Data visualization is an evolving discipline. Over the past decades several schools of thought developed perspectives on when and how to use visuals to analyze data. Trying to decide which type works for your goals, or the data you have, can be tough. People see dozens of common types of data visualizations each day. Some are beautiful but provide little insight. Some are functional, allowing the viewer to draw conclusions at a glance, but not aesthetic. A few need little work to make, while the creator must prepare the data for most to be effective. Beyond the fifty or so common types scores of obscure ones exist. The creator's goal, as well as the structure and size of the underlying data, dictates when to use one type of visualization over another.
This Data Visualization Glossary explores many of the common types of visualizations. Reading it will help you make informed decisions when building visualizations. Many topics and types of visualizations are not covered in it right now. It will grow and evolve over time as the discipline does.
Families of visualizations
A visualization’s Family speaks to its nature. This allows for hierarchical classification between major groups: Charts, Geospatial Visualizations, and Tables. Many schools of thought on visualization categorize families along different lines. It's most common to break Charts into independent subcategories. We chose three to simplify the glossary and elevate the importance of Analytical Function and Mark Type.
Charts include graphs, plots, and some diagrams focused on quantitative data mappable to cartesian coordinates.
Geospatial visualizations depict the relationship between multiple points on a map through marks, lines, and colors.
Using data visualizations for analytics
The Analytical Function describes how people interpret, explore, and understand the data within it. Many visualizations have more than one Analytical Function. These often depend on how the creator implements them. We broke out the functions with a significant differentiator into their own page. The six Analytical Functions below cover many ways a visualization explores data but the list is not comprehensive.
Highlights the spread, center, and shape of data within a population.
The correlation, magnitude, or rankings between dimensions in a data set.
Change over Time
Highlights how a measure changes on an axis over several intervals.
Explores the path between origin and destination.
Exposes patterns related to position and location in data.
Explores how much of the total population one measure takes up.
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The importance of choosing the right type of visualization
Of the considerations you make, knowing the needs of your audience is most important. Good visualizations allow people to gain insight from complex data at a glance. They highlight the relationships between measures. They explain concepts. And they engage the mind in ways looking at the raw data will not. Yet, using the wrong visualization will leave the viewer in a state of confusion or indifference. Be aware of your audience’s needs to ensure your visualization is effective.
Declaring a specific purpose helps you use the audience’s needs to narrow down your choices. Some data visualizations help perform analysis while others present information in a beautiful way. Some depict concepts, processes, or strategies in a digestible manner. Every purpose is best served by a specific set of visualizations. Consider the Analytical Function and Mark Type of your visualization before you build. This makes sure you are capable of achieving your purpose.
Know your data well to understand what types of visualizations it can support. Certain charts support limited dimensionality. Others excel at highlighting the relationships between many categories. You can use the structure and nature of the data you have to guide you towards making a final selection. When in doubt, experiment. Then you can create a visualization balancing your purpose with the data you have and the needs of your audience.