Data Visualization - A Very Rough Guide

1. Data Visualization- A Very Rough Guide Ken Brodlie University of Leeds

2. Visualization “Use of computer-supported, interactive, visual representations of data to amplify cognition” (Card, McKinlay, Shneiderman) Born as a discipline in 1987 with publication of NSF Report Now widely used in computational science and engineering Vis5D What is This Thing Called Visualization?

3. Scientific Visualization Visualization of physical data Information Visualization Visualization of abstract data Automobile web site - visualizing links Ozone layer around earth Visualization – Twin Subjects

4. Focus is on visualizing an entity measured in a multi-dimensional space 1D 2D 3D Occasionally nD Underlying field is recreated from the sampled data Relationship between variables well understood – some independent, some dependent http://pacific.commerce.ubc.ca/xr/plot.html Image from D. Bartz and M. Meissner Scientific Visualization – Another Characterisation

5. Visualization represented as pipeline: Read in data Build model of underlying entity Construct a visualization in terms of geometry Render geometry as image Realised as modular visualization environment IRIS Explorer IBM Open Visualization Data Explorer (DX) AVS data visualize model render Scientific Visualization Model

6. The dataflow model has proved extremely flexible Provides basis of collaborative visualization Implemented in IRIS Explorer as the COVISA toolkit Extensible User code introduced as module in pipeline allows computational steering data visualize model render render collaborative server internet simulate visualize control render Extending the SciVis Model

7. Emergency scenario: release of toxic chemical Simulation launched on Grid resource, steered from desktop using IRIS Explorer Collaborators linked in remotely using COVISA toolkit Dispersion of pollutant studied under varying wind directions A collaborator links in over the network An e-Science Demonstrator

8. Other user interface metaphors have been suggested Spreadsheet interface becoming popular.. Allows audit trail of visualizations Other Metaphors Jankun-Kelly and Ma

9. Focus is on visualizing set of observations that are multi-variate Example of iris data set 150 observations of 4 variables (length, width of petal and sepal) Techniques aim to display relationships between variables Information Visualization

10. Again we can express as a dataflow – but emphasis now is on data itself rather than underlying entity First step is to form the data into a table of observations, each observation being a set of values of the variables Then we apply a visualization technique as before data visualize render variables observations Dataflow for Information Visualization data table

11. Software: Xmdvtool Matthew Ward Acknowledgement: Many of images in following slides taken from Ward’s work Multivariate Visualization • Techniques designed for any number of variables • Glyph techniques • Parallel co-ordinates • Scatter plot matrices • Pixel-based techniques ..and also IRIS Explorer!

12. Star plots Each observation represented as a ‘star’ Each spike represents a variable Length of spike indicates the value Variety of possible glyphs Chernoff faces Crime in Detroit Glyph Techniques

13. Each variate represented as vertical axis Axes laid out uniformly Observation represented as a polyline traversing all M axes, crossing each axis at the observed value of the variate Parallel Co-ordinates Detroit homicide data (7 variables,13 observations)

14. Matrix of 2D scatter plots Each plot shows projection of data onto a 2D subspace of the variates Order M2 plots Scatter Plot Matrices

15. All techniques, sooner or later, run out of screen space Parallel co-ordinates Usable for up to 150 variates Unworkable greater than 250 variates Remote sensing: 5 variates, 16,384 observations) The Screen Space Problem

16. Brushing selects a restricted range of one or more variables Selection then highlighted Brushing as a Solution

17. Success has been achieved through clustering of observations Hierarchical parallel co-ordinates Cluster by similarity Display using translucency and proximity-based colour Clustering as a Solution

18. Hierarchical Parallel Co-ordinates

19. Reduce number of variables, preserve information Principal Component Analysis Transform to new co-ordinate system Hard to interpret Hierarchical reduction of variate space Cluster variables where distance between observations is typically small Choose representative for each cluster Reduction of Dimensionality of Variate Space

20. IRIS Explorer used to visualize data from BMW Five variables displayed using spatial arrangement for three, colour and object type for others Notice the clusters… More later.. Using a Dataflow System for Information Visualization Kraus & Ertl

21. Focus is on visualizing set of observations that are multi-variate There is no underlying field – it is the data itself we want to visualize The relationship between variables is not well understood Scientific Visualization – Information Visualization Scientific Visualization Information Visualization • Focus is on visualizing an entity measured in a multi-dimensional space • Underlying field is recreated from the sampled data • Relationship between variables well understood