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Classification methods

Release 9.2
Last modified June 26, 2008
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There are several different classification methods you can choose to organize your data when doing thematic mapping. These include equal interval, natural breaks, quantile, equal area, and standard deviation.
In the Equal Interval classification method, each class has an equal range of values; that is, the difference between the high and low value is equal for each class. You should use this method if your data is evenly distributed and you want to emphasize the difference in values between the features.
With the Natural Breaks classification method, data values that cluster are placed into a single class. Class breaks occur where there is a gap between clusters. You should use this method if your data is unevenly distributed; that is, many features have the same or similar values and there are gaps between groups of values.
With the Quantile classification method, each class has roughly the same number of features. If your data is evenly distributed and you want to emphasize the difference in relative position between features, you should use the quantile classification method. If, for example, the point values are divided into five classes, points in the highest class would fall into the top fifth of all points.
With the Geometrical Interval classification method, class ranges are based on intervals that have a geometric sequence based on a multiplier. It creates these intervals by minimizing the square sum of elements per class; this ensures that each interval has an appropriate number of values within it and the intervals are fairly similar. This algorithm was specifically designed to accommodate continuous data. It produces a result that is visually appealing and cartographically comprehensive.
With the Standard Deviation classification method, class breaks are placed above and below the mean value at intervals of 1, 0.5, or 0.25 standard deviations until all the data values are included in a class.

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