1. Data Abstraction and Time-Series Data CS 4390/5390 Data Visualization Shirley Moore, Instructor September 15, 2014 1

2. 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 2

3. Dataset Types Munzner Chapter 2 – Tables – Networks – Fields – Geometry Another dataset type: unstructured text 3

4. Tables 2 dimensional with rows and columns Multidimensional – Attribute values can also be multi-dimensional. 4

5. Networks and Trees Network Tree (acyclic network) Nodes and links can both have attributes. 5

6. Fields Positions with attributes Realm of scientific visualizaiton Types of grids: Considerations for continuous field data – sampling – interpolation 6

7. Geometry Positions and items 7

8. Attribute Data Types 8 For ordered data :

9. 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 9

10. Temporal Data Values having to do with dates and times Can be key or value attribute Can have complex hierarchical structure 10

11. 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 – CO 2 emissions since 1950 – total, by country, per capita 11