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:
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
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
3 17 April 2007 vizNET-LEEDS-PRES Images, animation Visualization Reality Data ObservationSimulation Data Visualization – What is it?
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+ :
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
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
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
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
9 17 April 2007 vizNET-LEEDS-PRES The Humble Graph
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
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?
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 ?
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
14 17 April 2007 vizNET-LEEDS-PRES Dot Graph Very simple but effective… Horizontal to give more space for labelling
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
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
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
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….
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
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!
21 17 April 2007 vizNET-LEEDS-PRES Exercise How much can be removed from this graphic? 1 2 3 4 5 6 Answer at:
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?
23 17 April 2007 vizNET-LEEDS-PRES Lie Factor Lie Factor = (Size of effect on graph) / (Size of effect on data) Spot the lie!
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
25 17 April 2007 vizNET-LEEDS-PRES Scientific Visualization Data defined over 2D regions and 3D volumes
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
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??
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
29 17 April 2007 vizNET-LEEDS-PRES Lobster – Increasing the Threshold Level From University of Bonn
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
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
32 17 April 2007 vizNET-LEEDS-PRES Example – medical application Vertebrae… .. Also from UC Davis
33 17 April 2007 vizNET-LEEDS-PRES Example – Heart Modelling
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?
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
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
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
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
41 17 April 2007 vizNET-LEEDS-PRES Isosurface and Volume Rendering Storm cloud data rendered by IRIS Explorer – Isosurface & volume rendering
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