Graduated symbols are used to show a quantitative difference between mapped features by varying the size of symbols. Data is classified into ranges that are each then assigned a symbol size to represent the range. For instance, if your classification scheme has five classes, five different symbol sizes are assigned. The color of the symbols stays the same.
Symbol size is an effective way to represent differences in magnitude of a phenomenon because larger symbols are naturally associated with meaning a greater amount of something. Using graduated symbols gives you a good degree of control over the size of each symbol, because they are not related directly to data values as they are with proportional symbols. This means you can design a set of symbols that have sufficient variation in the size that represents each class of data to make them distinguishable from one another.
Graduated symbols can be based on an attribute field in the dataset, or you can write an Arcade expression to generate numeric values to symbolize on.
When graduated symbology is based on a single field, the symbols are drawn in a sorted order where the larger features draw first and the smaller features draw above. When the symbology is based on an expression, this sorting does not occur, and some smaller symbols might be obscured by larger ones.
- Select a feature layer in the Contents pane.
- On the Appearance tab, in the Drawing group, click Symbology and click Graduated Symbols.
The Symbology pane appears.
- On the Symbology pane, choose the numeric field for the data to be mapped, or write an expression.
To use an expression, click to open the Expression Builder dialog box. Write an expression and click Verify to validate it. Note that although an expression is valid, it may not return a valid numeric value. You can filter the Expression Builder dialog box to show only numeric fields to help prevent this.
- To normalize the data, choose a field from the Normalization menu, or choose percentage of total to divide the data value to create ratios, or choose log to symbolize on the logarithm of each value. This can be an effective way to generate a smaller range of values if the dataset includes significant outliers. Normalization is available only when the graduated symbology is based on a field. If it is symbolized on an expression, the Normalization field is disabled.
- Classify the data using an appropriate classification method and number of classes.
- Set the minimum and maximum sizes of the symbol representing your data.
Modify graduated symbology
- To refine the classification, you can edit the classification breaks manually.
- To remove a classification break, right-click the Value cell and click Remove break(s).
- To edit a symbol, click the symbol in the Symbol cell to open the Format Symbol pane.
- To edit a label, right-click the text in the Label cell and click Edit label.
- To format the labels, click and click Advanced. Expand Format Labels.
- To change the maximum sample size, click and click Advanced. Expand Sample Size and modify the Maximum sample size value. This is the maximum number of records considered when the data is classified. Limiting the sample size you improve performance, but may inadvertently omit important outliers in the dataset. Generally, the larger the dataset, the larger the sample size you should use.
- To exclude data values from the symbology scheme and optionally define an alternate symbol for excluded values, click and click Advanced. Expand Data Exclusion and define the query. Click to return to the main Symbology pane. To stop showing excluded values, click More, and uncheck Show excluded values.
- To show values that are out of range (either because they were newly added, fall in removed classes, or contain null values), click More, and click Show values out of range.
Modify class breaks with the histogram
The histogram view of the classification breaks offers a visual tool for editing the classes and understanding how the data is represented by different classification methods.
- The gray bars of the histogram represent the distribution of the data. The value stops along the side show how the current classification method applies to the data distribution.
- The histogram for graduated symbols does not display the actual size of your symbols, but rather their size relative to one another as a visual guide.
- To view the distribution and class breaks more easily, you can drag the expander bar above the histogram upward to make it larger in the pane.
Any dynamic edits made to the histogram will switch the classification method to Manual.
Vary graduated symbology by transparency, rotation, or color
In addition to specifying the magnitude of features with graduated symbology, you can also symbolize additional attributes by varying the transparency, rotation, and color of the graduated symbols. While all of these treatments can be applied simultaneously, be aware that too many visual variations make the layer very difficult to interpret. It is advisable to apply secondary symbology sparingly.
- On the Symbology pane, click and click Vary symbology by attribute.
- Expand Transparency, Rotation, or Color.