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Summer Undergraduate Research Program (SURP)

Workshop Activities & Presentation

What is a Data Visualization?

The graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualizations provide an accessible way to see and understand trends, outliers, and patterns in data.​ From Tableau.

Principles of Data Visualizations

Design Principles: Color


(Color Name)






(External factors)

Useful for comparing categories that are not ordered Useful for ordered relationships Useful for ordered relationships Consider the cultural, social, and political context


Resources for Accessible Color Design 


Design Principles: Gestalt

“Gestalt Principles are principles/laws of human perception that describe how humans group similar elements, recognize patterns and simplify complex images when we perceive objects.” Interaction Design Foundation

List of Gestalt principles

Image from


Resources for Gestalt Principles

Forms of Data Visualizations

Choosing the appropriate type of visualization suited to your data can improve the readability and communication of your message. Consider the following when choosing a type:

  • Different visualization forms have different functions​

  • Different types of data need different visualization forms


Common Types of Visualizations

Bar Chart

  • Best for categorical data or group-based data​

    • Tip: intentionally order bars

Line Graph

  • Best for showing value changes over time​

  • Comparing lots of data at same time​

  • Forecasting data

Pie Chart

  • Used for part-to-whole relationships​

  • Conveying a segment as relatively small or large ​

    • Note: exact comparisons are difficult


  • Similar to a bar chart​

  • Best for grouping numbers into ranges


  • Useful for showing geographically based datasets​

  • Multiple types of maps, with common types including:​

    • Bubble Maps​ (displays location and population)

    • Dot Maps​ (displays location)


Resources for Selecting Visualization Type

Evaluating Data Visualizations for Accuracy

Use the Digital Image Guide (DIG) Method to help you spot misleading data visualizations.

  1. Review and describe the image. ​

  2. Review the text.​

  3. React to the image.

  1. Determine the source (creator, publisher, and/or website) of the image.​

  2. Determine the message of the image. ​

  3. Determine the context (social, political, cultural) of the image.

  1. Think back to your first reaction of the image.​

  2. Assess the reliability and accuracy of the image.​

  3. Refer to the other sources/websites that have shared or used the image.

  1. Form your own judgement on the image. ​

  2. Assess your biases or point of views and how it may impact how you view the image. ​

  3. Determine the purpose of the image.

Adapted from

Tools for Creating Data Visualizations

Tableau - Visual analytics platform


Excel - Spreadsheet software program and data visualization tool


R - Programming language and free software for statistical computing and graphics