Information Visualisation - Lecture 2

@wassx#ILV Informationsvisualisierungen
Information
Visualisation
Information
Visualisation
Lecture 2 - Data

#ILV Informationsvisualisierungen 2
Types of Data

Types of Data
Our goal of visualisation research is to transform data into a
perceptually efﬁcient visual format.
Therefore we must be able to say something about types of
data to visualise.

Types of Data
For example:
„Color coding is good for stock-market symbols, but texture
coding is good for geological maps.“

Types of Data
Better?
„Color coding is good for category information.“
or
„Motion coding is good for highlighting selected data.“

Types of Data
https://en.wikipedia.org/wiki/Jacques_Bertin
Jacques Bertin
„..was a French cartographer and
theorist, known from his book
Semiologie Graphique (Semiology
of Graphics), published in 1967.
This monumental work, …
represents the ﬁrst and widest intent
to provide a theoretical foundation
to Information Visualization.“

Types of Data
Jacques Bertin
… suggested that there are two fundamental forms of data:
1. Data values (Entities)
2. Data structures (Relationships)

Types of Data
Entities are the objects we wish to visualise, 
relations deﬁne structures and patterns that relate
entities. 
 
Sometimes relations are provided explicitly, sometimes
the discovery of relations is the main purpose of a
visualisation.
Entity / Relation

Types of Data
Entities
... are generally objects of interest.
e.g. people, cars,... 
but groups too: trafﬁc jams
http://www.shutterstock.com/video/clip-476470-stock-footage-stand-and-wait-people-silhouette.html http://www.iconsfind.com/20140406/transport-traffic-jam-icons/

Types of Data
Entities

Types of Data
Relationships
... form the structures that relate entities.
e.g. "Part-of" relationship, structural, physical,
causal, temporal

Types of Data
Relationships

Types of Data
Part-of

Types of Data
Hierarchical

Types of Data
http://www.nytimes.com/interactive/2013/02/20/movies/among-the-oscar-contenders-a-host-of-connections.html?_r=0

Types of Data
Attributes of Entities or Relationships
... property of an entity and cannot be thought of
independently.
e.g. color of apple, duration of journey

Types of Data
Attributes of Entities or Relationships
... property of an entity and cannot be thought of
independently.
e.g. color of apple, duration of journey
How about the salary of an employee?

Types of Data
Data Dimensions: 1D, 2D, 3D,..
Attribute of an entity can have multiple dimensions.
Single scalar Weight of a person

Types of Data
Vector quantity Direction of person walking

Types of Data
Vector quantity Direction of person walking
Tensors Direction and shear forces

https://www.windyty.com/?48.137,13.975,4

Types of Numbers

Types of Numbers
https://en.wikipedia.org/wiki/Stanley_Smith_Stevens
Stanley Smith Stevens
American psychologist
„In 1946 he introduced a theory of
levels of measurement widely
used by scientists but criticized
by statisticians.“

Types of Numbers
Taxonomy of number scales by statistician Stevens (1946)
• Nominal
• Ordinal
• Interval
• Ratio
Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103, 677–680.

Types of Numbers
Nominal
Labeling function
Fruit can be classiﬁed into apples, bananas, oranges,…
No sense in which fruit can be ordered in a sequence.
Sometimes numbers are used this way (bus line)
„Rejected“, Don Hertzfeld, 2000

Types of Numbers
Ordinal
Numbers used to order things in a sequence.
The position of an item in a list is an ordinal quality.
Ranking items (e.g. itunes) in order of preference

Types of Numbers
Interval
Gap between data values
Time of departure and time of arrival of e.g. a train
Has no meaningful (absence) zero point (11:13 - 15:26)

Types of Numbers
Ratio
Full expressive power of a real number.
Statements: „Object A is twice as large as object B“
E.g. mass of an object, money,…
Use of ratio scale implies a zero value used as reference

Data „Add-ons“

Data „Add-ons“
Uncertainty
Common for science and engineering to attach uncertainty
attribute.
Estimating uncertainty is a major part of engineering practice.
Important to show uncertainty in a visualisation: 
Visual object suggests literal concrete quality, which
makes the viewer think it is accurate.

Data „Add-ons“
Metadata
… is data about data.
E.g. who collected it, which
transformations used,
uncertainty,..
Visualisation is challenging due to additional complexity.
image resource: http://house-co.com/blog/why-metadata-should-be-the-love-of-your-life/

Data „Add-ons“
Operations Considered as Data
• Mathematical operations on numbers

Data „Add-ons“
• Merging two lists

Data „Add-ons“
• Inverting a value to create opposite

Data „Add-ons“
• Bringing an entity or relationship to existence

Data „Add-ons“
• Deleting an entity or relationship

Data „Add-ons“
• Transforming an entity in some way (caterpillar turns into a butterﬂy)

Data „Add-ons“
• Forming a new object out of other object (a pie is baked from apples
and pastry)

Data „Add-ons“
• Forming a new object out of other object (a pie is baked from apples
and pastry)
• Splitting a single entity into its component parts (disassemble machine)

Hands-on #2a

Hands-on #2a - Pen & Paper
Short exercise ~15min
Take 3 operations of the list and try to sketch a visual (iconic)
representation of it.
http://cs-shop.de/explosionszeichnungen/C10127.htm

Data Aggregations

Data Aggregations
Factoid Series Multiseries
Summable
Multiseries
Summary
Records
Individual
Transaction

Data Aggregations
Summable
Multiseries
Summary
Records
Individual
Transaction
Limited ability to explore and pivot More options to explore and pivot

Data Aggregations
Level of Aggregation Number of metrics Description
Factoid Maximum context Single data point; No drill-down
Series One metric across an axis Can compare rate of change
Multiseries Several metrics, common axis
Can compare rate of change,
correlation between metrics
Summable mutliseries Several metrics, common axis
Can compare rate of chagne,
correlation between metrics; Can
compare percentages to whole
Summary records
One record for each item in a
series; Metrics in other series have
been aggregated somehow
Items can be compared
Individual transactions One record per instance
No aggregation or combination;
Maximum drill-down

Data Aggregations
Summary records
Maximum drill-down

Data Aggregations
Summable multiseries Several metrics, common axis
Summary records
Maximum drill-down

Data Aggregations
Summary records
Maximum drill-down

Data Aggregations
Factoid
A factoid is a piece of trivia. It is calculated from source
data, but chosen to emphasise a particular point.
„36.7% of coffee in 2000 was consumed by women“
Summable
Multiseries
Summary
Records
Individual
Transaction

Data Aggregations
Series
This is one type of information (the dependent variable)
compared to another (the independent variable). 
Often the independent variable is time.
0
17,5
35
52,5
70
April Mai Juni Juli
0
1,25
2,5
3,75
5
Peter Mary Charles Marty
Summable
Multiseries
Summary
Records
Individual
Transaction

Data Aggregations
Multiseries
A multiseries dataset has several dependent variables
and one independent.
0
22,5
45
67,5
90
April Mai Juni Juli
male female
Summable
Multiseries
Summary
Records
Individual
Transaction

Data Aggregations
Summable 
Multiseries
Multiseries which are subgroups are stacked to give an
impression of the overall sum.
0
37,5
75
112,5
150
April Mai Juni Juli
male female
Summable
Multiseries
Summary
Records
Individual
Transaction

Data Aggregations
Summary 
Records
Keeps dataset fairly small, suggests ways how to
explore data.
Summable
Multiseries
Summary
Records
Individual
Transaction
Name Gender Occurrance A Occurrance B Total
Mary F 5 9 14
Charles M 2 8 10
Marty M 3 2 5
Peter M 2 8 10
Sum 12 27 39

Data Aggregations
Individual 
Transactions
Transactional records capture things about a speciﬁc
event.
No aggregation of the data.
Summable
Multiseries
Summary
Records
Individual
Transaction

Data Aggregations
Individual 
Transactions
Summable
Multiseries
Summary
Records
Individual
Transaction
Timestamp Name Gender Type of Occurrance
13:00 Paul M A
13:14 Bob M A
14:34 Charly M B
14:55 Simon M A
15:23 Mary F B
15:25 Betty F A
16:11 Peter M B
17:01 Lisa F B
18:23 Betty F A
20:09 Mary F A

Hands-on #2b
Visit following websites for datasets you are interested in:
http://data.un.org
https://www.google.com/trends/
Try to find datasets which you could set in relation to explain a „theory“.
For example: alcohol deaths vs. weather trend
You are allowed to find most ridiculous datasets. The goal is to filter, aggregate
and visualize the data to make a statement which you support with the
visualization. Make us curious. So data first, attractive visual design is
secondary.
Use your available tools (excel, openoffice, google charts,…)
Keep in mind: simple bar charts, scatter plots,… are enough to tell the story. ->
Keep it simple and clear.
Upload a zip file, containing datasets and screenshots of charts. Add JS code if
used. Don’t forget to document progress.
http://www.targetmap.com/viewer.aspx?reportId=7830

Push conference
Audree Lapierre
@ffunction
http://itsmylife.cancer.ca
http://earthinsights.org
http://dataveyes.com/#!/en
@dataveyes
Caroline Goulard
http://audreelapierre.com/
http://dataveyes.com/#!/en/case-studies/identite-generative

Information Visualisation - Lecture 2

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