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1 17 April 2007 vizNET-LEEDS-PRES-0001-070417 A Rough Guide to Data Visualization VizNET 2007 Annual Event Ken Brodlie School of Computing University.

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Presentation on theme: "1 17 April 2007 vizNET-LEEDS-PRES-0001-070417 A Rough Guide to Data Visualization VizNET 2007 Annual Event Ken Brodlie School of Computing University."— Presentation transcript:

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2 1 17 April 2007 vizNET-LEEDS-PRES A Rough Guide to Data Visualization VizNET 2007 Annual Event Ken Brodlie School of Computing University of Leeds

3 2 17 April 2007 vizNET-LEEDS-PRES  Visualization now seen as key part of modern computing  High performance computing generates vast quantities of data...  High resolution measurement technology likewise...  microscopes, scanners, satellites  Information systems involve not only large data sets but also complex connections... ... we need to harness our visual senses to help us understand the data Data Visualization

4 3 17 April 2007 vizNET-LEEDS-PRES Images, animation Visualization Reality Data ObservationSimulation Data Visualization – What is it?

5 4 17 April 2007 vizNET-LEEDS-PRES Applications - Meteorology Pressure at levels in atmosphere - illustrated by contour lines in a slice plane Generated by the Vis5D system from University of Wisconsin (now Vis5d+) Vis5d: Vis5d+ :

6 5 17 April 2007 vizNET-LEEDS-PRES Applications - Medicine From scanner data, we can visualize 3D pictures of human anatomy, using volume rendering Generated by Anatomy.TV used by Leeds medical students to learn anatomy

7 6 17 April 2007 vizNET-LEEDS-PRES Applications – Computational Fluid Dynamics  Interface between immiscible fluids  e.g. oil / water  Loops and fingers arise when mixing starts  Rayleigh-Taylor instability  Simulated on ASCII Blue Pacific (Cook & Dimotakis, 2001)  Interface visualized using a density isosurface

8 7 17 April 2007 vizNET-LEEDS-PRES Applications – Hierarchical Information Usenet news groups For history of treemaps see: hcil/treemap-history Developed over many years by Ben Schneiderman and colleagues

9 8 17 April 2007 vizNET-LEEDS-PRES Structure of Session Part 1  Introduction  What is visualization and some examples  The humble graph  Much to learn  Scientific visualization  Understanding 2D and 3D data Part 2  Exploratory data visualization  Finding relationships in tables of data  Visualizing structures  Information hierarchies  Interacting with visualizations  Focus and context

10 9 17 April 2007 vizNET-LEEDS-PRES The Humble Graph

11 10 17 April 2007 vizNET-LEEDS-PRES The First Visualization This picture is taken from Brian Collins ‘Data Visualization - Has it all been seen before?’ in ‘Animation and Scientific Visualization’, Academic Press

12 11 17 April 2007 vizNET-LEEDS-PRES Simple Data Presentation  Simple data tables are often presented as line graphs, bar graphs, pie charts, dot graphs, histograms…  Which should we use and when?

13 12 17 April 2007 vizNET-LEEDS-PRES Line Graph  Fundamental technique of data presentation  Used to compare two continuous variables  X-axis is often the control variable  Y-axis is the response variable  Good at:  Predicting values where data not given  Often (dubiously) used for trends when control is a categorical variable Students participating in sporting activities ?

14 13 17 April 2007 vizNET-LEEDS-PRES Simple Representations – Bar Graph  Bar graph  Presents categorical variables  Height of bar indicates value  Double bar graph allows comparison  Note spacing between bars  Can be horizontal (when would you use this?) Internet use at a school Number of police officers

15 14 17 April 2007 vizNET-LEEDS-PRES Dot Graph  Very simple but effective…  Horizontal to give more space for labelling

16 15 17 April 2007 vizNET-LEEDS-PRES Pie Chart  Pie chart summarises a set of categorical/nominal data  Shows proportions  But use with care…  … too many segments are harder to compare than in a bar chart Should we have a long lecture? Favourite movie genres

17 16 17 April 2007 vizNET-LEEDS-PRES Histograms  Histograms summarise discrete or continuous data that are measured on an interval scale  No gaps if variable is continuous Distribution of salaries in a company

18 17 17 April 2007 vizNET-LEEDS-PRES Scatter Plot  Used to present measurements of two variables  Effective if a relationship exists between the two variables Car ownership by household income Example taken from NIST Handbook – Evidence of strong positive correlation

19 18 17 April 2007 vizNET-LEEDS-PRES A Visualization Guru  Edward Tufte has written a series of books on the design of good visualizations  Visit:  Here are some of the things he teaches us….

20 19 17 April 2007 vizNET-LEEDS-PRES Tufte Design Principles  “Give the viewer the greatest number of ideas in the shortest space of time using the least ink in the smallest space”  Try to maximize the data-ink ratio  Show data variation, not design variation  Tell the truth about the data

21 20 17 April 2007 vizNET-LEEDS-PRES Data Ink  Data Ink Ratio = (data-ink) / (total ink to produce graphic) = proportion of ink devoted to non- redundant display of information = 1.0 – proportion of graphic that can be deleted without loss of data-information A low value of data ink ratio!

22 21 17 April 2007 vizNET-LEEDS-PRES Exercise  How much can be removed from this graphic?  1  2  3  4  5  6 Answer at:

23 22 17 April 2007 vizNET-LEEDS-PRES Design Variation  Fundamental purpose of a graph is to show changes in the data  Design variation – where the same data is displayed differently for decoration - is to be avoided  Leads to ambiguity and deception What is wrong with this?

24 23 17 April 2007 vizNET-LEEDS-PRES Lie Factor  Lie Factor = (Size of effect on graph) / (Size of effect on data) Spot the lie!

25 24 17 April 2007 vizNET-LEEDS-PRES Summary  Use the correct type of graph  Line graph for response against continuous control  Bar chart when control is categorical  Pie chart when viewing as proportions  Histograms when aggregating over intervals  Scatter plots to see relationships between two variables  Remember Tufte’s principles when creating a graphic  Thanks to Statistics Canada – an excellent web site for simple data presentation 

26 25 17 April 2007 vizNET-LEEDS-PRES Scientific Visualization Data defined over 2D regions and 3D volumes

27 26 17 April 2007 vizNET-LEEDS-PRES Data over 2D Region - Contouring  In contouring we are extracting lines of constant ‘height’ from data defined over a 2D region… sometimes called isolines  What is the analogy for data defined over a 3D volume? Topographic map with isohypses of height -wikipedia

28 27 17 April 2007 vizNET-LEEDS-PRES Isosurfacing  The analogy for 3D data is the isosurface: points where the measurements have a constant value…  Here we see surface of brain extracted from a 3D medical dataset  What limitations do you notice compared with contours in 2D??

29 28 17 April 2007 vizNET-LEEDS-PRES Marching Cubes  Famous isosurfacing algorithm is marching cubes  Each cube processed in turn  For zero isosurface, create surface separating positive and negative vertices of cube  After each cube is processed we have a surface (or surfaces) separating all positive vertices from all negative ones

30 29 17 April 2007 vizNET-LEEDS-PRES Lobster – Increasing the Threshold Level From University of Bonn

31 30 17 April 2007 vizNET-LEEDS-PRES  Advantages  isosurfaces good for extracting boundary layers  surface defined as triangles in 3D - well-known rendering techniques available for lighting, shading and viewing... with hardware support  Disadvantages  shows only a slice of data Isosurfacing by Marching Cubes Algorithm

32 31 17 April 2007 vizNET-LEEDS-PRES Example – mechanical engineering  Isosurfacing can be applied to rendering of objects… here an engine Computer Science, UC Davis

33 32 17 April 2007 vizNET-LEEDS-PRES Example – medical application  Vertebrae… .. Also from UC Davis

34 33 17 April 2007 vizNET-LEEDS-PRES Example – Heart Modelling

35 34 17 April 2007 vizNET-LEEDS-PRES Image Presentation of Data over 2D Region Note here that in addition to the contour lines the height of each ‘dot’ is individually coloured – so there is a mapping from ‘height’ to colour … this is known as a transfer function. What is the analogy in 3D?

36 35 17 April 2007 vizNET-LEEDS-PRES  The analogy in 3D is known as volume rendering  To overcome the step to 3D, we transfer values to colour and opacity  Volume is a partially opaque gel material  By controlling the opacity, we can:  EITHER show surfaces through setting opacity to 0 everywhere except at a specific value where it is set to 1  OR see both exterior and interior regions by grading the opacity from 0 to 1 [Note: opacity = 1 - transparency] Volume Rendering

37 36 17 April 2007 vizNET-LEEDS-PRES Data Classification – Assigning Opacity to CT data  CT will identify fat, soft tissue and bone  Each will have known absorption levels, say f fat, f soft_tissue, f bone CT value Opacity  f soft_tissue 0 1 This transfer function will highlight soft tissue

38 37 17 April 2007 vizNET-LEEDS-PRES Data Classification – Assigning Opacity to CT Data  To show all types of tissue, we assign opacities to each type and linearly interpolate between them CT value Opacity  f soft_tissue 0 1 f fat f bone In practice, a is also increased in areas where data changes rapidly – This accentuates boundaries

39 38 17 April 2007 vizNET-LEEDS-PRES  Colour classification is done similarly white red yellow Air Fat Soft Tissue Bone CT number Known as colour transfer function Data Classification – Constructing the Gel – CT Data

40 39 17 April 2007 vizNET-LEEDS-PRES Volume Rendering Cerebral aneurysm Marcelo Cohen

41 40 17 April 2007 vizNET-LEEDS-PRES Volume Rendering Tooth, engine, woman – Marcelo Cohen

42 41 17 April 2007 vizNET-LEEDS-PRES Isosurface and Volume Rendering Storm cloud data rendered by IRIS Explorer – Isosurface & volume rendering

43 42 17 April 2007 vizNET-LEEDS-PRES Summary  Scientific visualization allows us to understand data defined over 2D and 3D regions  Traditional 2D methods have been generalised to 3D:  Contouring – isosurfacing  Image representation – volume rendering  Excellent new text book  Helen Wright  Introduction to Scientific Visualization – Springer Verlag


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