lec 8 intro to information visualization

7/23/2019 Lec 8 Intro to Information Visualization

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1 Dr.-Ing. Benjamin Weyers | Virtual Reality & Immersive Visualization | WS 2015/16 |Course on Data Analysis and Visualization

Course on Data Analysis and Visualization

Introduction to Information Visualization

- Info Vis 1 -



3 Dr.-Ing. Benjamin Weyers | Virtual Reality & Immersive Visualization | WS 2015/16 |Course on Data Analysis and Visualization3

Introduction




Introduction

Florence Nightingales‘ digram, which represents the reduction of the deatch rate based on her

changes in hygenic




Introduction

Map of Soho District, London 1845 showing death rates through Cholera and positions of

water pumps




Introduction

Harry Beck: „When you are underground it does not matter where your are“



7Dr.-Ing. Benjamin Weyers | Virtual Reality & Immersive Visualization | WS 2015/16 |

Course on Data Analysis and Visualization7http://www.flickr.com/photos/24736216@N07/6439692875/sizes/o/in/photostream/

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Course on Data Analysis and Visualization8http://sovibrantopinion8.blogspot.de/2011/04/design-classic-no145-london-underground.html

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Course on Data Analysis and Visualization9http://sovibrantopinion8.blogspot.de/2011/04/design-classic-no145-london-underground.html Today9




Course on Data Analysis and Visualization10

http://farm3.staticflickr.com/2139/2259870535_4fa1a719f9_o.jpg

Future

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Museum of Modern Art, New York, USA – Massimo Vignelli

http://www.mappery.com/m aps/New-York-City-Subway-Map-2.gif

http://s-walker1215-dc.blogspot.de/2012/10/modernism-in-graphic-design.html

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Information Visualization

• “to visualize” [Oxford, 2010]

form a mental image of; imagine

make (something) visible to the eye

• Not only produce pretty images, but aid the understanding of data

• visualization is interdisciplinary by definition

• The difference between InfoVis and SciVis is the type of data being

visualized: Abstract Data vs. Measured Spatial Data

Nevertheless, the differentiation is not 100% clear…

Very different definitions out there

• In general: Information visualization‘s objective (as well as scientific

visualization) is to represent data in a way that the human is able to

gain insight to it and get enabled to understand its structure easily .





The User

• The user has been extensively discussed in the first part of this lecture:

Visual Perception

• Missing is the higher cognitive functions, such as memory, learning,

and information processing in general on a cognitive level

• Missing is the discussion of further modalities like audio, etc.





Rasmussen’s Skill, Rule & Knowledge (SRK) Model of Decision Making

Rasmussen, J. (1983). Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in humanperformance models. Systems, Man and Cybernetics, IEEE Transactions on, (3), 257-266.





The User

• Nevertheless, the basic knowledge of visual perception helps to

produce good visualization concepts• Interaction is very important: Working with the data (visualization)

enables the user to gain a much deeper insight – the human is used to

dynamic processes!

• Apply evaluation methods applicable for the aimed at scenario Create a solid study design, Apply the study to a group of participant,

Evaluate the results





The Task

• There are various possibilities to investigate tasks

Refer to task analysis research

Hierarchical task models (GOMS, CTT, etc.)

• Important is that you are discussing all relevant aspects with the users!

• Questions are:

What is your overall goal?

What is your current approach to reach these goals?

What are current visualization approaches you are used to and you are using

in your everyday work?

• Helpful is to present possible solutions to the user how a possible

visualization could look like -> From Mock-Ups to Prototypes

Cognitive walk throughs

Think aloud protocols

Diaries of daily work and processes





ConccurTaskTrees: Hierachical Task Models by Paterno

• Enabling T1 >> T2

• Enabling with information passing T1 []>> T2

• Disabling T1 [> T2

• Synchronization T1 |[]| T2

• Concurrency T1 ||| T2

• Optionality [T]

• Iteration T1* or T1{n}





Context

• What you need:

Understanding what context is

A description of context that is

understandable by the computer

A system that brings the

description and data

representing the contexttogether

Makes context understandable by

the computer-based system

http://adexchanger.com/comic-strip/adexchanger-context-matters/





The Data

• Before starting to discuss the visual representation of data and values,

the different types of data should be first classified

• The goal of this classification is to describe concepts in InfoVis not like

„Color encoding is well suited for the representation of the development

in a the stock market“

but

„Color encoding is well suited for the representation of categories“





Visual Representation of Data and Values – Data Classification

In general, data classification is very complex task and can be compared

to the classification and description of knowledge• Bertin (1977) proposed a pretty simple classification of data, which

represents relevant features of data

Enti t ies refer to the central objects and information carriers in a domain

Relations specify structures, which entities relates to each other. Many

different types of relations can be imagined. In general, relations can be ofthe type

structural or physical

conceptual

causal

temporal

At t r ibutes of entities and relations can further specify these. In general, it

has not to be clear what attribute or what entity/relation is.

• This concept is used in formal modeling of ontologies.

Have a closer look to OWL






• Stevens (1946) proposed a qualitative classification of data attributes

along numeric scales:

1. Nominal: Elements that could not logically ordered, e.g., apple, orange,

grape

2. Ordinal: Elements that could be logically ordered but where the ordering

has no distance metric, e.g., weather situations along a scale of favor 3. Interval: Ordinal attributes that can be equipped with a (discrete) metric,

e.g., arrival time of planes

4. Ratio: Extended Interval scale to the whole range of real numbers, e.g., the

mass of solid physical objects

1 2 3

1 3 7 1.8 2.45 6.3

1. 2. 3. 4.






Nominal Category Data char

Ordinal Discrete Data enum 1 2 3

1 3 7

1.8 2.45 6.3

1.

2.

3.

4.Ratio + Interval Continous Data int, float






• The attribution of entities can be of higher dimension

• In general, an entity can be represented as field of attributes of differentdimensions -> Data Objects

• Operations applied to data objects or more general than entities,

attributes, or relations, which can not be defined as operations

• Examples of operations are:

Mathematical operations

Merge of two lists

Invert values

Instantiation of entities or relations

…





Visual Representation of Data and Values

• Central requirements of the visualization of data and values are:

Present more than one value at a time

Relevant dependencies and correlation should be visible at one glance

Should be intuitive and simple to understand

Should match the basic visual and perceptual characteristics of the

human visual cognition

• In the following, the visual representation of single values will be

followed by the representation of multiple values up to multi-

dimensional data representations





Overview

User Task

Data

RepresentationsRequirements

Structure





Literature and References

• Robert Spence, (2014), Information Visualization: An Introduction,

Springer.• Edward R. Tufte, (1991), Envisioning Information, Graphics Press.

• Edward R. Tufte, (2001), The Visual Display of Quantitative Information,

Graphics Press.

• Scott Murray, (2013), Interactive Data Visualization for the Web,

O’Reilly

All material not equipped with additional references (URL) on the slides

is taken from the above books.





Single Value Representation

(0-dimensional)






• A simple example for the representation of a

single value is the altimeter used in airplanes.

• This instrument is responsible for various

accidents

• By changes in attention and focusing to other

contexts, changes in the altimeter can overseenvery easily

http://www.m0a.com/altimeter/





Change Blindness

• This effect is known as change blindness. Human perception is

unaware of small changes in complex environments or in more or lesscomplex representations.






2200

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2200

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1-Dimensional Value Representation





Representation of Value Sets – One Dimension

• Example: Visualize the prize of various cars, which are related to a

linear scale

• Question: What is most effective? What should be identified in the

data?

• Focusing on

Mean values

Distribution

Min and Max (price/s)

…

• Possible Visualization Methods: Dot Plot, Box Plot, Histogram





Representation of Value Sets – One Dimension

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Price k€60

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Price k€

00 - 20 20 - 30 30 - 40 40 - 50 50 - 60

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Dot Plot Box PlotHistogram





Box Plot and Histogram

• Box Plot:

Central Line specifies the Median

End of Boxes specify 25 and 75 percentile

End of Lines specify 5 and 95 percentile

Dots show outliers

60

50

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Price k€

• Histogram:

Represent

frequency/occurrence of

values in a value set or of

specific characteristics

00 - 20 20 - 30 30 - 40 40 - 50 50 - 60

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http://www.vorkon.de/VorKon-12.1-Leseprobe/drittanbieter/Anleitungen/vorkon/07112101/

Doku/Doku/digikam/maininterfaceimgproperties3.png





Presentation of Values Sets – One Dimension

• The values do not have to be interval or ratio values but can be also

nominal

• Example: EZChooser (Wittenburg et al. 2010)

• Enables the user to identify the number of cars available in a category

Nissan Ford Ferrari MG Cadillac





Dimensionality of Data

• Data points can be matched to a certain (parameter) dimensionality:

Consider every data point as a tuple t i of parameters, such that

t i = (p1, …, pn ),

where n specifies the dimensionality of the data point t i and p1 to pn

specify the values of the n parameter of point t i . The set

T = (t 1, …, t m )

of a finite number m of data values is defined as data set.

• Example: 2 dimensions (numberOfBedrooms [], price [k €])

(1, 108), (1, 115), (1, 135), (1,150), …, (4, 150), …, (5, 195)

• Example: 3 dimensions (timeToWork [min], numberOfBedrooms [], price [k €])

(30, 1, 108), …, (25, 3, 140), …, (15, 5, 180)





Presentation of Values Sets – Two Dimension

• Example: 2 dimensions (numberOfBedrooms [], price [k €])

(1, 108), (1, 115), (1, 135), (1,150), …, (4, 150), …, (5, 195)

Number of bedrooms

Price [k€]





Scatter Plot

• Basic Scatter Plot can visualize two-

dimensional data

• Possible interpretations are

Identification of trends

Local trade-offs

Outliers

• A specific area for scatter plots is in time-

dependent data, such as Spiking Plots in

Neuroscience

Number of

bedrooms

Price [k€]

https://capocaccia.ethz.ch/capo/wiki/2013/spinnaker13





Spiking Plots



44 Dr.-Ing. Benjamin Weyers | Virtual Reality & Immersive Visualization | WS 2015/16 |


Presentation of Value Sets – Two Dimension

jcharts.sourcefourge.net

Function Plot Bar Chart

https://capocaccia.ethz.ch/capo/wiki/2013/spinnaker13

Scatter Plot Heat Maps

http://www.infovis.info/





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Australia

New

Zealand





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3D Data

http://stats.stackexchange.com/questions/70569/interpreting-3d-scatter-plot





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3D Data

•

X : %

o f w o r k i n g a g e p o p u l a t i o n

•

Y : %

o f p o p u l a t i o n

a b o v e 6 5

•

C o l o r : f e r t i l i t y r a t e

•

S i z e : t o t a l p o p u l a t i o n b y a r e a

•

L i n e : t i m e 1 9 6 0 -

2 0 1 2





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N-Dimensional Value Representation





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Presentation of Value Sets – N Dimensions

• A well known and nice concept is the so called „Small Multiples“

described by Tufte, 2013

• In general: A data set is presented in many small drawings, where one

dimension is altered between all drawings, such as time, position, etc.

• Small Multiples are very good in making changes visible along thealtered dimension

It enables the user to compare different views to the data with each other in a

very simple and convenient way.





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Small Multiples – Historic Example

• Christiaan Huygens, Systema Saturnium (The Hague,1659), p. 55. Cited by Edward R. Tufte, (1991) p.67.

Envisioning Information Graphics Press. Cheshire,

Connecticut





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Small Multiples – Examples

http://dougmccune.com/blog/wp-content/uploads/2011/04/small_multiples_small.png

- Crime data from San Francisco http://apps.sfgov.org/datafiles/index.php?dir=Police&by=name&order=asc

- Double Bar Charts





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http://media.juiceanalytics.co m/images/smallmultiples1.png



56 Dr -Ing Benjamin Weyers | Virtual Reality & Immersive Visualization | WS 2015/16 |56


lec 8 intro to information visualization

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