visual analytics review
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Visual Analytics Review. IAT 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. Overview and definitions. - PowerPoint PPT PresentationTRANSCRIPT
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
IAT 355 Introduction
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
IAT 355 Introduction
Visual analytics
• analytical reasoning supported by the interactive visual interface
• Intersection of visualization with data analysis
• Biology
• National security
IAT 355 Introduction
IAT 355 Introduction
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
IAT 355 Introduction
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!
Show Me the Numbers! : Data
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
Show Me the Numbers! : Data
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
Show Me the Numbers! : Data
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
Show Me the Numbers! : Data
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)
Show Me the Numbers! : Data
Descriptive Statistics• Range• Min/Max• Average• Median• Mode
Distribution Statistics• Variance• Error• Standard Deviation• Histograms and
Normal Distributions
Show Me the Numbers! : Data
• 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.
Show Me the Numbers! : Data
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
Show Me the Numbers! : Data
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
Show Me the Numbers! : Data
Summary
Show Me the Numbers! : Data
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
Show Me the Numbers! : Data
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
Show Me the Numbers! : 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
Show Me the Numbers! : Data
Example: job losses in US over time
Show Me the Numbers! : Data
Example: job losses in US over time
Show Me the Numbers! : Data
Show Me the Numbers! : Data
2D Visualization Classes
IAT 355 Introduction
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
T yp e n am e h e reT yp e t it le h e re
T yp e n am e h e reT yp e t it le h e re
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
Jan 21, 2011
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
UC Berkeley, 09/19/00
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
UC Berkeley, 09/19/00
UC Berkeley, 09/19/00
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
UC Berkeley, 09/19/00
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
IAT 355 Introduction
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
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
IAT 355 Introduction
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
Contrast, Luminance and Colour | IAT814 | 21.09.2011
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.
Contrast, Luminance and Colour | IAT814 | 21.09.2011
Crispening
Contrast, Luminance and Colour | IAT814 | 21.09.2011
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
Contrast, Luminance and Colour | IAT814 | 21.09.2011
Vischeck
• Simulates color vision deficiencies■ Web service or Photoshop plug-in■ Robert Dougherty and Alex Wade
• www.vischeck.com
Deuteranope Protanope Tritanope
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)
IAT355 | Colour for Information Display
Colour great for classification
• Rapid visual segmentation• Colour helps us determine type• Only about six categories
Information Visualization Colin Ware
IAT355 | Colour for Information Display
Contrast Creates Pop-out
Hue and lightness Lightness only
IAT355 | Colour for Information Display
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
IAT355 | Colour for Information Display
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
IAT355 | Colour for Information Display
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
IAT355 | Colour for Information Display
Pseudocoloring
IAT355 | Colour for Information Display
Thematic MapsUS Census
Map
Mapping Census 2000: The Geography of U.S. Diversity
IAT355 | Colour for Information Display
• 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?
IAT355 | Colour for Information Display
Brewer System
http://www.colorbrewer.org
IAT355 | Colour for Information Display
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
IAT355 | Colour for Information Display
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
IAT 355 Introduction
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.
IAT 355 Introduction
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 | IAT 355
Interaction Techniques
• View Specification (map data to visual variables)• Navigation (pan, zoom, scale, rotate)• Selection• Highlighting (Brushing)• Filtering• Sorting• Extract Data
interaction | IAT 355
Advanced Interaction Techniques• Brushing and Linking
• Overview + Detail
• Focus + Context
• Panning and Zooming
interaction | IAT 355
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)
interaction | IAT 355
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
interaction | IAT 355
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
IAT 355 Introduction
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
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.
Slide adapted from Fengdong Du
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
Slide adapted from Fengdong Du
Image from Sarkar & Brown ‘92