m13 if5170 isuvisualisasidata 150414

8/10/2019 m13 IF5170 IsuVisualisasiData 150414

1/126

IF5170 Visualisasi DataIssues on Visualization:

Color, Large-Scale Data Visualization,

and Perceptual Issues

12/1/2014 1FNA/IF5170/Issues on Visualization


2/126

Tujuan

Mahasiswa memahami berbagai isu yang

terkait dengan penggunaan warna (color)

dalam visualisasi data

Mahasiswa memahami berbagai isu yangterkait dengan visualisasi data dalam jumlah

besar

Mahasiswa memahami berbagai isu yang

terkait dengan persepsi dalam visualisasi data

12/1/2014 FNA/IF5170/Issues on Visualization 2


3/126

COLOR



4/126

Sources

C. Ware: Information Visualization:

Perception for Design, Chapter 4: Color,

Morgan Kauffman, 2004

Some pictures are from: J. Estelle, N. Illinsky: Beautiful Visualization:

Looking at Data Through the Eyes of Experts,

Chapter 4: Color: The Cinderella of Data

Visualization by Michael Driscoll



5/126



6/126

The use of color in visualization

It is useful to think of color as an attribute of

an object rather than as its primary

characteristic.

It is excellent for labeling and categorization,but poor for displaying shape, detail, or space.



7/126

Color Measurement

We can match any color with a mixture of no morethan three primary lights the basis of colorimetry



8/126



9/126

CIE System of Color Standards



10/126

Opponent Process Theory



11/126

Application of Color in Visualization

Color specification interfaces and color spaces

Color for labeling

Color sequences for data maps

Color reproduction

Color for exploring multidimensional discrete

data



12/126

Color Specification Interfaces and

Color Spaces (1)

Types of control that can be given to the users

to choose their own colors:

a point in a three-dimensional color space

a set of color names to choose from a palette of predefined color samples



13/126


Color Spaces (2)

HSV color space (Smith, 1978) hue, saturation,and value (HSV) coordinates to RGB monitorcoordinate



14/126



15/126


Color Spaces (3)

Color naming systems, e.g.:

Natural Color System (NCS)

based on Herings opponent color theory (1920)

widely used in England and other European countries The Pantone system

used in the printing industry

The Munsell system

an important reference for surface colors



16/126


Color Spaces (4)

NCS



17/126

Color for Labeling (1)

Nominal information coding: technical name

for labeling an object

Color is extremely effective as a nominal code

Perceptual factors to choose a set of colorlabels:

1. Distinctness: the degree of perceived difference

between two colors that are placed close

together


(kemampuan untuk menentukan perbedaan)


18/126



19/126


Perceptual factors to choose a set of color labels

(cont.):

2. Unique hues: red, green, yellow, blue, black, white

Natural choices when a small set of colors is needed

No two colors should be chosen from the same category

3. Constrast with background

Color-coded objects can be expected to appear on a

variety of backgrounds

A method for reducing contrast effects place a thinwhite or black border around the color-coded object



20/126


Perceptual factors to choose a set of colorlabels (cont.):

4. Color blindness: most color-blind populationcannot differ colors in a red-green direction

Almost everyone can distinguish color in yellow-bluedirection

Reduces the design choices

5. Number: Only a small number of color can be

rapidly perceived

five to ten codes (Healey,1996)


(tergantung user, butawarna atau tidak)


21/126


Perceptual factors to choose a set of color labels(cont.):

6. Field size: the larger the area that is color-coded,the more easily colors can be distinguished

Small objects highly saturated colors for maximumdiscrimination

Large areas low saturation, differ only slightly from oneanother

7. Conventions:

Common conventions, e.g. red = hot, red = danger, blue =cold, green = life, green = go



22/126


12 colors recommended for use in coding:



23/126




24/126

Color Sequences for Data Maps (1)

Pseudocoloring: the technique of

representing continuously varying map values

using a sequence of color

Used in: astronomical radiation charts,medical imaging, geography, etc.



25/126



26/126


27/126



28/126


Ordinal pseudocolor sequence:

pseudocolor sequence in which the monotonic

ordering of data values in different parts of the

display can be perceived Can be achieved by using:

a black-white, red-green, or yellow-blue sequence

a saturation sequence or with any relatively

straight line through opponent color space a spectrum approximation



29/126


A map of ozone concentrations in the

atmosphere is shown:

(a) As a blackwhite sequence.

(b) As a saturation sequence.(c) As a spectrum-approximation sequence.

Images courtesy of Penny Rheingans (Rheingans,

1999).


30/126


31/126


Interval pseudocolor sequences: pseudocolor sequences in which each unit step of the

sequence represents an equal change in magnitudeof the characteristic being displayed across the wholerange of the sequence.

Can be achieved by: Using a uniform color space in which equal perceptual

steps correspond to equal metric steps (Robertsonand OCallaghan, 1988)

Introducing steps deliberately in the color sequence (a

banded color sequence) Using isovalue contours



32/126



33/126



34/126


35/126



36/126


Sequences for the color blind:

Some color sequences are not perceived well by

people with color blindness red to green

Sequences on black-to-white or yellow-to-bluedimension will still be clear for color-blind people



37/126



38/126


Bivariate color sequences:

it is possible to display two or even three

dimensions using pseudocoloring

e.g. yellow-blue variation combined with light-dark variation and saturation



39/126



40/126



41/126



42/126

Color Reproduction (1)

The problem transferring color

appearances from one display device to

another

E.g.: from a computer monitor to a sheet of paper The gamut of a device: the set of all colors

that can be produced by a device



43/126



44/126


Heuristic principles to create good mapping from

one device to another (Stone et al.,1988):

The gray axis of the image should be preserved

Maximum luminance contrast (black to white) is

desirable

Few colors should lie outside the destination gamut

Hue and saturation shifts should be minimized

An overall increase of color saturation is preferable to

a decrease



45/126



46/126


Heuristic principles to create good mapping from onedevice to another (Stone et al.,1988) (cont.), steps:

Calibration: to calibrate the monitor and the printingdevice in a common reference system

Range scaling: to equate the luminance range of thesource and destination images

Rotation: to equate the monitor white with the paperwhite, the monitor gamut is rotated so as to make thewhite axes collinear

Saturation scaling: the monitor gamut is scaled radially

with respect to the blackwhite axis to bring the monitorgamut within the range of the printing gamut



47/126

Color for Exploring Multidimensional

Discrete Data (1)

One of the most interesting but difficult

challenges for data visualization is to support

exploratory data analysis

Problems can arise in exploring data whenmore than two dimensions of data are to be

displayed



48/126



49/126


50/126


51/126

Conclusions (3)

Color contrast can cause large errors in the

representation of quantity

Beware of oversaturating colors, especially

when a printed image is to be the end product



52/126

Tugas

Untuk dikumpulkan dan dibahas minggu depan(Selasa, 22 April 2014) Carilah 1 buah contoh visualisasi data (bebas, boleh

interaktif, boleh statik)

Jelaskan: Data apa yang divisualisasikan dan bagaimana teknik

visualisasinya

Diskusikan berdasarkan aspek-aspek evaluasi (lihat catatandi belakang ini)

Diskusikan penggunaan color dalam visualisasi tersebut(kaitkan dengan saran-saran pada kuliah hari ini)

Dikerjakan berkelompok 2 orang, ditulis dalamlaporan hardcopy di kertas A4



53/126

Evaluation

Things to watch out:

Data and statistical accuracy

Visualization accuracy

Functional accuracy

Visual inference

Formatting accuracy

Annotation accuracy

12/1/2014FNA/IF5170/Interactive Visualization and

Virtual Reality53


54/126

Post-Launch Evaluation

To seek to assess the visualization's

effectiveness and impact in a post-launch

setting position yourself as the user

12/1/2014FNA/IF5170/Interactive Visualization and

Virtual Reality54


55/126

LARGE-SCALE DATA VISUALIZATION



56/126

Sources

C. Johnson, C. M. Hansen: The Visualization

Handbook, Elsevier, 2004:

Chapter 27: Large Scale Data Visualization and

Rendering: A Problem Driven Approach by P.

McCormick and J. Ahrens

Chapter 28: Issues and Architectures in Large-

Scale Data Visualization by C. Pavlakos, P. D.

Heermann



57/126

Introduction

Large-scale data the datasets are largerthan can be processed by a single computer

4 fundamental techniques for processing thevisualization of large-scale data:

Data streaming

Task parallelism

Pipeline parallelism

Data parallelism

+ Hybrid systems


(penjumlahan matriks cocok untuk ini)

=> data sama, proses beda

=> proses sama


58/126

Data Streaming (1)

Data streaming: Most commonly used to processindependent subsets of a larger dataset, one subset ata time

Often, the only feasible approach in situations where thesize of a dataset exceeds the capacity of the available

computing resources (memory and swap space)

The key advantage any size dataset can besuccessfully processed

The drawback often requires a substantial amount

of execution time and does not allow for theinteractive exploration of the data



59/126

Data Streaming (2)

In order to produce the correct solution, the

algorithms must be result invariant:

the results must be consistent regardless of the

number of subsets into which the data is split

the algorithms must be able to divide the original

dataset into piece



60/126


61/126


http://www.csm.ornl.gov/newsite/group_astro.html


62/126



63/126



64/126


65/126

Task Parallelism (1)

Task parallelism: independent modules in an applicationexecute in parallel

An algorithm is required to be broken up into independenttasks and that multiple computing resources be available

The key advantage it enables multiple portions of a

visualization task to be executed in parallel The main disadvantage

Difficult to calculate the number of independent tasks that canbe identified and the number of CPUs available, limits themaximum amount of parallelism

Difficult to load-balance the tasks it can be very challenging

to take full advantage of the available resources



66/126

Task Parallelism (2)

Case Study: Datasets on earths oceans by

Parallel Ocean Program (POP) at Los Alamos

National Laboratory



67/126



68/126



69/126



70/126


71/126

Pipeline Parallelism (1)

Pipeline parallelism: a number of modules in an applicationexecute in parallel, but on independent subsets of data

Advantages: In situations where there are multiple, heterogeneous tasks.

It allows parallel use of the overall computing resources

Disadvantages: It can make it difficult to balance the execution time required by

the individual stages; in an unbalanced pipeline, the sloweststage directly impacts the overall performance.

The length of the pipeline directly limits the amount ofparallelism that can be achieved (i.e., you must have as manyprocessors as there are pipeline stages).



72/126

Pipeline Parallelism (2)

Case study: A simple PC-based animation

application that reads the image files from

disk and displays them on a single monitor

Conclusion: the two tasks require similar time for small images

but the read operation becomes more costly as

the image size increases



73/126


74/126



75/126


76/126


77/126

Data Parallelism (3)

Case study: Salinity of the Atlantic by creating andviewing an isosurface of salinity colored bytemperature (by by the Parallel Ocean Program(POP))

Conclusions: Performance improves by a factor of two with each

doubling of the number of processors for all datasetsizes.

Due to the memory requirements of the full datasetand the resulting graphics primitives, visualization is

only possible when we use 16 or more processor



78/126



79/126


80/126

Summary



81/126


82/126

Hybrid Systems (2)

Case study: TRex is a hybrid system that usesdata streaming, pipelining, and dataparallelism

a large volume of data is broken into individual

subsets in order to efficiently render the datadata parallelism

overall series of parallel tasks: Read, Render,Composite, Display parallel pipeline

streaming multiple time-steps of data through theRead module



83/126


84/126

Th f l i d


85/126

The process of analyzing and

visualizing large-scale scientific data



86/126

Some Problems (1)

Enabling Data Exploration and Discovery

The need for tools and environments that support

the efficient, effective exploration of data

A robust interactive environment is needed that

enables the scientist or analyst (the user) to

receive timely and useful feedback in the search

for answers.

This environment must be accessible from the

office, which is where day-to-day work is done



87/126

Some Problems (2)

The Need for Scalable Solutions The computing resources demanded by complex,

high-fidelity simulation applications inherently implythe use of parallel computing

Common problems: the visualization of data does not fit in the memory of a conventional office

visualization system

is large enough that conventional office graphics packagesare inadequate

may not fit on the local disk

is large enough that traditional high-performance graphicssystems lack the rendering performance


A E d t E d A hit t f


88/126

An End-to-End Architecture for

Large Data Exploration



89/126



90/126


91/126

Conclusions (2)

Features of tools and environments that enableeffective and efficient data analysis andvisualization of large-scale data (cont.):

The ability to get the right data to the right place for

further processing when needed Wide availability of cost-effective, scalable, high-

performance infrastructures

Parallelism and the ability to support end-to-endparallelism throughout high-performance parts of the

environment



92/126

Conclusions (2)

Features of tools and environments that enableeffective and efficient data analysis andvisualization of large-scale data (cont.):

The ability to apply a broad set of diverse tools,

leveraging interoperability The ability to leverage the interactivity and power of

increasing desktop computing and visualizationresources

The ability to drive the whole environment from the

computational-science laboratory of choice, namelythe office



93/126

PERCEPTUAL ISSUES



94/126

Source

C. Johnson, C. M. Hansen: The VisualizationHandbook, Elsevier, 2004: Chapter 39: Extending Visualization to

Perceptualization: The Importance of Perception inEffective Communication of Information by D. S.

Ebert Chapter 40: Art and Science in Visualization by

Victoria Interrante

C. Ware: Information Visualization: Perceptionfor Design

Appendix C: The Perceptual Evaluation ofVisualization Techniques and Systems



95/126

Introduction

Traditional visualization must evolve intoperceptualization of information

conveying information through multipleperceptual channels and perceptually tuned

rendering techniques The choice of visual rendering techniques

should be driven by characteristics of humanperception

perceptual channels are the communicationmedium.



96/126

Human Perception

Categories: Preattentive processing: some of the information

is processed at a very low level in parallel without

conscious thought

Attentive processing: other information requiresattention, or conscious thought, to perceive the

information.


(persepsi alam bawah sadar)

(persepsi yang dilakukan dengan berpikir)


97/126


98/126

Preattentive Visual Processing (2)

Classification of features:



99/126

Types of Channels

Types of channels: The visual perceptual channel the most widely

used communication channel

The auditory and haptic channels beingincorporated

to convey additional information as redundant forms of communication to increase accuracy

or speed of communication

others: olfactory, vestibular, gustatory

Understanding the way humans perceive

information

vital to the effective conveyanceof information through perceptualization.


The Use of Art dan Science in


100/126

The Use of Art dan Science in

Visualization

There are significant potential benefits in:

Seeking inspiration from previous graphical work

in art, illustration, visual communication, and

design

Seeking insights from research in vision and visual

perception



101/126


Using gaps to effectively portraying dense collections of overlapping lines

Since prehistoric times, artists have used gaps to indicate the passing of one surface behind

another, as shown in the image of a horse from Paleolithic cave paintings in Lascaux, France

Using gaps to effectively portray dense collections of overlapping lines


102/126


g g p y p y pp g

Using Feature Lines to Emphasize the Essential 3D Structure of a Form


103/126



104/126


105/126


Using Texture on Surfaces to Clarify Shape


106/126


An illustration of the texture interference effects that arise when multiple

overlapping transparent surfaces are rendered with principal directiontexture strokes

(http://www-users.cs.umn.edu/~interran/tvcg/tvcg.html)


107/126

Perceptual Evaluation Techniques

Psychophysics

Detection methods

Method of adjustment

Cognitive Psychology

Structural Analysis

Testbench applications

Structured interviews

Rating scales

Statistical Exploration

Principal Component

Analysis Multidimensional Scaling

Clustering

Multiple Regression

Cross-cultural Studies

Child Studies


Empirical research methods can be applied toevaluate perceptual aspects of visualization:

h h ( )


108/126

Psychophysics (1)

Psychophysics: A set of techniques based on applying the methods of

physics to measurements of human sensation

Extremely successful in defining the basic set oflimits of the visual system and discovering the

important sensory dimensions of color, texture,sound, and so on, e.g.: How rapidly must a light flicker before it is perceived

as steady?

What is the smallest brightness change that can be

detected?


h h ( )


109/126

Psychophysics (2)

Problems with psychophysics:

Often carried out using only one or two observer

generalized to the entire human race

But, some experiments require hundreds of hours of

careful observation large subject population is out ofquestion

It is usually assumed (sometimes wrongly) that

instructional biases are not significant in the

experiment


h h i ( )


110/126

Psychophysics (3)

Information psychophysics: a new variant ofpsychophysics

To apply classical psychophysics to common

information structures, e.g. elementary flow

pattern, surface shape, paths in graphs


P h h i (4)


111/126

Psychophysics (4)

Common psychophysical methods (may alsobe applied in information psychophysics):

Detection Method

The goal of the experiment: determining the error rate

how many errors people make when performing acertain task

E.g.: aircraft inspection process expected error rateof an inspector is critical

Error rate is commonly used to determine threshold

Staircase procedure

Signal detection theory



112/126


P h h i (5)


113/126

Psychophysics (5)

Common psychophysical methods (cont.):

Method of Adjustment

Give application domain experts control over some

variable and ask them to adjust it so that it is optimal in

some way for them Can also be used to answer questions about perceptual

distortion


C iti P h l


114/126

Cognitive Psychology

Goal of experiments: testing a hypothesisabout a cognitive model

Methods:

Measuring reaction time or measuring error

E.g. Determining whether or not a particular object is ina display

Measuring interference between visual patterns

Increase in the resulting errors is used as evidence thatdifferent channels of information processing converge

at some point



115/126

St t l A l i (2)


116/126

Structural Analysis (2)

Testbench Applications:

The testbench application: a flexible tool capable

of producing a range of visual mappings of the

data and a range of interaction possibilites

E.g. Problem: find the best way to represent the

shape of a surface, then the testbench app should

be able to:

Load different surface shapes, change lighting, change

surface texture properties, turn stereoscopic viewingon/off, provide motion parallax cues, etc.


St t l A l i (2)


117/126


Structured Interviews: Construct an interview with a structured set of

questions of elicit information about specific taskrequirements

In visualization:

To evaluate what aspects of visualization actually areimportant for potential users

To evaluate a number of different solutions for strengths andweaknesses

Advantage: to gain information about a wide range of

issues with relatively little effort


St t l A l i (3)


118/126


Rating Scales: A method for turning opinions into numbers, e.g.:

We have six visual representations of a flow pattern, we might ask

subjects to rate how well they are on a scale of 1 to 5

Subjects tend to bias the rating scale toward either thelower or upper end

No absolute meaning should be given to rating scale data

Rating scale an excellent tool for measuring relative

preferences


St ti ti l E l ti (1)


119/126

Statistical Exploration (1)

Statistical discovery techniques can be useful tolearn about some class of visualization methods

Using statistics to discover how many dimensions that

can be conveyed by a visualization

Major techniques: Principal Component Analysis

Multidimensional Scaling

Clustering

Multiple Regression


St ti ti l E l ti (2)


120/126


Principal Component Analysis: The goal: to take a set of variables and find a new set

of variables (the principal components) that are

uncorrelated with each othermight be used to

reduce a high-dimensional dataset to lowerdimensions

Multidimensional Scaling:

A method explicitly designed to reduce the

dimensionality of a set of data points to two or three,

so that these dimensions can be displayed visually




121/126


Clustering: A statistical technique designed to find clusters of

points in a data space of any dimensionality

Two basic kinds: hierarchical and k-means

Multiple Regression:

A statistical technique that can be used to

discover whether it is possible to predict some

response variable from display properties


Cross Cultural Studies


122/126

Cross-Cultural Studies

Cross-cultural studies can be used to testwhether sensory codes are interpreted easily

by all humans

E.g. Color naming are compared across more than

100 languages (Berlin & Kay, 1969)

It is becoming impossible with the globalization of

world culture


Child Studies


123/126

Child Studies

Using behaviorism techniques: To discover things about a childs sensory

processing even before the child is capable of

speech revealing basic processing mechanism


Practical Problems in Conducting User


124/126

Studies (1)

Experimenter bias

There are many opportunities for experimenter

bias in both the gathering and the interpretation

of results

How many subjects are used?

Statistically, the number of subjects and the

number of observations required depend on the

variability of responses with a single subject and

the variability from one subject to another




125/126

Studies (2)

Combinatorial Explosion: In visualization design problems, there are often

many possible independent variables leads to

combinatorial explosion cannot be

experimented using brute force approach

Task Identification:

In order to provide a useful measure of

performance, it is also important that the task can

be set up to have a clear and simple user response




126/126

Studies (3)

Controls: A control is a condition that is used to provide

some basis for comparison

In evaluating a new visualization method, the

most reasonable control is the current bestpractice display method

Getting help:

Studies in information visualization are

fundamentally multidisciplinary

m13 if5170 isuvisualisasidata 150414

Documents