Data Visualization

Guide for technologies, techniques, and best practices for data visualization.

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Peter Lawson
Data and Visualization Librarian
 
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Context: Asking the Right Question

Planning a data visualization should always begin with a series of questions designed to better understand your audience, what you are trying to communicate, and how you intend to do so with visualization.
Who is your target audience?
It is important to understand what your audience knowledge and expertise level is. A data visualization for a publication in your field will be different than one for a general lay audience.
What are you trying to show?
What message are you trying to communicate with your data? What variables in your data are you trying to show? What relationship between those variables is important? This might be a correlation between variables, rankings of variables, distributions of variables, etc. The Understanding Your Data section of this guide can help you better understand aspects of your data and how those can best represented visually.
How do you intend to show it?
What is the appropriate visualization type for your data and what you are trying to communicate? The Choosing an Appropriate Visualization Type section of this guide can help point you towards the right visualization for your data.

Understanding Your Data

What can we visualize?
 

When we visualize data we must convert data values into visual elements that comprise a graphic. To choose the appropriate visualization for your data it is important to first understand what kind of dataset you are working with. Common dataset types include:
Dataset Types What Can Be Visualized Examples
Table Items - Tabular data (.tsv, .csv),
- Spreadsheet data (.xls, .xlsx)
Attributes
Networks Items (Nodes or Vertices) - Phylogenetic trees,
- social network analysis,
- FlowingData Network Visualizations
Links (Links or Edges)
Attributes
Fields
 
Grids

- Simulations from fluid or solid mechanics,
- astronomy, weather and climate data
- data from geology and geophysics,
- biomedical image data,
- molecular data


 

Positions
Attributes
Spatial (Geometry) Position

- Geographic information systems (GIS),
- spatial point processes

Table content and images adapted from: Munzner, Tamara. Visualization analysis & design. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2015. Print.
Scientific Visualization examples adapted from: Ozsu, M. Tamer, and Ling Liu. Encyclopedia of Database Systems. Springer, 2009.

Selecting and Designing an Appropriate Visualization

How do I visualize my data?
 

Choice of visualization technique, framework, and design depend on the type of data being visualized. Select the guide specific to the dataset you are trying to visualize. If you would like to learn about data visualization in general see Designing Effective Data Visualizations.
Dataset Type Guide
Table                       See the Designing Effective Data Visualizations section of the Libguide which details best practices, techniques, and tools for information visualization. This is the most common data visualization type, and a recommended starting point for individuals new to data visualization.
Network For visualization of networks consult the section on Network Visualization.
Field For visualization of scalar, vector, and tensor fields, consult the section on Scientific Visualization.
Spatial For geographic information visualization (GIS), please refer to our separate Libguide: GIS and Maps.