Download presentation

Presentation is loading. Please wait.

Published byJenna Dempsey Modified over 3 years ago

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

2
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
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
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 http://pacific.commerce.ubc.ca/xr/plot.html Image from D. Bartz and M. Meissner

5
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
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
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
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
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
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 1.. 2 variables observations

11
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
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
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
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
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
16 SDMIV Brushing as a Solution n Brushing selects a restricted range of one or more variables n Selection then highlighted

17
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
18 SDMIV Hierarchical Parallel Co- ordinates

19
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
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
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

Similar presentations

Presentation is loading. Please wait....

OK

Addition 1’s to 20.

Addition 1’s to 20.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on air conditioning auditorium Ppt on difference between product and service marketing Ppt on review of related literature on research Ppt on chromosomes and genes activities Ppt on series and parallel circuits practice Ppt on child labour in india free download Ppt on network theory books Ppt on life history of william shakespeare Ppt on pathophysiology of obesity Ppt on different solid figures games