In this session you will be introduced to:

1. The purpose of data visualization
2. A framework of elements of data visualization
3. Basic types of visualization
4. How to choose the right visualization depending on:
   • Variable type
   • Amount of variables
   • Types of properties/relationships to be highlighted

Daniel

Purpose of Visualization

• Explore properties of the data
• Reveal insights to be found in the data
• Create data-narratives
• …

Q: What is wrong with this data visualization?

Borner, K. (2015). Atlas of knowledge: Anyone can map MIT Press.

Q1: What insight do I want to gain/communicate with this visualization?

• Distribution?
• Composition?
• Cluster?
• Trends (over time)?
• Position in space?
• Correlation, relationships?
• Statistical properties?

Different scales….

• … allow to ask different questions
• … require different means of presentation

Often, we do not only look at raw data, but aim at visualizing result of an analysis. Again, different analysis offer/require different forms of visualization.

• When: Temporal Analysis / Timeseries
• Where: Geospatial Analysis
• What: Topical Data Analysis
• Why: Inferential Statistics
• With whom: Network Analysis

• Often, the position in 2-dimensional space represent the first ways to map information in th data.
• We refer to this 2d mapping as the choice of reference system.

The shape of elements plotted in the reference system represent another dimension to communicate (discrete) data properties. Eg.

• Points
• Lines
• Linguistic symbols

Graphical variables allow further dimensions to communicate (discrete or contineous) data properties

In combinations, shapes and variable mappings allow for multiple types of information expressed jointly.

Interactive visualizations allow for:

• communicating more dynamic and complex properties/relationships
• Allow own insight-creation by exploration

The combination of…

• … graphical symbols …
• … other graphical variables …
• … on a reference system…

… allow us to represent a multitude of information to be found in the data or as result of an analysis visually.

Scatterplot (2 variables)

Reference system:

• y = Value Variable y
• x = Value variable x
• Symbol = Point

Scatterplot (3 variables, 2c1d)

Reference system:

• y = Value Variable y
• x = Value variable x
• Symbol = Point
• Variable: Color (Species)

Scatterplot (3 variables, 3c)

Reference system:

• y = Value Variable y
• x = Value variable x
• Symbol = Point
• Variable: Color (Petal.Lenght)

Scatterplot (4 variables, 3c,1d)

Reference system:

• y = Value Variable y
• x = Value variable x
• Symbol = Point
• Variable: Color (Petal.Lenght), Shape (Species)

Facet Matrix (4 variables, 2c,1d)

Reference system:

• y = Value Variable y
• x = Value variable x
• Y-Facet: Species

Boxplot (Univariate distribution of multiple variables)

Reference system:

• y = Value Variable
• x = Value variable x
• Symbol = Confidence Interval Box

Correlation Matrix (bivariate distribution of multiple variables)

Reference system:

• y = Variable
• x = Variable
• Variable: Color (Correlation)

Correlation Matrix (bivariate distribution of multiple variables)

Examples ar manifold. Just to give you one:

• Data visualization is of high importance for data exploration, insight generation & communication
• Depending on the purpose of th visualization, different types have to be chosen.
• Variable characteristics influence the possibilities of visual mapping.
• Depending on type & amount of relationships to be depicted, different visualization devices can b utulized.
• Common mapping elements are: Reference position (x, y), color, shape, alpha, facet