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Data Visualization

Data Visualization. Lecture 2 Fundamental Concepts - Reference Model Visualization Techniques – Overview Visualization Systems - Overview. A Simple Example. This table shows the observed oxygen levels in the flue gas, when coal undergoes combustion in a furnace. TIME (mins). 0. 2. 4.

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Data Visualization

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  1. Data Visualization Lecture 2 Fundamental Concepts - Reference Model Visualization Techniques – Overview Visualization Systems - Overview

  2. A Simple Example This table shows the observed oxygen levels in the flue gas, when coal undergoes combustion in a furnace TIME (mins) 0 2 4 10 28 30 32 20.8 8.8 4.2 0.5 3.9 6.2 9.6 OXYGEN (%)

  3. Visualizing the Data - but is this what we want to see?

  4. Estimating behaviour between the data - but is this believable?

  5. Now it looks believable… but something is wrong

  6. At least this is credible..

  7. What have we learnt? • It is not only the data that we wish to visualize - it is also the bits inbetween! • The data are samples from some underlying ‘field’ which we wish to understand • First step is to create from the data a ‘best’ estimate of the underlying field - we shall call this a MODEL • This needs to be done with care and may need guidance from the scientist

  8. Data Enrichment • This process is sometimes called ‘data enrichment’ or ‘enhancement’ • If data is sparse, but accurate, we INTERPOLATE to get sufficient data to create a meaningful representation of our model • If sparse, but in error, we may need to APPROXIMATE

  9. The Visualization Process • Overall the Visualization Process can be divided into four logical operations: • DATA SELECTION: choose the portion of data we want to analyse • DATA ENRICHMENT: interpolating, or approximating raw data - effectively creating a model • MAPPING: conversion of data into a geometric representation • RENDERING: assigning visual properties to the geometrical objects (eg colour, texture) and creating an image

  10. Enrich Select Map Render Back to the Simple Example Data Extract part of data we are interested in Interpolate to create model Select a line graph as technique and create line segments from enriched data Draw line segments on display in suitable colour, line style and width

  11. Classification of Mapping Techniques • The mapping stage is where we decide which visualization technique to apply to our ‘enriched’ data • There are a bewildering range of these techniques - how do we know which to choose? • First step is to classify the data into sets and associate different techniques with different sets.

  12. Back to the Simple Example • The underlying field is a function F(x) • F represents the oxygen level and is the DEPENDENT variable • x represents the time and is the INDEPENDENT variable • It is a one dimensional scalar field because • the independent variable x is 1D • the dependent variable F is a scalar value

  13. General Classification Scheme • The underlying field can be regarded as a function of many variables: say F(x) where F and x are both vectors: F = (F1, F2, ... Fm) x = (x1, x2, ... xn) • The dimension is n • The dependent variable can be scalar (m=1) or vector (m>1)

  14. A Simple Notation • This leads to a simple classification of data as: EnS/V • So the simple example is of type: E1S • Flow within a volume can be classed as: E3V3

  15. Exercise • Can you give suitable techniques for the following classes: • ES1 • ES2 • ES3 • EV33

  16. Overview of Visualization Techniques Using the classification to organise the various visualization techniques

  17. ES1 A nice example of web-based visualization…. • The humble graph! • How can we represent errors in the data? http://fx.sauder.ubc.ca/plot.html

  18. ES2 http://weather.unisys.com/surface/ • Here we see a contour map of wind speed over the USA (28-Sep-04) • What can you observe? • Can you use an ES1 technique for this sort of data?

  19. As dimension increases, it becomes harder to visualize on a 2D surface Here we see a lobster within resin.. where the resin is represented as semi-transparent Technique known as volume rendering ES3 Image from D. Bartz and M. Meissner

  20. Corresponding to contours for ES2, we can generate isosurfaces What are the limitations of this approach compared with volume rendering? ES3 Image from D. Bartz and M. Meissner

  21. This is a flow field in two dimensions Simple technique is to use arrows.. What are the strengths and weaknesses of this approach? During the module, we will discover better techniques for this EV22

  22. This is flow in a volume Arrows become extremely cluttered Here we are tracing the path of a particle through the volume EV33

  23. Visualization Systems Showing how the map and render steps are realised in a visualization system

  24. IRIS Explorer

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