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 earth Automobile 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

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

(learning, knowledge extraction) Goal Data Data transfer Insight (learning, knowledge extraction)

Method Data transfer Data Insight ~Map-1: visual → data insight Map: data → visual ~Map-1: visual → data insight Visualization Visual transfer (communication bandwidth)

Visual Mappings Visual Mappings must be: Data Computable (math) visual = f(data) Comprehensible (invertible) data = f-1(visual) Creative! Map: data → visual Visualization

Visualization Pipeline task Raw data (information) Data tables Visual structures Visualization (views) Data transformations Visual mappings View transformations User interaction

Visual Mapping: Step 1 Map: data items  visual marks Visual marks: Points Lines Areas Volumes Glyphs

Visual Mapping: Step 2 Map: data items  visual marks Map: data attributes  visual properties of marks Visual 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.

Visualization can do this! User Tasks Excel can do this 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, … Visualization can do this!

Scientific Visualization Model visualize model data render Visualization represented as pipeline: Read in data Build model of underlying entity Construct a visualization in terms of geometry Render geometry as image Data 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 Representation Presentation 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 Histories Classifications 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 Escher’s woodcut 2D Hyperbolic Tree Based on hyperbolic geometric transformation

InfoVis Example Themescape (Cartia) Each document is treated a high dimensional point. We cluster the points first in the high dimensional space, and then take the center of the cluster to perform projection.

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 Communications of the ACM, 36(4):57--71, April 1993.

Visual Abstractions Hierarchical Structure -> Cone Tree Linear Structure -> Perspective Wall Continuous Data -> Data Sculpture Spatial Data -> Office Floor plan Used to speed pattern detection

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 2-D 3-D Multi-D Temporal Tree Network Workspace 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 (Tufte) Calculate data/pixel “A pixel is a terrible thing to waste.” (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: Strategies: Multi-D Design Principles 1D Hierarchies/Trees Networks/Graphs Document collections Strategies: Design Principles Interaction strategies Navigation strategies Visual Overviews Multiple Views Empirical Evaluation Development Theory Tools

Philosophy: Optimization 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 Visualization = the best of both Impressive computation + impressive cognition

Thank You