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

2 SDMIV What is This Thing Called Visualization? n 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

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

4 SDMIV Scientific Visualization – Another Characterisation n Focus is on visualizing an entity measured in a multi-dimensional space – 1D – 2D – 3D – Occasionally nD n Underlying field is recreated from the sampled data n Relationship between variables well understood – some independent, some dependent Image from D. Bartz and M. Meissner

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

6 SDMIV Extending the SciVis Model n The dataflow model has proved extremely flexible n Provides basis of collaborative visualization – Implemented in IRIS Explorer as the COVISA toolkit n Extensible – User code introduced as module in pipeline allows computational steering visualizemodeldatarender internet collaborative server render simulatevisualize rendercontrol

7 SDMIV An e-Science Demonstrator n 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

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

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

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

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

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

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

14 SDMIV Scatter Plot Matrices n Matrix of 2D scatter plots – Each plot shows projection of data onto a 2D subspace of the variates – Order M 2 plots

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

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

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

18 SDMIV Hierarchical Parallel Co- ordinates

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

20 SDMIV Using a Dataflow System for Information Visualization n 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… n More later.. Kraus & Ertl

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