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Graduated colors

Graduated color symbology is used to show a quantitative difference between mapped features by varying the color of symbols. Data is classified into ranges that are each assigned a different color from a color scheme to represent the range. For instance, if your classification scheme has five classes, five different symbol colors are assigned. The size of the symbols stays the same. Maps that vary in color this way are usually called choropleth maps. Typically, you choose a continuous color scheme to apply different shades of the same color so that lighter shades match lower data values and darker shades match higher data values.

Symbol color is an effective way to represent differences in magnitude of a phenomenon because it is easy to distinguish variations in color if there are relatively few classes. A range of seven colors is the approximate upper limit of colors that can be easily distinguished on a map. Avoid using too many classes, especially if you are using light colors. Although symbol color is applied from a color scheme, you can modify the color of each symbol class. This means you can design a custom set of colors that have sufficient variation to make them distinguishable from one another.

When classifying data, your data may vary around a specific value that is important to maintain, such as a median value or any other significant threshold. For instance, data showing positive and negative change may vary around a value of 0. Rather than use a continuous color scheme with a mid-range color applied to the values near 0, you want to clearly highlight that 0 is an inflection point in the data distribution. You can do this by adding a critical break to that symbol class and then applying a diverging color scheme. This forces the upper end of one class and the lower end of the next class to use the critical break value.

Graduated color symbology can be based on an attribute field in the dataset, or you can write an Arcade expression to generate numeric values on which to symbolize.

The Primary symbology tab Primary symbology has three subtabs to establish graduated color symbology:

  • The Classes tab is where you manage the symbol, the values, the descriptive labels, and grouping of the symbol classes.
  • The Histogram tab is where you view and edit the data ranges of the symbol classes.
  • The Scales tab is where you specify the scale ranges in which each symbol class draws.

  1. Select a feature layer in the Contents pane. On the Appearance tab, in the Drawing group, click Symbology and click Graduated Colors to open the Symbology pane.
  2. In the Symbology pane, on the Primary symbology tab Primary symbology, choose the numeric field for the data to be mapped.
  3. Optionally click the expression button Set an expression to open the Expression Builder dialog box. Write an expression and click Verify Validate to validate it. Note that although an expression is valid, it may not return a valid numeric value. You can use the filter button Filter on the Expression Builder dialog box to show only numeric fields to help prevent this.
  4. To normalize the data, choose a field from the Normalization menu, 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 color symbology is based on a field. If it is symbolized on an expression, the Normalization field is disabled.
  5. Classify the data using an appropriate classification method and number of classes.
  6. Choose a color scheme. The classification method and number of classes in the layer dictate which color schemes from core styles appear in the list. For example, if you classify your data by standard deviation, diverging color schemes are available. If you show five classes, only continuous color schemes and color schemes with five classes are shown. Color schemes stored in your Favorites style or custom styles always appear in this list, regardless of their type or number of colors.

Modify graduated color symbology

From the Primary symbology tab Primary symbology, on the Classes tab you can do the following:

  • To refine the classification, you can edit the Upper value of each classification manually by typing new values.
  • To set a critical break around a central, important value, right-click the symbol class and click Set as critical break Color Scheme. Choose a diverging color scheme to highlight the central value. To remove a critical break, right-click the symbol class and click Remove critical break Remove Critical Break. To remove a classification break, right-click the Upper value cell and click Remove Remove.
  • 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.
  • 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.

From the Advanced symbol options tab Advanced symbol options you can do the following:

  • To format the labels, expand Format labels.
  • To change the maximum sample size, expand Sample size. 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 set up masking per feature, expand Feature level masking.
  • To exclude data values from the symbology scheme and optionally define an alternate symbol for excluded values, expand Data exclusion to define the query. To stop showing excluded values, on the Primary symbology tab Primary symbology, click More, and uncheck Show excluded values.

Modify class breaks with a histogram

The histogram offers a visual tool for editing the classes and understanding how the data is represented by different classification methods. Access it by clicking the Histogram tab on the Primary symbology tab Primary symbology.

  • 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.
  • 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.
Note:

Any dynamic edits made to the histogram will switch the classification method to Manual.

Vary graduated color symbology by transparency, rotation, or size

In addition to specifying the magnitude of features with graduated color symbology, you can also symbolize additional attributes by varying the transparency, rotation, and size of the graduated color symbols. While all of these treatments can be applied simultaneously, be aware that too many visual variations make the layer difficult to interpret. It is advisable to apply secondary symbology sparingly.

Learn more about symbolization concepts

  1. On the Symbology pane, click the Vary symbology by attribute tab Vary symbology by attribute.
  2. Expand Transparency, Rotation, or Size. In the case of polygon features, expand Outline width replaces Size and Rotation is not available..

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