mao lin huang university of technology, sydney,

Mao Lin Huang

University of Technology, Sydney,

Visual Representations of Data and Knowledge

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Rendering Effective Route Maps

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General Idea Automatically generate a route map that has

the same properties as a hand drawn map. Hand drawn maps:

Exaggerated Lengths (non-constant scale factor)

No irrelevant information

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More Specifically Constant scale factor

Road lengths on a conventional map vary in several orders of magnitude => small roads and neighborhoods are hard to navigate with large maps

Information irrelevant to navigation Names of locations, places, cities, etc. that are all far

away from the route Takes up space that would be otherwise useful for

showing crossroads and relevant landmarks

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Generalization Techniques Generalize Length

Use more space for short roads, less for longer ones. Distribute based on importance, not physical length

Generalize Angle Align roads or make room for others

Generalize Shape Navigator doesn’t need to know roads shape. Simpler roads are easier to differentiate on a map.

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Demo at mapblast.com

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Simple Visualization Model

Data View PortVisual Mapping

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Film Data Table Example: Attributes

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Visual Mapping Define a Space Map: data marks Map: data attributes graphical mark attributes

Year X Length Y Popularity size Subject color Award? shape

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Example: FilmFinder

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Example: FilmFinder

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Use of graphical time scales as an approach to visualize histories. [Time Scale + History = Intuitive]

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Patient Records

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Galaxies Projection of clustering algorithms into 2D Galaxies are clusters of related data Proximity of galaxies is relevant Designed to add temporal patterns to

clustering

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Galaxies

3D Visualization & VR Techniques

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3D Cone Tree

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3D Cone Trees

research.microsoft.com/~ggr/gi97.ppt 17

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Perspective Wall

research.microsoft.com/~ggr/gi97.ppt 18

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Example: 3D-Room (The Exploratory)

Robertson, Card, and Mackinlay (1989) 20

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3D Navigation Task (Hallway)

research.microsoft.com/~ggr/gi97.ppt 21

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3D GUI for Web Browsing

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3D GUI for Web Browsing

http://research.microsoft.com/ui/TaskGallery/index.htm 23

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Web Forager

http://research.microsoft.com/ui/TaskGallery/index.htm 24

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WebBook

research.microsoft.com/~ggr/gi97.ppt 25

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3D GUI for Desktop

http://research.microsoft.com/ui/TaskGallery/index.htm 26

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ThemeScape Abstract 3D landscape of information Reduce cognitive load using terrain Elevation, colour encode theme strength

redundantly Landscape metaphor translates well

Peaks are easy to recognize Interesting characteristics include ridges and

valleys

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ThemeScape

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ThemeScape

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Calendar Based Visualization Using 3 dimensions

X-axis: Time of day Y-axis: Days of data period Z-axis: Univariate data samples

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Calendar Based Visualization

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Calendar Based Visualization

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Graph-Driven Visualization of Relational DataGraph-Driven Visualization of Relational Data

An example of visualizing relational data. This is the visualization of a family tree (graph). Here each image node represents a person and the edges represent relationships among these people in a large family.

Graph VisualizationGraph Visualization

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Classical Graph Layouts Link-node diagrams Layout algorithms (graph drawing) Geometric positioning of nodes & edges Small amount of nodes Avoid node overlaps Reduce edge crossings

hierarchical force-directed orthogonal

symmetric

radial layout

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Using a very large virtual page

The virtual page technique predefines the drawing of the whole graph, and then provides a small window and scroll bar to allow the user to navigate through it (by changing the viewing area).

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Fish-eye views The fish-eye technique can keep a detailed picture of a part of a graph as well as the global context of the graph. It changes the zoomed focus point.

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3D Graph DrawingSGI fsn file-system viewer

Image from:

http://www.sgi.com/fun/images/fsn.map2.jpg

Trees

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2 Approaches Connection (node & link)

Enclosure (node in node)

Structure vs. attributes Attributes only (multi-dimensional viz) Structure only (1 attribute, e.g. name) Structure + attributes

A

CB

A

B C

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Containment Approach

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Treemaps (Shneiderman)

Slice and Dice Alternate horizontal and

vertical cuts for levels

Node area node attribute Zoom onto nodes

Space-Filling Structure + 3 attributes

Area, color, label

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Treemaps

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Balanced trees

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Treemaps ~ 1000 nodes Quantitative attributes Good combination of structure + attributes For unbalanced trees, structure more difficult Learning time: 20 min Evaluation: major performance boost over outliner Bad aspect ratios: long narrow rectangles Large scale or deep causes solid black

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Treemap Algorithm Calculate sizes:

Recurse to children My size = sum children sizes

Draw Treemap (node, space, direction) Draw node rectangle in space Alternate direction For each child:

Calculate child space as % of node space using size and direction Draw Treemap (child, child space, direction)

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Cushion Treemaps

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Squared Treemaps

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Treemaps on the Web Map of the Market: http://www.smartmoney.com/marketmap/ People Map: http://www.truepeers.com/ Coffee Map: http://www.peets.com/tast/11/coffee_selector.asp

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DiskMapper http://www.miclog.com/dmdesc.htm

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2D Tree Drawing (web sitemap)

MosiacG SystemZyers and Stasko

Image from:http://www.w3j.com/1/ayers.270/paper/270.html

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PDQ Trees Overview+Detail of 2D layout Dynamic Queries on each level for pruning

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Space-Optimized Tree Layout

A large data set of approximately 50 000 nodes My Unix root with approx. 3700 directories and files

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Hyperbolic treeThe hyperbolic browser technique performs fish-eye viewing with animated transitions to preserve the user’s mental map. It changes both the viewing area and the zoomed focus point.

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H3

Image from: http://graphics.stanford.edu/papers/h3/fig/nab0.gif