divides the range of attribute values into equal sized sub ranges, allowing one to specify the number of intervals while the algorithm determines where the breaks should be

emphasizes the amount of an attribute value relative to other values

best applied to familiar data ranges such as percentages and temperature

GOOD: presenting information to a non-technical audience easier to interpret especially for familiar values like percentages

DISADVANTAGE: values may cluster in the histogram, may have many features in one class and none in another

each class contains an equal number of features

well suited to linearly (i.e. evenly) distributed data

similar features can be placed in adjacent classes, or features with widely different values can be put in the same class

distortion can be minimized by increasing the number of classes

GOOD: emphasizing relative position (e.g. which countries are in the top 20% for income)

DISADVANTAGE: features with similar values may end up in different classes, exaggerating their Differences reverse can also happen: wide range of values can end up in the same classes, minimizing differences

classes are based on natural groupings inherent in the data

identifies break points by picking the class breaks that best group similar values and maximizes differences between classes

boundaries are set where there are relatively big jumps in the data values

GOOD: mapping values that are not evenly distributed in the histogram

DISADVANTAGE: class ranges are tailored to one data set, so difficult to compare maps can be difficult to choose optimum number of classes especially if data are evenly distributed

creates class breaks based on class intervals that have geometric series

the algorithm minimizes the sum of squares of the number of elements in each class

ensures that each class range has approximately the same number of values with each class and the change between intervals is fairly consistent

GOOD: designed to accommodate continuous data creates a balance between highlighting changes in the middle values and the extreme values, thereby visually appealing and cartographically comprehensive

DISADVANTAGE: especially if data are evenly distributed

shows how much a feature’s attribute value varies from the mean

class breaks are created based on the calculated mean and the standard deviations from the mean

two-color ramp helps emphasize values above (shown in blue) and below (shown in red) the mean

GOOD: seeing which features are above or below an average value data has a normal distribution

DISADVANTAGE: map doesn’t show actual values, only how far values are from the mean very high or low values can skew the mean