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Classification methods <br />You can explore your data by applying the different classification techniques found in the <br />Graduated Color and Graduated Symbol Legend Editors or by typing in your own classes. The <br />purpose of classification is twofold: to make the process of reading and understanding a map <br />easier and to show something about the area you're mapping that is not self- evident. Try each of <br />the classification types and see if any interesting spatial patterns appear. <br />ArcView provides six classification methods to display data: <br />Natural Breaks <br />This is the default classification method in ArcView. This method identifies breakpoints between <br />classes using a statistical formula ( Jenk's optimization). This method is rather complex, but <br />basically the Jenk's method minimizes the sum of the variance within each of the classes. Natural <br />Breaks finds groupings and patterns inherent in your data. <br />Quantile <br />In the quantile classification method, each class contains the same number of features. Quantile <br />classes are perhaps the easiest to understand, but they can be misleading. Population counts (as <br />opposed to density or percentage), for example, are usually not suitable for quantile classification <br />because only a few places are highly populated. You can overcome this distortion by increasing <br />the number of classes. Imagine the difference, for example, if five classes are used in the chart <br />instead of three. Quantiles are best suited for data that is linearly distributed; in other words, data <br />that does not have disproportionate numbers of features with similar values. <br />Equal Area <br />This method classifies polygon features by finding breakpoints so that the total area of the <br />polygons in each class is the approximately the same. (ArcView determines the total area of the <br />features that have valid data values.) Classes determined with the equal area method are typically <br />very similar to Quantile classes when the sizes of all the features are roughly the same. Equal Area <br />will differ from Quantile if the features are of vastly different areas. <br />Equal Interval <br />The equal interval method divides the range of attribute values into equal sized sub - ranges. Then <br />the features are classified based on those sub - ranges. <br />Standard Deviations <br />When you classify data using the standard deviations method, ArcView finds the mean value and <br />then places class breaks above and below the mean at intervals of either 1/4, 1/2, or 1 standard <br />deviations until all the data values are contained within the classes. ArcView will aggregate any <br />values that are beyond three standard deviations from the mean into two classes, greater than <br />three standard deviations above the mean (" > 3 Std Dev. ") and less than three standard <br />deviations below the mean V< -3 Std. Dev. "►. <br />0 Related topics <br />