NIST Handbook –
Evidence of strong
Car ownership by household income
Edward Tufte has written a series of books on the design of good visualizations
Here are some of the things he teaches us….
Data Ink Ratio
= (data-ink) / (total ink to produce graphic)
= proportion of ink devoted to non-redundant display of information
= 1.0 – proportion of graphic that can be deleted without loss of data-information
A low value of data ink ratio!
How much can be removed from this graphic?
Fundamental purpose of a graph is to show changes in the data
Design variation – where the same data is displayed differently for decoration - is to be avoided
Leads to ambiguity and deception
What is wrong with this?
= (Size of effect on graph) / (Size of effect on data)
Spot the lie!
Data defined over 2D regions and 3D volumes
In contouring we are extracting lines of constant ‘height’ from data defined over a 2D region… sometimes called isolines
What is the analogy for data defined over a 3D volume?
Topographic map with isohypses
of height -wikipedia
The analogy for 3D data is the isosurface: points where the measurements have a constant value…
Here we see surface of brain extracted from a 3D medical dataset
What limitations do you notice compared with contours in 2D??
Famous isosurfacing algorithm is marching cubes
Each cube processed in turn
For zero isosurface, create surface separating positive and negative vertices of cube
After each cube is processed we have a surface (or surfaces) separating all positive vertices from all negative ones
From University of Bonn
Isosurfacing can be applied to rendering of objects… here an engine
Computer Science, UC Davis
.. Also from UC Davis
Note here that in addition to the contour lines the height
of each ‘dot’ is individually coloured – so there is a mapping
from ‘height’ to colour … this is known as a transfer function.
What is the analogy in 3D?
[Note: opacity = 1 - transparency]
This transferfunction will highlight soft tissue
In practice, a is
also increased in
areas where data
changes rapidly –
Known as colour transfer function
Tooth, engine, woman –
by IRIS Explorer –
Isosurface & volume