About classifying attributes

You can choose different ways to display the data you've added to your map. For example, water bodies and streams might be shown with a single, constant blue color. Roads might be symbolized based on road class. Seismic events, such as earthquakes, might be represented using graduated symbols based on their magnitude. And polygons might be classified based on land use. The map viewer includes several options for displaying your data. Only the options that apply to your data appear. For example, if you have one value for each type of feature, you can only use a single symbol or unique symbols (not size or color).

Display options

Single symbol

Display all features using a single symbol. Drawing your data with just a single symbol gives you a sense of how features are distributed—whether they're clustered or dispersed—and may reveal hidden patterns. For example, mapping a list of restaurant locations, you would likely see that the restaurants are clustered together in a business district.

Unique symbols

Display features using a particular characteristic that identifies them. For example, in the restaurant example, you could use different colors to represent the cuisine the restaurant serves. An attribute can have up to 200 unique values to use this display option.

Color

Display features based on a single color gradient that distinguishes the differences between the features. You pick the classification scheme and the number of classes. For example, you could use a color ramp of restaurant revenue to gauge the potential earnings of each location.

Size

Display features based on a single symbol of varying size distinguishes the differences between the features. You pick the classification scheme and the number of classes. For example, you could use a money symbol of different sizes to show the relative profitability of the restaurants.

Classification scheme and number of classes

If you classify your features using color or size, you decide how to define the ranges and breaks for the classes. You also decide the number of classes—one through ten. How you define the class ranges and breaks—the high and low values that bracket each class—determines which features fall into each class and what the layer looks like. By changing the classes, you can create very different-looking maps. Generally, the goal is to make sure features with similar values are in the same class.

You can choose from four standard classification schemes.

Equal interval

Equal interval divides the range of attribute values into equal-sized subranges. Equal interval is best applied to familiar data ranges such as percentages and temperature. This method emphasizes the amount of an attribute value relative to other values. For example, it could show that a store is part of the group of stores that make up the top one-third of all sales.

Natural breaks

Natural breaks are based on natural groupings inherent in the data that maximizes the differences between classes, for example, tree height in a national forest.

Standard deviation

Standard deviation shows you how much a feature's attribute value varies from the mean. Standard deviation helps emphasize values above the mean and values below the mean, for example, foreclosure rates.

Quantile

With quantile breaks, each class contains an equal number of features. A quantile classification is well suited to linearly distributed data. Quantile assigns the same number of data values to each class. There are no empty classes or classes with too few or too many values. Because features are grouped in equal numbers in each class using quantile classification, the resulting map can often be misleading. Similar features can be placed in adjacent classes, or features with widely different values can be put in the same class. You can minimize this distortion by increasing the number of classes.