INFORMATION VISUALIZATION

Visualization

The use of computer-supported, interactive, visual representations of data to amplify cognition.

The purpose of visualization is insight not pictures

Goals of insight are Decision making, Discovery and Explanation

Why Visualization?

A picture is worth ten thousand words Amplify our cognition ability

Cognition: the acquisition or use of knowledge Specific goals:

Communicating ideas Create and discover ideas Use visual perception to solve problems

To get a ‘Ah HA’ response from the viewer

Origin

Data graphics-1786-Playfair(Use lines, areas visually)

Theory of Graphics-1967-Bertin(Plotting Data)

Theory of Data-1983-Tufte(maximising density of useful information)

Exploratory Data Analysis-use of pictures to give statistical insight to Data

Visualization amplifies cognition

Increases resources Reduces search Enhanced recognition of patterns Perceptual inference Perceptual monitoring Manipulable medium

Visualization Principles

Expressiveness: Encode all the facts in the result set. Encode only the facts in the result set.

Effectiveness: Depends on the capability of the perceiver. Encode the more important information more

effectively.

Visualization – Twin Subjects

Scientific Visualization Visualization of

physical data

Information Visualization Visualization of

abstract data

Ozone layer around earthAutomobile web site- visualizing links

Scientific Visualization – Information Visualization

Focus is on visualizing set of observations that are multi-variate

There is no underlying field – it is the data itself we want to visualize

The relationship between variables is not well understood

Focus is on visualizing an entity measured in a multi-dimensional space

Underlying field is recreated from the sampled data

Relationship between variables well understood

Scientific Visualization

Information Visualization

Information Visualization

“… is a process of transforming data and information that are not inherently spatial, into a

visual form allowing the user to observe and understand the information.”

(Source: Gershon and Eick, First Symposium on Information Visualization)

“… the use of computer-supported, interactive, visual representations of abstract data to amplify cognition.”

Card, Mackinlay, Shneiderman

Basic Visualization Model

Goal

Data

Data transfer

Insight(learning, knowledge extraction)

Method

Data

Visualization

Map: data → visual

~Map-1: visual → data insight

Data transfer

Insight

Visual transfer

(communication bandwidth)

Visual Mappings

Data

Visualization

Map: data → visual

Visual Mappings must be:• Computable (math)

visual = f(data)

• Comprehensible (invertible)data = f-1(visual)

• Creative!

Visualization Pipeline

Raw data(information)

Visualization(views)

Data tables

Visualstructures

Datatransformations

Visualmappings

Viewtransformations

task

User interaction

Visual Mapping: Step 1

1. Map: data items visual marks

Visual marks: Points Lines Areas Volumes Glyphs

Visual Mapping: Step 2

1. Map: data items visual marks2. Map: data attributes visual properties of

marksVisual properties of marks:

Position, x, y, z Size, length, area, volume Orientation, angle, slope Color, gray scale, texture Shape Animation, time, blink, motion

Information Types

Multi-dimensional: databases,… 1D: timelines,… 2D: maps,… 3D: volumes,… Hierarchies/Trees: directories,… Networks/Graphs: web, communications,… Document collections: digital libraries,…

1-D Data

Linear data: textual document, source code, etc.

User problems: count, find, replace, … Encoding: fonts, color, size, layout, scrolling,

selection capabilities, … Product example: text editor, browser, …

2-D Data

Planar or map data: geographical maps, floor plans, newspaper layouts, …

User problems: find adjacent items, search containment, find paths, filtering, details-on-demand, …

Encoding: size, color, layout, arrangement, multiple layers, …

Product example: CAD

3-D Data

Real-world objects: building, human body User problems: adjacency in 3-D,

inside/outside relationship, position, orientation

Encoding: overviews, landmarks, transparency, color, perspective, stereo display

Product example: CAD

Temporal Data

Time series data: medical records, project management, historical presentation

User problems: finding all events before, after or during some time period or moment.

Encoding: time lines

Multi-dimensional Data

Relational and statistical databases tuples. User problem: finding patterns, clusters,

correlations, gaps, outliers. Challenge:

Simultaneously display many dimensions of large subsets of data.

Create displays that best encode the data pattern for a particular task.

Rapidly select a subset of tuples or dimensions.

Tree Type Data

Exponential data: hierarchies, tree structures. User problems: find the structural properties

Height of the tree Number of children Find nodes with same attributes

Encoding:Node-link diagrams: allowing the encoding of linkage between entities. Treemap: child rectangles inside parent rectangles

Product example: windows explorer, internet traffic

Network Data

Graph data: multiple paths, cycles, lattices User problems:

Shortest path Topology problems

Encoding: Node-link diagram Matrix

Basic Visualization Tasks

Overview of a collection of data. Zoom in/on objects of interest. Filter out uninterested items. Details-on-demand: view details. Relate: View relationship. History: Undo, Redo, Refinement. Extract a subset of the data.

User Tasks

Easy stuff: Min, max, average, % These only involve 1 data item or value

Hard stuff: Patterns, trends, distributions, changes over time, outliers, exceptions, relationships, correlations, multi-way, combined min/max, tradeoffs, clusters, groups, comparisons, context, anomalies, data errors, Paths, …

Excel can do this

Visualization can do this!

Scientific Visualization Model

visualizemodeldata render

Visualization represented as pipeline:Read in dataBuild model of underlying entityConstruct a visualization in terms of geometryRender geometry as imageData are more spatial

Classification of InfoVis Techniques

Based on the type of information Visualization of Information Structure

Trees, Networks Visualization of Multivariate Data

1D, 2D, 3D, n-D, Temporal Visualization of Workspace

Windows, web pages, documents, etc

Classification of InfoVis Techniques

Based on how we interact with the data Overview: fisheye Zooming: e.g. Table Lens Interactive filtering: e.g. Magic Lens Brushing and linking: e.g. XGobi Details-on-demand: e.g. Spotfire

InfoVis Design Issues

Selection What data should we choose to visualize?

Representation How should data be represented? Colors? Locations?

…

Presentation Too much data, too little display space

InfoVis Design Issues (cont’d)

Scale and Dimensionality What if you have 93 variables to visualize?

Interaction and Exploration How user interacts with the data?

Hot Topics: network visualization, document visualization, security related problems, etc

InfoVis Applications

Complex Documents Biography, manuscript, data structure

Histories Patient histories, student records, etc

Classifications Table of contents, organization charts, etc

Networks Telecom connections and usages, highway, etc

InfoVis Example

Stephen Eick’s Seesoft

InfoVis Example

Hyperbolic Trees

2D Hyperbolic Tree 3D Hyperbolic Tree

Escher’s woodcut

InfoVis Example

Themescape (Cartia)

Interactive Graphics

Homefinder

Visualization Techniques

Location Probes-eg.Film finder(use location to view additional data)

View point Controls-eg.Information mural(Overview+detail)

Distortion-eg.Perspective Wall(Focus+Context)

Focus + Context

Data Types

Univariate Bivariate Trivariate

Univariate

Dot plot Bar chart (item vs. attribute) Histogram

Bivariate

Scatterplot

Trivariate

3D scatterplot, spin plot 2D plot + size (or color…)

Information Visualization Using 3D Interactive

Animation

Visual Abstractions

Hierarchical Structure -> Cone Tree Linear Structure -> Perspective Wall Continuous Data -> Data Sculpture Spatial Data -> Office Floor plan

Cone Tree

Cone Tree (Compaq Research)

Perspective Wall

Spatial data

Web Book and Web Forager. Card, Robertson, York. 1996.

Document Visualization

IE Vs IV

Examples 1-D

Traversing long lists in changeable sort orders. Viewing summary data about many ordered items, possibly to find important specific elements. Filtering out unwanted items.

2-D Semantic zooming

3-D Containment issues. Position (Up, Down, Inside, Outside) queries.

Multi-D Understand or get an overview of the whole or a part of the n-dimensional data. For example, finding patterns, relationships, clusters, gaps, and outliers of the data. Find a specific item in the data. For example, zooming, filtering and selecting a group or a single item from the data.

Temporal Viewing events or data in sequence and/or varying the order of those events or data. Viewing and creating historical overviews of events or data. Finding temporal inconsistencies and/or undesirable relationships in events or data.

Tree Obtain global relationships and structure from the entire hierarchy. Find the most recent common ancestor between two nodes. Find the path to a particular node from the root of the hierarchy. Find clusters, duplicates, relationships, and inheritance properties from the structure of the hierarchy. Discovering attributes (especially the size) of nodes or entire subtrees.

Network Change the layout of nodes of a graph so that the network is easier to comprehend. Relate: enable the user to find relationship among the nodes in a diagram. Find interesting paths in a graph.

Workspace Enable users to view and interact with computer screen layouts in a more efficient manner. Allow geographically dispersed users to collaborate and interact concurrently. Synthesize information, expertise, and results to create high quality solutions. Organize, interact, and search task related information efficiently. Allow rapid access and restructuring for task information.

 

Data Scalability

# of attributes (dimensionality)

# of items

Value range(e.g. bits/value)

Increase Data Density

Calculate data/pixel

“A pixel is a terrible thing to waste.”

(Tufte)

(Shneiderman)

Advantages

Reduce visual search time (e.g. by exploiting low level visual perception).

Provide a better understanding of a complex data set (e.g., by exploiting data landscape metaphors).

Reveal relations otherwise not being noticed (e.g., by exploiting the mind’s ability to see relationships in physical structures).

Enable to see a data set from several perspectives simultaneously.

Are effective sources of communication.

Visualization techniques can help individuals to abstract, customize, manipulate and understand the information being presented

Conclusion

Visualization helps Information presentation Information extraction

Good visual encoding should match the target data and user problems.

Studying the successful/unsuccessful visual encoding designs and techniques helps us to design and develop new encoding approaches.

IE Vs IV

Topics

Information Types: Multi-D 1D 2D 3D Hierarchies/Trees Networks/Graphs Document collections

Strategies: Design Principles Interaction strategies Navigation strategies Visual Overviews Multiple Views Empirical Evaluation Development Theory Tools

Philosophy: Optimization

Visualization = the best of bothImpressive computation + impressive cognition

Computer•Serial•Symbolic•Static•Deterministic•Exact •Binary, 0/1•Computation•Programmed •Follow instructions•Amoral

Human•Parallel •Visual •Dynamic •Non-deterministic •Fuzzy•Gestalt, whole, patterns •Understanding •Free will•Creative •Moral

Thank You