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Data Abstractionand Time-Series Data

CS 4390/5390 Data VisualizationShirley Moore, Instructor

September 15, 2014

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What – Why – How?

• Data abstraction is the what part of visualization design

• Why – task abstraction• How – visual encoding (e.g., marks, spatial

layout, color maps)• Goal: Understand dataset and datatype

characteristics so that we use appropriate visualization encodings and techniques

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Dataset Types

• Munzner Chapter 2– Tables– Networks– Fields– Geometry

• Another dataset type: unstructured text

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Tables• 2 dimensional with rows and columns

• Multidimensional

– Attribute values can also be multi-dimensional.

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Networks and Trees

• Network

• Tree (acyclic network)

Nodes and links can both have attributes.

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Fields• Positions with attributes• Realm of scientific visualizaiton• Types of grids:

• Considerations for continuous field data– sampling– interpolation

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Geometry

• Positions and items

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Attribute Data Types

For ordered data:

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Key vs. Value Attributes

• Key– Must be uniquely valued– Can be comprised of multiple attrivbutes– Can be implicit (e.g., row number)– Also called an independent variable

• Value– Need not be uniquely valued– Can be multidimensional data

• Scalar• Vector• Tensor

– Also called a dependent variable

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Temporal Data

• Values having to do with dates and times• Can be key or value attribute• Can have complex hierarchical structure

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Time Series Dataset

• Common type of dataset in which time is the independent variable

• Goal in visualization is to show changes and trends over time.

• Fry Chapter 4– Example: consumption of different beverages (milk,

coffee, tea) from 1910 to 2010• Lab 2 assignment– CO2 emissions since 1950 – total, by country, per capita