This is an archive related to a previous version of ArcGIS Maps for Office. If you need the current version go to http://doc.arcgis.com/en/maps-for-office/.

Classification methods

If you style your layer using Counts and Amounts (color or size), you decide how you want to classify your data and how to define the ranges and breaks for the classes. Depending on how much data you have in your layer, you can also decide the number of classes—one through ten. The more data you have, the more classes you can have. 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.

Equal interval

With the equal interval classification method, the range of all of your data values is divided into equal-sized subranges. With this classification method, you specify the number of intervals (or subranges), and ArcGIS Maps for Office automatically determines how to divide the data. For example, if you specify three classes for a field whose values range from 0 to 300, ArcGIS Maps for Office creates three classes with ranges of 0–100, 101–200, and 201–300.

The equal interval classification 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 shows that a store is part of the group of stores that make up the top one-third of all sales.

Natural breaks

Natural breaks classes are based on natural groupings inherent in the data. Class breaks that best group similar values and that maximize the differences between classes are identified. The features are divided into classes whose boundaries are set where there are relatively big differences in the data values.

Natural breaks classification is best used for mapping data values that are not evenly distributed, but instead tend to cluster into groups as natural breaks places clustered values in the same class.

Standard deviation

Standard deviation shows you how much a feature's attribute value varies from the mean, also known as the average. It helps emphasize values above the mean and values below the mean, showing which features are above or below an average value. Use this classification when knowing how values relate to the mean is important, such as when looking at population density in a given area, or comparing foreclosure rates across the country. For greater detail in your map, you can change the class size from 1 standard deviation to .5 standard deviation.

Standard deviation applies only to feature layers.

Quantile

In quantile classification, each class contains an equal number of features (for example, 10 per class or 20 per class). A quantile classification is well suited to linearly (evenly) distributed data. It is useful when you want to emphasize the relative position of a feature among other features, for example, to show that a store is in the top quarter of all stores by sales. Quantile classification 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.

Manual breaks

If you want to define your own classes, you can manually add class breaks and set class ranges that are appropriate for your data. Alternatively, you can start with one of the standard classifications and make adjustments as needed. There may already be certain standards or guidelines for mapping your data. For example, temperature maps are often displayed with 10-degree temperature bands, or you might want to emphasize features with particular values, such as those above or below a threshold value.