visual analytics review

Visual Analytics ReviewIAT 355

Lyn Bartram

Overview

• Topics ( in no particular order)■ Data models and analytics■ Information visualization techniques: Types and components■ Interaction■ Perception■ Cognition■ Navigation and Scent■ Presentation and screen space

IAT 355 Introduction

Overview and definitions


Information visualization

• visual metaphors for non-inherently spatial data such as the exploration of text-based document databases.■ More abstract

• Assign structure and position to information that has none

• Text• Statistics• Finance/Business• Internet• Software


Visual analytics

• analytical reasoning supported by the interactive visual interface

• Intersection of visualization with data analysis

• Biology

• National security



Visual thinking

Visual thinking involves:• Constructing visual queries on displays• Visual search strategies through eye movements and

attention to relevant patterns• Visual notification and attention “redirection” to new patterns

and events

• Well structured balance of elements and tasks

Data Analytics


Data We Use

• Data Models

• Types

• Metadata

• Aggregates

• Descriptive Statistics

• Distribution

• Clusters

Show Me the Numbers! : Data

Data models

• take raw data and transform it into a form that is more workable

• Main idea: build a model■Individual items are called cases or records■Cases have attributes : an attribute is a value of a

variable or factor ■In vis terms, a dimension

How many dimensions?

• Data sets of dimensions 1, 2, 3 are common

• Number of variables per class• 1 - Univariate data• 2 - Bivariate data• 3 - Trivariate data• >3 - Hypervariate data

■These are the fun and interesting ones! But hard!


Data Types (measurements)

• Nominal: categorical,( equal or not equal to other values)■ Example: gender, Student Number■ No concept of relative relation other than inclusion in the set

• Ordinal : sequential ( obeys < > relation, ordered set■ Example: Size of car, speed settings on road■ Example: mild, medium, hot, suicide■ Distance is not uniform


Data Types 2

• Interval : Relative measurements, no fixed zero point. ■ Data is numerical, not categorical. Rank order among variables

is explicit with an equal distance between points in the data set: -2, -1, 0, +1, +2

■ can say “twice as much as”■ Example: height above sea level, hours in a day

• Ratio: Interval data with absolute zero ■Example: account balance, degrees Kelvin


13

Dimensions

• Data Dimensions are classified as:■ Quantitative i.e. numerical

• Continuous (e.g. pH of a sample, patient cholesterol levels)

• Discrete (e.g. number of bacteria colonies in a culture)

■ Categorical• Nominal (e.g. gender, blood group)• Ordinal (ranked e.g. mild, moderate or severe illness).

Often ordinal variables are re-coded to be quantitative.

Metadata

• Descriptive information about the data

• Might be something as simple as the type of a variable, or could be more complex

■ For times when the table itself just isn’t enough■ Example: if variable1 is “l”, then variable3 can only be 3, 7 or

16

• Missing values, uncertainty or importance are all examples of metadata


Mary Tom Louise65432101 98765651 89846251

20 22 19

Sep 2006 Jan 2004 Sep 2005

4.0 2.3 3.04

Primary types of data analysis

• Qualitative• Descriptive. Used to describe the distribution of a

single variable or the relationship between two nominal variables (mean, frequencies, cross-tabulation)

• Inferential (Used to establish relationships among variables; assumes random sampling and a normal distribution)

• Nonparametric (Used to establish causation for small samples or data sets that are not normally distributed)


Descriptive Statistics• Range• Min/Max• Average• Median• Mode

Distribution Statistics• Variance• Error• Standard Deviation• Histograms and

Normal Distributions


• The Range■ Difference between minimum and maximum values in a data

set■ Larger range usually (but not always) indicates a large spread

or deviation in the values of the data set.

(73, 66, 69, 67, 49, 60, 81, 71, 78, 62, 53, 87, 74, 65, 74, 50, 85, 45, 63, 100)

Range, Min, Max

Average = measure of centrality

• Measures of location indicate where on the number line the data are to be found. Common measures of location are:

• (i) the Arithmetic Mean,• (ii) the Median, and• (iii) the Mode

The mean is vulnerable to problems

0 2.5 7.5 104.8

0 2.5 7.5 104.8

The data may or may not be symmetrical around its average value

• The Median■The middle value in a sorted data set. Half the values

are greater and half are less than the median.■Another measure of central location in the data set.(45, 49, 50, 53, 60, 62, 63, 65, 66, 67, 69, 71, 73, 74, 74,

78, 81, 85, 87, 100)Median: 68

(1, 2, 4, 7, 8, 9, 9)

• The Median■May or may not be close to the mean.■Combination of mean and median are used to define

the skewness of a distribution.


0 2.5 7.5 106.25

The Mode

• The Mode■The most frequent occurring value.■Another measure of central location in the data set.■(45, 49, 50, 53, 60, 62, 63, 65, 66, 67, 69, 71, 73, 74,

74, 78, 81, 85, 87, 100)■Mode: 74■Generally not all that meaningful unless a larger

percentage of the values are the same number


When do we use what?

• Dependent on how the data are distributed■ Note if mean=median=mode then the

data are said to be symmetrical• Rule of thumb:

■ use mean if data are normally distributed and variance is within constraints

■ Use median to reduce effects of outliers


Summary


http://statistics.laerd.com/statistical-guides/measures-central-tendency-mean-mode-median.php

Data distribution

• Measures of dispersion characterise how spread out the distribution is, i.e., how variable the data are.

• Commonly used measures of dispersion include:1. Range2. Variance & Standard deviation3. Coefficient of Variation (or relative standard deviation)4. Inter-quartile range


Measures of variance

• Variance■ One measure of dispersion (deviation from the mean) of a data

set. The larger the variance, the greater is the average deviation of each datum from the average value

• Standard Deviation■ the average deviation from the mean of a data set.

• An outlier is an datum which does not appear to belong with the other data


27

Inter-quartile range

• The Median divides a distribution into two halves.

• The first and third quartiles (denoted Q1 and Q3) are defined as follows:

■ 25% of the data lie below Q1 (and 75% is above Q1),

■ 25% of the data lie above Q3 (and 75% is below Q3)

• The inter-quartile range (IQR) is the difference between the first and third quartiles, i.e. IQR = Q3- Q1

28

Box-plots

• A box-plot is a visual description of the distribution based on ■ Minimum■ Q1■ Median■ Q3■ Maximum■ If a data point is < lower limit or > upper

limit, the data point is considered to be an outlier.

• Useful for comparing large sets of data

Distribution is important for Aggregation

• Visualization helps us see relations – or the trends of them - as visual patterns

• a lot of what we visualize are the descriptive statistics ■ Example: mean income vs median income■ Need to ensure that the univariate units of visualization are legit

• Rule: check your core units /variables. If hey are descriptive, look at the distribution


Example: job losses in US over time


2D Visualization Classes


Types of Symbolic Displays (Kosslyn 89)

• Graphs

• Charts

• Maps

• Diagrams

T yp e n am e h e reT yp e t it le h e re




Types of Symbolic Displays

• Graphs■ at least two scales required■ values associated by a symmetric “paired with” relation

• Examples: scatter-plot, bar-chart, layer-graph

Graphs

• Encode quantitative information using position and magnitude of geometric objects.

• Examples: scatter plots, bar charts.

Types of Symbolic Displays

• Charts■ discrete relations among discrete

entities■ structure relates entities to one

another■ lines and relative position serve as

links• Examples:

■ Family tree■ Flow chart■ Network diagram

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IAT 355 38

Map

• Internal relations determined (in part) by the spatial relations of what is pictured■ Grid: geometric metadata

• Locations identified by labels• Nominal metadata

• Examples:• Map of census data• Topographic maps

Jan 21, 2011

IAT 355 39

Choropleth Map

• Areas are filled and colored differently to indicate some attribute of that region

Diagrams

• Schematic pictures of objects or entities

• Parts are symbolic (unlike photographs)■how-to illustrations■figures in a manual

From Glietman, Henry. Psychology. W.W. Norton and Company, Inc. New York, 1995

Jan 21, 2011

IAT 355 41

Graph Components

• Framework (spatial substrate)■Measurement types, scale■Geometric Metadata

• Content■Marks, lines, points■Data

• Labels■Title, axes, ticks■Nominal Metadata

Jan 21, 2011

IAT 355 42

Marks

• Things that occur in space■Points■Lines■Areas■Volumes

IAT 355 43Jan 21, 2011

Graphical Properties

• Size, shape, color, orientation... Spatial

PropertiesObject Properties

Expressing Extent

Position, Size

Greyscale

Differentiating Marks

Orientation Color, Shape, Texture

Jan 21, 2011

IAT 355 44

What goes where

• In univariate representations, we often think of the data case as being shown along one dimension, and the value (quantity) in another

Y Axis is quantitative

Graph shows change in Y over continuous range X

Y Axis is quantitative

Graph shows value of Y for 4 cases

Jan 21, 2011

IAT 355 45

Bivariate Data

• Representations■Scatter plot■Each mark is a data case■Want to see relationship between

two variables■What is the pattern?■Note both variables are

continuous data

Price

Mileage

Jan 21, 2011

IAT 355 46

Multivariate: Project data onto other graphical variables• E.G., Use blob attribute for another variable

Price

Mileage

Price

Mileage

Jan 21, 2011

IAT 355 47

Alternative

• Represent each variable on its own line

Small multiples

Data projection

• Fundamentally, we have 2 display dimensions• For data sets with >2 variables, we must project data

down to 2D■Come up with visual mapping that locates each

dimension into 2D plane• Computer graphics 3D->2D projections

IAT 355: Mutivariate Data

This slide courtesy of Matt Ward, UC Berkeley

What is Multivariate Data?

• Each data point has N variables or observations• Each observation can be:

■ nominal or ordinal ■discrete or continuous ■scalar, vector, or tensor

• May or may not have spatial, temporal, or other connectivity attribute

This slide courtesy of Matt Ward, UC Berkeley

Methods for Visualizing Multivariate Data

• Dimensional Subsetting• Dimensional Reorganization

dimensional re-ordering• Dimensional Embedding• Dimensional Reduction

UC Berkeley, 09/19/00

Dimensional Subsetting

• Scatterplot matrix displays all pairwise plots

• Selection allows linkage between views

• Clusters, trends, and correlations readily discerned between pairs of dimensions

• Small mulitples


Dimensional Reorganization

• Parallel Coordinates creates parallel, rather than orthogonal, dimensions.

• Data point corresponds to polyline across axes

• Clusters, trends, and anomalies discernable as groupings or outliers, based on intercepts and slopes

Dimensional Reorganization (2)

• Glyphs map data dimensions to graphical attributes• Size, color, shape, and orientation are commonly used• Similarities/differences in features give insights into

relations



Dimensional Embedding

• Dimensional stacking divides data space into bins

• Each N-D bin has a unique 2-D screen bin

• Screen space recursively divided based on bin count for each dimension

• Clusters and trends manifested as repeated patterns


Dimensional Reduction

• Map N-D locations to M-D display space while best preserving N-D relations

• CLUSTERING• Approaches include MDS,

PCA, and Kohonen Self Organizing Maps

• Relationships conveyed by position, links, color, shape, size, etc.

Perception


Preattentive processing

• A limited set of visual properties are processed preattentively (without need for focusing attention).■ Visual features

• This is important for the design of visualizations■ what can be perceived immediately■ what properties are good discriminators■ what can mislead viewers■ Differentiate items “at a glance” – THEY POP OUT

Some examples from Chris Healey:http://www.csc.ncsu.edu/faculty/healey/PP/PP.html

IAT 355 Perception

http://www.csc.ncsu.edu/faculty/healey/PP/PP.html

Preattentive processing features

• Form■ Line orientation■ Line length■ line width■ Size■ Curvature■ Spatial grouping■ Blur■ numerosity

• Colour■ Hue■ Intensity

• Motion■ Flicker■ Direction of motion

• Spatial position■ 2D position■ Stereo depth■ Concavity/convexity shape

from shading

IAT 355 Perception

Coding with several features: conjunction

• What happens with more complex patterns ?■ a large red circle, not just something that is red or something that is large?

• slow if the surrounding objects are large (but not red ones) and other red sizes.■ a serial search of either the red or the large circles.

• conjunction search - searching for the specific conjunction of colour and size attributes.■ generally not pre-attentive, although there are a few very interesting

exceptions.

IAT 355 Perception

IAT 355 Perception

Conjunction does not pop out

IAT 355 Perception

Compound features do not pop out

Surrounded colours do not pop out

IAT 355 Perception

Patterns | IAT355 | 25.03.2012

Similarity and integral dimensions

• A: separable dimensions allow both groupings to be perceived – but not simultaneously

• B: with integral dimensions we can see both to construct a grid

separable integral

Similarity and the separability of dimensions

Integral dimensions (colour and grayscale) are used to delineate rows and columns

Separable dimensions (colour and texture) make it easier to attend separately to either the rows or the columns

Integral-Separable

• Not one or other, but along an axis

Integral vs. Separable Dimensions

Integral

Separable

Jan 17, 2011

66[Ware 2000]

Colour

Perceptual issues

• Contrast effects

• Luminance and brightness

• Colour deficiency


Contrast, Luminance and Colour | IAT814 | 21.09.2011

Simultaneous contrast effects

• a gray patch placed on a dark background looks lighter than the same gray patch on a light background.

• http://www.michaelbach.de/ot/lum_dynsimcontrast/index.html

http://www.michaelbach.de/ot/lum_dynsimcontrast/index.html


Effects cause error!

• Simultaneous contrast effects can result in large errors of judgment when reading quantitative (value) information displayed using a gray scale.

• Ware et al showed an average error of 20% of the entire gray scale in a map encoding gravity fields using 16 levels of gray.


Crispening


What about colour?

• Colour perception is relative

• We are sensitive to small differences■ hence need sixteen million colours

• Not sensitive to absolute values■ hence we can only use < 10 colours for coding


Vischeck

• Simulates color vision deficiencies■ Web service or Photoshop plug-in■ Robert Dougherty and Alex Wade

• www.vischeck.com

Deuteranope Protanope Tritanope

http://www.vischeck.com/

IAT355 | Colour for Information Display

Fundamental Uses

• To label (colour as noun)• To measure ( colour as quantity/value)• To represent (colour as representation)

■ to imitate reality• To enliven or decorate (colour as beauty)


Colour great for classification

• Rapid visual segmentation• Colour helps us determine type• Only about six categories

Information Visualization Colin Ware


Contrast Creates Pop-out

Hue and lightness Lightness only


Pop-out vs. Distinguishable

• Pop-out■ Typically, 5-6 distinct values simultaneously■ Up to 9 under controlled conditions

• Distinguishable■ 20 easily for reasonable sized stimuli■ More if in a controlled context■ Usually need a legend


Data to Color

• Types of data values■ Nominal, ordinal, numeric■ Qualitative, sequential, diverging

• Types of color scales■ Hue scale

• Nominal (labels)• Cyclic (learned order)

■ Lightness or saturation scales• Ordered scales• Lightness best for high frequency• More = darker (or more saturated)• Most accurate if quantized

Quantized• Signal varies

continuously

Discretized• Restricted to a

prescribed set of values


Pseudocoloring

• Pseudocoloring is the technique of representing continuously varying map values with a sequence of colours

• Sometimes overlaid on luminosity information■ Need to use an isoluminant color map to avoid distortion

• “intuitive” based on lightness, saturation• No perceptually based hue scales

■ Need to be learned


Pseudocoloring


Thematic MapsUS Census

Map

Mapping Census 2000: The Geography of U.S. Diversity

http://www.census.gov/population/www/cen2000/atlas.html

http://www.census.gov/population/www/cen2000/atlas.html


• Univariate scale is a path in a colour space■ Progression along a line

• Multivariate is:■ Plane? 2D■ Volume ? 3D■ Rules for color mixing

• Only perceptual coding is 2D ■ lightness x saturation

• Color for multivariate only works well for highly quantized data■ Like a mnemonic for a labeling scheme

•

How many dimensions?


Brewer System

http://www.colorbrewer.org


What Defines Layering?

• Perceptual features■ Contrast (especially lightness)■ Color, shape and texture

• Task and attention■ Attention affects perception

• Display characteristics■ Brightness, contrast, “gamma”

Emergency

Emergency

Emergency


General guidelines … orfrom Tufte to practice [Stone, Ware]

• Assign colour according to function

• Use contrast to highlight

• Use analogy to group

• Control value contrast for legibility

• Break isoluminance with borders

Visual Organisation and Patterns


Patterns | IAT355 | 25.03.2012

Pattern learning

• People who work with visualizations must learn the skill of seeing patterns in data.

• In terms of making visualizations that contain easily identified patterns, one strategy is to rely on pattern-finding skills that are common to everyone.

• Good idea to use priming to enhance perceptual receptivity

Patterns | IAT355 | 25.03.2012

The Gestalt laws

The core laws1. Proximity2. Similarity3. connectedness4. Continuity5. Symmetry6. Closure7. Relative Size8. Common fate

Principal effects

9. Figure - ground10. Prägnanz : the “organising

principle”

Patterns | IAT355 | 25.03.2012

Proximity: design implications

• Emphasize relationship by proximity

• Emphasize relationship by spatial density

Patterns | IAT355 | 25.03.2012

Similarity

• Similarity between the elements in alternate rows causes the row percept to dominate

Patterns | IAT355 | 25.03.2012

Continuity

• The Gestalt principle of continuity states that we are more likely to construct visual entities out of objects that are smooth and continuous, rather than those that contain abrupt changes in direction.

• We see a-b crossing c-d • not a-d or b-c

A

BC

DDA

Patterns | IAT355 | 25.03.2012

Connectedness

• Connecting graphical objects by a line is a very powerful way of expressing that there is a relationship between them

• Basis of node-link diagrams

• Most common method of indicating relationships

Patterns | IAT355 | 25.03.2012

Symmetry

• Symmetry creates visual whole

• Powerful organising principle

• b and c are seen as figures/objects, where a is a pair of parallel lines

• We construct objects in the world

(a) (b) ( c )

Patterns | IAT355 | 25.03.2012

Closure

• Over-rules proximity !• A closed contour tends to be seen as an object• The Gestalt psychologists argued that there is a perceptual tendency to close

contours that have gaps

a circle behind a rectangle as in (a), not a broken ring as in (b).

Patterns | IAT355 | 25.03.2012

Figure and Ground

• Confronted by a visual image, we seem to need to separate a dominant shape (a 'figure' with a definite contour) from what our current concerns relegate to 'background' (or 'ground')

• Symmetry, white space, and closed contour contribute to perception of figure.

• The perception of figure as opposed to ground can be thought of as the fundamental perceptual act of identifying objects.

Patterns | IAT355 | 25.03.2012

Prägnanz

• A stimulus will be organized into as good a figure as possible. Here, good means symmetrical, simple, and regular.

• here we see a square overlapping a triangle, not a combination of several complicated shapes.

Cognitive tasks

99

Low-Level Components of Analytic Activity in Information Visualization Amar, Eagan, and Stasko

• Identified 10 low-level analysis tasks that largely capture people’s activities while employing information visualization tools for understanding data

• Retrieve value• Filter• Compute derived value• Find extrema• Sort

• Determine range• Characterize distribution• Find anomalies• Cluster• Correlate

100

Example Visual Analytics Characteristics

• Whole-part relationship: multiple levels of information extraction

• Relationship discovery: high dimensional analytics to detect the expected and discover the unexpected

• Combined exploratory and confirmatory analytics

• Selection, search (bool. and similarity) and groupings

• Temporal and geospatial analytics

• Extensive labeling: everything active on screen

• Multiple linked views

• Analytic interactions are foundational to critical thinking

• Analytic reasoning framework

• Capture analytic snippets for reporting

• Both general and application specific applications

Understanding

• People utilize an internal model that is generated based on what is observed

• Tversky calls the internal model a cognitive map• Just don’t have one big one• Have large number of these for all different kinds of

things• Collection of cognitive maps --> Cognitive collage

Process model 1: Navigation (spence)

Content

Browsingstrategy

Internalmodel

Interpretation

Browse Model

InterpretFormulate a

browsingstrategy

New view

Cognitive map

OverviewZoomFilterDetails-on-demandBrowseSearch query

Read factRead comparisonRead patternManipulateCreateDelete

ReorderClusterClassAveragePromoteDetect patternAbstract

Process model 2: Knowledge Crystallization

Instantiateschema

Search forschema

Foragefor data

Problem-solve

Author,decideor act

TaskExtractCompose

Instantiate

Process

task

Rawdata

Datatables

VisualStructures Views

Datatransformations

Visualmappings

Viewtransformations

Visual Task Taxonomy (Zhou & Feiner)

# Relational tasksAssociate-- Collocate-- Connect-- Unite-- AttachBackgroundCategorize-- MarkDistributeCluster-- Outline-- IndividualizeCompare-- Differentiate-- Intersect

Correlate-- Plot-- MarkComposeDistinguish-- MarkDistribue-- IsolateEmphasize-- Focus-- Isolate-- ReinforceGeneralize-- MergeIdentify-- Name-- Portray-- Individualize-- Profile

Locate-- Position-- Situate-- Pinpoint-- OutlineRank-- TimeReveal-- Expose-- Itemize-- Specify-- SeparateSwitch

# Direct visual# organizing and# encoding tasksEncode-- Label-- Symbolize-- -- Quantify-- -- Iconify-- Portray-- Tabulate-- Plot-- Structure-- Trace-- Map

The nested items are refinements of particular ways of achieving task

Dimensions

• Visual tasks have two main dimensions1. Visual accomplishments - describe presentation intents that task might help

to achieve2. Visual implications - particular type of visual action that visual task may

carry out

1. Visual Accomplishments

• All about presentation intent• Classified into two categories:

■ Tasks that inform the user (e.g., make a presentation with ppt)■ Tasks that enable user to explore or compute (e.g., decide

which stock to buy)• Each of these can be broken down further

Visual Accomplishments

Inform EnableElaborate Summarize Explore Compute

Search Verify Sum DifferentiateEmphasizeReveal

AssociateBackgroundCategorizeClusterCompareCorrelateDistinguishGeneralizeIdentifyLocateRank

CorrelateLocateRank

CorrelateLocateRank

CategorizeClusterCompareCorrelateDistinguishEmphasizeIdentifyLocateRankReveal

CategorizeCompareCorrelateDistinguishIdentifyLocateRankReveal

2. Visual Implications

• Categorize various visual tasks by whether they imply■ Certain types of visual organization■ Certain ways of visual signaling■ Certain paths of visual transformation

interaction

interaction | IAT 355

Shneiderman’s Taxonomy of Information Visualization Data Types

• 1-D Linear Document Lens, SeeSoft• 2-D Map GIS, Medical imagery• 3-D World CAD, Medical, Molecules, Architecture• Multi-Dim Parallel Coordinates, Spotfire,

Influence Explorer, TableLens• Temporal Perspective Wall, LifeLines, Lifestreams• Tree Cone/Cam/Hyperbolic, TreeBrowser, Treemap• Network Netmap, netViz, Multi-trees

Shneiderman’s Taxonomy of Information Visualization Tasks• Overview: see overall patterns, trends• Zoom: see a smaller subset of the data• Filter: see a subset based on values, etc.• Detailed on demand: see values of objects when interactively

selected• Relate: see relationships, compare values• History: keep track of actions and insights• Extract: mark and capture data


Interaction Techniques

• View Specification (map data to visual variables)• Navigation (pan, zoom, scale, rotate)• Selection• Highlighting (Brushing)• Filtering• Sorting• Extract Data


Advanced Interaction Techniques• Brushing and Linking

• Overview + Detail

• Focus + Context

• Panning and Zooming


Brushing and Linking

• Select (“brush”) a subset of data• See selected data in other views

• The components must be linked by tuple (matching data points), or by query (matching range or values)


How long in majorssalaries

Filtering: Dynamic Queries• Spotfire, by Ahlberg & Shneiderman

■ http://hcil.cs.umd.edu/video/1994/1994_visualinfo.mpg■ Now a very sophisticated product:

• http://spotfire.tibco.com/products/gallery.cfm


http://hcil.cs.umd.edu/video/1994/1994_visualinfo.mpg

http://spotfire.tibco.com/products/gallery.cfm

Interaction strategies

• Data manipulation■ Structure, Transform, edit

• Selecting:■ details on demand■ Extract, Manipulate

• Linking:■ Brushing+linking

• Filtering: ■ Dynamic Queries

• Rearranging:■ Sorting, ordering

• Remapping:■ Changing the visual mapping

• Navigation:■ Overview navigation: z+p,

o+d, f+c, physical nav■ 3D navigate

Filtering/Limiting

• Fundamental interactive operation in infovis is changing the set of data cases being presented■ Focusing■ Narrowing/widening■ Suppression (remove irrelevant items from attention)

• Iterative interactive queries/ dynamic queries

Sensitivity

HomeFinder (Williamson and Shneiderman, 1992)

???

Visualizing Relations

When is Graph Visualization Applicable?

• Ask the question: is there an inherent relation among the data elements to be visualized?

■ If YES – then the data can be represented by nodes of a graph, with edges representing the relations.

■ If NO – then the data elements are “unstructured” and goal is to use visualization to analyze and discover relationships among data.

Source: Herman, Graph Visualization and Navigation in Information Visualization: a Survey

Graph and Tree Data Structures

• Graphs: ■ Representations of structured, connected data■ Consist of a set of nodes (data) and a set of edges

(relations)■ Edges can be directed or undirected

• Trees: ■ Graphs with a specific structure

• connected graph with n-1 edges■ Representations of data with natural hierarchy■ Nodes are either parents or children

Traditional Graph Drawing

poly-line graphs (includes bends)

planar, straight-line drawing

orthogonal drawing

upward drawing of DAGs

Aesthetic constraints

• Minimize link crossings• Minimize link lengths• Minimize link bends• Maximize symmetries• Minimize link crossings• Mathematically difficult to do

everything• Often unsuitable for interactive

visualisation• Approximation algorithms very complex• Unless you only need to compute layout

once• Precompute layout, or compute once at

the beginning of an application then support interaction

(Some) Layout Approaches

• Tree-ify the graph - then use tree layout• Hierarchical graph layout• Radial graph layout• Optimization-based techniques

■ Includes spring-embedding / force-directed layout• Adjacency matrices• Structurally-independent layout• On-demand revealing of subgraphs• Distortion-based views

■ Hyperbolic browser

• (this list is not meant to be exhaustive)

Optimization-based layout

• Specify constraints for layout■ Series of mathematical equations■ Hand to “solver” which tries to optimize the constraints

• Examples■ Minimize edge crossings, line bends, etc■ Multi-dimensional scaling (preserve multi-dim distance)■ Force-directed placement (use physics metaphor)

• Benefits■ General applicability■ Often customizable by adding new constraints

• Drawbacks■ Approximate constraint satisfaction■ Running time; “organic” look not always desired

Hyperbolic Layout

• Root mapped at center• Multiple generations of children

mapped out towards edge of circle

• Drawing of nodes cuts off when less than one pixel

Presentation


Approaches

• Viewport/Window (Panning and Zooming)• Overview + Detail• Distortion-based Views• Focus + Context• Time Based Views

■ RSVP

Figure 4.2 Scrolling hides most of a document

Vewport

Figure 4.34 Panning is the smooth movement of a viewing frame over a 2D image

Zooming• Geometric Zooming

■ Get close in to see information in more detail■ Example: Google earth zooming in

• Intelligent Zooming■ Show semantically relevant information out of proportion■ Example: speed-dependent zooming, Igarishi & Hinkley

• Semantic Zooming■ Zooming can be conceptual as opposed to simply reducing

pixels■ Example tool: Pad++ and Piccolo projects

• http://hcil.cs.umd.edu/video/1998/1998_pad.mpg

http://hcil.cs.umd.edu/video/1998/1998_pad.mpg

Focus + Context Methods

• Filtering• Overview+Detail• Highlighting• Distortion

Figure 4.3 Focus + context. Miniatures of pages of a pdf document provide useful context while attention is paid to detail of one page

Overview + Detail: “you are here” K. Hornbaek et al., Navigation patterns and Usability of Zoomable User Interfaces with and without an Overview, ACM TOCHI, 9(4), December 2002.

ArcTrees – Interaction – Focus + Context

Hidden relations within Ch(16) of

Glyph

Expanded relations

between Ch 15 & 16

Slide adapted from Fengdong Du

Distortion-based Views

• Distort an image of a large amount of information so that it can fit in screen.■ Allow the user to examine a local area in detail;■ At the same time, present a global view of the information space;

• Provide navigation mechanism.• Co-existence of local details with global context at reduced

magnification.• A focus region to display detailed information.• De-magnified view of the peripheral areas is presented around the

focus area.


Distortion-based Techniques

• Bifocal Display• Polyfocal Display• Perspective Wall• Fisheye View• Graphical Fisheye View

Figure 4.8 Metaphor illustrating the principle of the Bifocal Display

(a) An information space containing documents, emails, etc.

(b) The same space wrapped around two uprights.

(c) Appearance of the information space when viewed from an appropriate direction

direction of view

Slide adapted from Hornung & Zagreus

Perspective Wall

• Similar to Bifocal, except demagnifies at increasing rate, while Bifocal is constant

• Visualizes linear information such as timeline• Adds 3D but uses excess real estate on screen

Feb 28, 2011

IAT 355 141

Fisheye Terminology

• Focal point• Distance from focus• Level of detail• Degree of interest function

http://www.cs.umd.edu/hcil/fisheyemenu/fisheyemenu-demo.shtml

http://www.cs.umd.edu/hcil/fisheyemenu/fisheyemenu-demo.shtml


Image from Sarkar & Brown ‘92

visual analytics review

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