Create a graduated symbols map to show symbols of graduated sizes to indicate numeric values, where larger symbols represent larger values. Graduated symbols maps use the Counts and amounts (Size) smart mapping symbol type. Graduated symbols maps help answer questions about data, such as: Where is it located? Where is it biggest? Where is it smallest?
Graduated symbols make it easy to distinguish between low and high values, allowing you to show differences and make comparisons on a map. Adjust the size of the symbols to clarify the story you're telling.
Example
An insurance company is conducting an assessment to determine how many of its policies are within a storm surge area, and the associated risk. A graduated symbols map using the sum of total insured values (TIV) can be used to determine which storm surge areas have the highest value of policies.
The above graduated symbols map is the result of a spatial aggregation between the insurance policies and storm surge layers. The map indicates the highest TIV on the southern tip with the largest symbol.
Create a graduated symbols map
To create a graduated symbols map, complete the following steps:
- Expand a dataset in the data pane so that the fields are visible.
- Select a number field
.Tip:
You can search for fields using the search bar in the data pane.
- Drag the field to the page and drop it on the Map drop zone.
A graduated symbols map is created using Counts and amounts (Size) as the Symbol type.
A graduated symbols map is also created when you perform spatial aggregation.
Usage notes
Click the Flip card button 
to turn the map card over. The back of the card includes statistics and a text box for a description of the map.
The Layer options pane is accessible from the layer legend and can be used to view the classification values being mapped, change the style of the map, and view information about selected features.
Use the Legend tab 
to view the classification values of the graduated symbols map and make selections based on the values.
Use the Symbology tab 
to do the following:
- Change the field being displayed on the map or switch to a different type of map.
- Change the statistics for the display field. This option is only available if location was enabled on the dataset with aggregation allowed for identical features or if the dataset was created through spatial aggregation.
- Change the classification type.
- Change the number of classes being displayed.
- Change, add, or remove the Divide by field.
Use the Appearance tab 
to change the symbol style properties, such as symbol size, fill color, outline thickness and color, and layer transparency.
Use the Attributes tab 
to view details for features selected on the map.
How graduated symbols maps work
Graduated symbols maps use data classification to apply symbols to number ranges. The classification method that you use will depend on the data you're using and the information you want to convey on your map.
The following classification options are available for graduated symbols maps:
| Classification method | Description | Example |
|---|---|---|
Natural breaks | Classes are based on natural groupings inherent in the data. This is the default classification. The natural breaks method should be used when you want to emphasize the natural groupings inherent in your data. Natural breaks should not be used to compare maps created with different data. | Use natural breaks to compare the number of crimes in neighborhoods across a city. The crime totals will be grouped so that neighborhoods with similar crime totals are symbolized with the same symbol size. |
Equal interval | Divides the range of attribute values into equal-sized subranges. The equal interval classification emphasizes the amount of an attribute relative to other values and should be used for data that has familiar ranges. | Use equal intervals to compare the total sales at store branches. If you use four bins, the stores will be divided into 25 percent ranges. |
Quantile | Divides the attributes into bins with equal numbers of features. The quantile classification can distort the look of your map by placing similar values in different classes. Therefore, this classification method should be used on data that is relatively uniform. You can also use quantile classification as a method of visual ranking. | Use quantile intervals to compare the carbon emissions between countries for a given year. If your dataset includes the emissions from 100 countries and you apply 10 bins, you'll be able to distinguish between groups of carbon emitters (10 highest emitters, 10 lowest emitters, and so on), but not within groups. |
Standard deviation | Classifies a feature based on how much the feature's attributes vary from the mean. The standard deviation method works best on datasets that are normally distributed and for analyses where the mean or the distance from the mean is important. | Use standard deviation to compare the number of admissions at hospitals across the state. You can use the map to see where the hospitals with an average number of admissions are located, as well as the locations of hospitals one or two standard deviations above or below the mean admissions. |
Unclassed | Numeric data is displayed on a continuous scale, rather than in discrete classes. The unclassed method should be used when you want to see proportional changes in your data. | Use an unclassed color ramp to compare the carbon emissions between countries. Unlike with the quantile classification, this method will give you the ability to distinguish between all of the countries because each country will have a slightly different symbol size (for example, the top carbon emitter will have a slightly larger symbol than the second highest emitter). |
Manual | Manually add class breaks that are appropriate for your data. The manual method should be used when there are known ranges that must be applied to your data, such as when you want to create multiple maps with the same bins. | Use a manual classification to compare the number of vacant houses in neighborhoods across a city over time. The manual classification can be used to apply the same bins to both maps so that patterns and comparisons can be made without making false assumptions due to differences in the classification. |