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2.1 Vis_04 Data Visualization Lecture 2 Fundamental Concepts - Reference Model Visualization Techniques – Overview Visualization Systems - Overview

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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. - PowerPoint PPT Presentation

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Page 1: Data Visualization

2.1Vis_04

Data Visualization

Lecture 2Fundamental Concepts - Reference

ModelVisualization Techniques – Overview

Visualization Systems - Overview

Page 2: Data Visualization

2.2Vis_04

A Simple Example

TIME (mins)

OXYGEN (%)

0 2 4 10 28 30 32

20.8 8.8 4.2 0.5 3.9 6.2 9.6

This table shows the observed oxygen levels inthe flue gas, when coal undergoes combustionin a furnace

Page 3: Data Visualization

2.3Vis_04

Visualizing the Data - but is this what we want to

see?

Page 4: Data Visualization

2.4Vis_04

Estimating behaviour between the data - but is

this believable?

Page 5: Data Visualization

2.5Vis_04

Now it looks believable… but something is wrong

Page 6: Data Visualization

2.6Vis_04

At least this is credible..

Page 7: Data Visualization

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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

Page 8: Data Visualization

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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

Page 9: Data Visualization

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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

Page 10: Data Visualization

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Back to the Simple Example

Data

Enrich

Map

Render

Interpolate to create model

Select a line graph as techniqueand create line segments fromenriched data

Draw line segments on display insuitable colour, line style and width

Select Extract part of data we are interested in

Page 11: Data Visualization

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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.

Page 12: Data Visualization

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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

Page 13: Data Visualization

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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)

Page 14: Data Visualization

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A Simple Notation

This leads to a simple classification of data as:

EnS/V

So the simple example is of type:E1

S

Flow within a volume can be classed as:

E3V3

Page 15: Data Visualization

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Exercise

Can you give suitable techniques for the following classes:

ES1

ES2

ES3

EV33

Page 16: Data Visualization

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Overview of Visualization Techniques

Using the classification to organise the various visualization

techniques

Page 17: Data Visualization

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ES1

The humble graph!

How can we represent errors in the data?

A nice example of web-basedvisualization….

http://fx.sauder.ubc.ca/plot.html

Page 18: Data Visualization

2.18Vis_04

ES2

Here we see a contour map of wind speed over the USA (28-Sep-04)

What can you observe?

Can you use an ES

1 technique for this sort of data?

http://weather.unisys.com/surface/

Page 19: Data Visualization

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ES3

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

Image from D. Bartz and M. Meissner

Page 20: Data Visualization

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ES3

Corresponding to contours for ES

2, we can generate isosurfaces

What are the limitations of this approach compared with volume rendering? Image from D. Bartz and M. Meissner

Page 21: Data Visualization

2.21Vis_04

EV22

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

Page 22: Data Visualization

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EV33

This is flow in a volume

Arrows become extremely cluttered

Here we are tracing the path of a particle through the volume

Page 23: Data Visualization

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Visualization Systems

Showing how the map and render steps are realised in a visualization system

Page 24: Data Visualization

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IRIS Explorer