Many resources classify geospatial visualizations as a subgroup of Charts. This glossary considers them separate. This makes it easier to explore and discover new ways of performing visual analytics. We do not intend to create a new taxonomy through this approach. Check out the examples below and the information on what makes Geospatial Visualizations so important to analyzing data to learn more.

Inspiring examples of geospatial visualizations from the community

We’ve selected a few visualizations from the Tableau community to highlight how beautifully data can be portrayed. For more practical examples check out the different Analytical Functions or individual visualization types.

What is a geospatial visualization or geovisualization?

These visualizations focus on the relationship between data and its location to create insight. Any positional data works for spatial analysis. What makes geospatial visualizations unique is the scale. A diagram of circuits on a microchip explores position, but it is not geospatial. It does not map to Earth or another planetary body. A map of the stars is also not considered geospatial, but a map of the surface of Mars is. Geovisualization overlays variables on a map using latitude and longitude to foster insight.

Maps are the primary focus of geospatial visualizations. They range from depicting a street, town, or park or subdivisions to showing the boundaries of a country, continent, or the whole planet. They act as a container for extra data. This allows you to create context using shapes and color to change the visual focus. They help identify problems, track change, understand trends, and perform forecasting related to specific places and times.

Geospatial visualizations highlight the physical connection between data points. This makes them susceptible to a few common pitfalls that may introduce error:

  • Scaling - Changes in the size of the map can affect how the viewer interprets the data
  • Auto-correlation - A view may create an association between data points appearing close on a map, even for unrelated data

Keep in mind your goal and the common pitfalls to help determine if a geospatial visualization is the right choice for your data.

Types of geospatial visualizations

The table below contains a tagline for some common types of Geospatial Visualizations. As the glossary expands in depth and breadth more types will be added and each will have a page dedicated to showing practical examples and explaining when to use them.

Proportional Symbol Maps

Show quantitative data for individual coordinates using size.

Choropoleth Maps

Filled maps for showing ratio and rate data in defined areas.

Point Distribution Maps

Shows approximate location to highlight visual clusters in data.

Heatmaps or Density Maps

Highlights trends by showing the frequency of occurrences.

Flow Maps

Connects paths across a map to highlight changes over time.

Topographic Maps

Shows the elevation of features on a map through contours.

Isopleth/Isoline

Shows a range of quantitative data overlaid on a map.

Spider Maps

Highlights interactions between origin and destination points.

Cartogram

Distorts one aspect of a map to accentuate the key data.

Choosing when to use a geospatial visualization

Geospatial visualizations work best when they address a question specific to spatial analysis. Location and position should be central to the investigation. A few examples:

  • What countries suffer the most earthquakes?
  • Which areas of the country get the most rain?
  • What airport sees the most traffic during the holiday season?
  • Where does a bird migrate over the course of a year?
  • What is the fastest way from point A to point B during rush hour?

You can investigate these questions by overlaying data on a map, but that may not be the best way to find the answers. Many questions about location or position do not need geospatial visualization. Data with similar quantitative measures can be hard to compare them when displayed on a map. Scale can also conflate data within the visualization if locations are close to one another. It’s best to consider whether the question requires spatial analysis create insight.

If the data does not lend itself to easy interpretation, both clear and not misleading, then a map may lead to error and confusion. When you can answer the question faster or easier with another type of visualization—a bar graph or a line graph—then you should use those. Only use a geospatial visualization when the data lends itself to spatial analysis and you can display it clearly.

For more information on when and how to use geospatial visualizations read this Help Article.

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