Visual Computing

Lecture 2

Visualization, Data, and Process

Pipeline 1High Level Visualization Process

1. Data Modeling2. Data Selection3. Data to Visual Mappings4. Scene Parameter Settings (View Transforms)5. Rendering

Pipeline 2Computer Graphics

1. Modeling2. Viewing3. Clipping4. Hidden Surface Removal5. Projection6. Rendering

Pipeline 3Visualization Process

Pipeline 4Knowledge Discovery

(Data Mining)

A Data Analysis Pipeline

Raw Data

Processed Data

HypothesesModels Results

Cleaning Filtering

Transforming

Statistical Analysis Pattern Rec

Knowledge Disc

Validation

A CB

D

Where Does Visualization Come In?

• All stages can benefit from visualization• A: identify bad data, select subsets, help

choose transforms (exploratory)• B: help choose computational techniques, set

parameters, use vision to recognize, isolate, classify patterns (exploratory)

• C: Superimpose derived models on data (confirmatory)

• D: Present results (presentation)

What do we need to know to do Information Visualization?

• Characteristics of data– Types, size, structure– Semantics, completeness, accuracy

• Characteristics of user– Perceptual and cognitive abilities– Knowledge of domain, data, tasks, tools

• Characteristics of graphical mappings– What are possibilities– Which convey data effectively and efficiently

• Characteristics of interactions– Which support the tasks best– Which are easy to learn, use, remember

Visualization Components

• Techniques• Graphs & plots

• Maps

• Trees & Networks

• Volumes & Vectors

• …

• Design Process• Iterative design

• Design studies

• Evaluation

• Design Principles• Visual display

• Interaction

• Frameworks

• Data types

• Tasks

• Human Abilities• Visual perception

• Cognition

• Motor skillsImply

Constrain design

Inform design

Issues Regarding Data

• Type may indicate which graphical mappings are appropriate– Nominal vs. ordinal– Discrete vs. continuous– Ordered vs. unordered– Univariate vs. multivariate– Scalar vs. vector vs. tensor– Static vs. dynamic– Values vs. relations

• Trade-offs between size and accuracy needs• Different orders/structures can reveal different

features/patterns

Adapted from Stone & Zellweger 11

Types of Data• Quantitative (allows arithmetic operations)

- 123, 29.56, …

• Categorical (group, identify & organize; no arithmetic)

Nominal (name only, no ordering)• Direction: North, East, South, West

Ordinal (ordered, not measurable)• First, second, third …• Hot, warm, cold

Interval (starts out as quantitative, but is made categorical by subdividing into ordered ranges)

• Time: Jan, Feb, Mar• 0-999, 1000-4999, 5000-9999, 10000-19999, …

Hierarchical (successive inclusion)• Region: Continent > Country > State > City• Animal > Mammal > Horse

Quantitative Data

• Characterized by its dimensionality and the scales over which the data has been measured

• Data scales comprise:– Interval scales - real data values such as degrees

Celsius, but do not have a natural zero point. – Ratio data scales - like interval scales, but have a

natural zero point and can be defined in terms of arbitrary units.

– Absolute data scales - ratio scales that are defined in terms of non-arbitrary units.

Data Dimensions

• Scalar - single value – e.g. Speed. It specifies how fast an object is traveling.

• Vector – multi value – e.g Velocity. It tells the speed and direction.

• Tensor – multi value– Scalars and vectors are special cases of tensors with degree (n) equal to 0 and 1

respectively. – The number of tensor components is given as dn, where d is the dimensionality

of the coordinate system. – In a three dimensional coordinate system (d=3), a scalar (n=0) requires three

values; and a tensor (n=2) requires 9 values. – There is a difference between a vector and a collection of scalars. – A multidimensional vector is a unified entity, the components of which are

physically related. – The three components of a velocity vector of particle moving through three-space

are coherently linked; while a collection scalar measurements such a weight, temperature, and index of refraction, are not.

Metadata

• Metadata provides a description of the data and the things it represents. – e.g., a data value of 98.6 oF has two metadata

attributes: temperature and temperature scale. – The value 98.6 has little meaning without the

metadata attribute of temperature. – By adding Fahrenheit the attribute, we know the

Fahrenheit sale is used.

• Metadata may also include descriptions of experimental conditions and documentation of data accuracy and precision.

Issues Regarding Mappings

• Variables include shape, size, orientation, color, texture, opacity, position, motion….

• Some of these have an order, others don’t

• Some use up significant screen space

• Sensitivity to occlusion

• Domain customs/expectations

www3.sympatico.ca/blevis/Image10.gif

Importance of Evaluation

• Easy to design bad visualizations• Many design rules exist – many conflict, many routinely

violated• 5 E’s of evaluation: effective, efficient, engaging, error

tolerant, easy to learn• Many styles of evaluation (qualitative and quantitative):

– Use/case studies– Usability testing– User studies– Longitudinal studies– Expert evaluation– Heuristic evaluation

Categories of Mappings

• Based on data characteristics– Numbers, text, graphs, software, ….

• Logical groupings of techniques (Keim)– Standard: bars, lines, pie charts, scatterplots– Geometrically transformed: landscapes, parallel coordinates– Icon-based: stick figures, faces, profiles– Dense pixels: recursive segments, pixel bar charts– Stacked: treemaps, dimensional stacking

• Based on dimension management (Ward)– Dimension subsetting: scatterplots, pixel-oriented methods– Dimension reconfiguring: glyphs, parallel coordinates– Dimension reduction: PCA, MDS, Self Organizing Maps– Dimension embedding: dimensional stacking, worlds within worlds

Scatterplot Matrix

• Each pair of dimensions generates a single scatterplot

• All combinations arranged in a grid or matrix, each dimension controls a row or column

• Look for clusters, outliers, partial correlations, trends

Parallel Coordinates

• Each variable/dimension is a vertical line

• Bottom of line is low value, top is high

• Each record creates a polyline across all dimensions

• Similar records cluster on the screen

• Look for clusters, outliers, line angles, crossings

Star Glyph

• Glyphs are shapes whose attributes are controlled by data values

• Star glyph is a set of N rays spaced at equal angles

• Length of each ray proportional to value for that dimension

• Line connects all endpoints of shape

• Lay glyphs out in rows and columns

• Look for shape similarities and differences, trends

Other Types of Glyphs

Dimensional Stacking

• Break each dimension range into bins• Break the screen into a grid using the number of bins for 2

dimensions• Repeat the process for 2 more dimensions within the

subimages formed by first grid, recurse through all dimensions• Look for repeated patterns, outliers, trends, gaps

Pixel-Oriented Techniques

• Each dimension creates an image

• Each value controls color of a pixel

• Many organizations of pixels possible (raster, spiral, circle segment, space-filling curves)

• Reordering data can reveal interesting features, relations between dimensions

Methods to Cope with Scale

• Many modern datasets contain large number of records (millions and billions) and/or dimensions (hundreds and thousands)

• Several strategies to handle scale problems– Sampling– Filtering– Clustering/aggregation

• Techniques can be automated or user-controlled

Examples of Data Clustering

Example of Dimension Clustering

Example of Data Sampling

The Visual Data Analysis (VDA) Process

• Overview

• Filter/cluster/sample

• Scan

• Select “interesting”

• Details on demand

• Link between different views

Issues Regarding Users

• What graphical attributes do we perceive accurately?

• What graphical attributes do we perceive quickly?• Which combinations of attributes are separable?• Coping with change blindness• How can visuals support the development of

accurate mental models of the data?• Relative vs. absolute judgements – impact on

tasks

Role of Perception

MC Escher

Consider the Following

Role of Perception

• Users interact with visualizations based on what they see. (e.g. black spots at intersection of white lines)

• Must understand how humans perceive images.

• Primitive image attributes: shape, color, texture, motion, etc.

Op Art - Victor Vasarely Visualization Example

OpGlyph (Marchese)

Gestalt Psychology

Rules of Visual PerceptionProximity

Similarity

Continuity

Closure

Symmetry

Foreground & Background

Size

Principles of Art & Design

Emphasis / Focal Point

Balance

Unity

Contrast

Symmetry / Asymmetry

Movement / Rhythm

Pattern / Repetition

Issues Regarding Interactions

• Interaction critical component• Many categories of techniques

– Navigation, selection, filtering, reconfiguring, encoding, connecting, and combinations of above

• Many “spaces” in which interactions can be applied– Screen/pixels, data, data structures,

graphical objects, graphical attributes, visualization structures