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.“

https://en.wikipedia.org/wiki/Jacques_Bertin


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



Single scalar Weight of a personVector quantity Direction of person walking


Types of Data



Single scalar Weight of a personVector quantity Direction of person walkingTensors 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 StevensAmerican psychologist

„In 1946 he introduced a theory of levels of measurement widely used by scientists but criticized by statisticians.“

https://de.wikipedia.org/wiki/Stanley_Smith_Stevens

https://en.wikipedia.org/wiki/Level_of_measurement


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

IntervalGap between data valuesTime of departure and time of arrival of e.g. a trainHas 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/

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“

Operations Considered as Data• Mathematical operations on numbers • Merging two lists


Data „Add-ons“

Operations Considered as Data• Mathematical operations on numbers • Merging two lists • Inverting a value to create opposite


Data „Add-ons“

Operations Considered as Data• Mathematical operations on numbers • Merging two lists • Inverting a value to create opposite • Bringing an entity or relationship to existence


Data „Add-ons“

Operations Considered as Data• Mathematical operations on numbers • Merging two lists • Inverting a value to create opposite • Bringing an entity or relationship to existence • Deleting an entity or relationship


Data „Add-ons“

Operations Considered as Data• Mathematical operations on numbers • Merging two lists • Inverting a value to create opposite • Bringing an entity or relationship to existence • Deleting an entity or relationship • Transforming an entity in some way (caterpillar turns into a butterfly)


Data „Add-ons“

Operations Considered as Data• Mathematical operations on numbers • Merging two lists • Inverting a value to create opposite • Bringing an entity or relationship to existence • Deleting an entity or relationship • Transforming an entity in some way (caterpillar turns into a butterfly) • Forming a new object out of other object (a pie is baked from apples

and pastry)


Data „Add-ons“

Operations Considered as Data• Mathematical operations on numbers • Merging two lists • Inverting a value to create opposite • Bringing an entity or relationship to existence • Deleting an entity or relationship • Transforming an entity in some way (caterpillar turns into a butterfly) • 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

http://cs-shop.de/explosionszeichnungen/C10127.htm


Data Aggregations


Data Aggregations

Factoid Series Multiseries SummableMultiseries

SummaryRecords

IndividualTransaction


Data Aggregations


SummaryRecords


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 axisCan compare rate of chagne,

correlation between metrics; Can compare percentages to whole

Summary recordsOne 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












Data Aggregations





Summable multiseries Several metrics, common axisCan compare rate of chagne,







Data Aggregations












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“


SummaryRecords



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 Juli0

1,25

2,5

3,75

5

Peter Mary Charles Marty


SummaryRecords



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


SummaryRecords



Data AggregationsSummable 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


SummaryRecords



Data AggregationsSummary Records

Keeps dataset fairly small, suggests ways how to explore data.


SummaryRecords


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 AggregationsIndividualTransactions

Transactional records capture things about a specific event. No aggregation of the data.


SummaryRecords



Data AggregationsIndividualTransactions


SummaryRecords


Timestamp Name Gender Type of Occurrance13:00 Paul M A13:14 Bob M A14:34 Charly M B14:55 Simon M A15:23 Mary F B15:25 Betty F A16:11 Peter M B17:01 Lisa F B18:23 Betty F A20:09 Mary F A


Hands-on #2bVisit 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

http://data.un.org

https://www.google.com/trends/




Push conferenceAudree Lapierre@ffunctionhttp://itsmylife.cancer.cahttp://earthinsights.org

http://dataveyes.com/#!/en@dataveyesCaroline Goulard

http://audreelapierre.com/

http://dataveyes.com/#!/en/case-studies/identite-generative

http://itsmylife.cancer.ca

http://earthinsights.org

http://dataveyes.com/#!/en

http://audreelapierre.com/

http://dataveyes.com/#!/en/case-studies/identite-generative

information visualisation - lecture 2

Data & Analytics