Tips on Classifying Data

This section provides definitions and tips to help you optimize your data classification. The Classification Method and # of classes that you chose for your data can affect the outcome of your analysis.

Classification Method

There are four types of classification methods:

Natural Breaks

  • Divides data into classes based on natural breaks in the data distribution

  • Calculates groupings of data values based on data distribution and seeks to reduce variance within groups and maximize variance between groups

  • Limitation: Class ranges are specific to the data set, so it is difficult to compare a map with another map and to choose the optimum # of classes, especially if the data is evenly distributed

Distinct

  • The only classification method for fields containing text

  • Creates a separate class for each unique value in the data set

  • Limitation: if you have a large number of unique values in your data set, your thematic map will be difficult to interpret due to too many different symbols/colors

Quantile

  • Divides a data set that divide into "n" groups, containing (to the extent possible) equal numbers of observations

  • Desirable in that this method produces distinct map patterns and minimizes the impact of skewed data

  • Highlights changes in the middle distribution values because the intervals are generally wider at the extremes

  • Limitation: areas with similar values can end up in different classes while areas with very different values can end up in the same class

Equal Interval

  • Simplest method for classifying data

  • Divides a set of data values into groups each of which represents the same range of values

  • Better communicates trends with continuous sets of data. Works well with familiar data ranges such as percentages or temperature.

  • Limitation: not good for non-normally distributed data sets because you might get a map with many features in 1 or 2 classes and some classes with no features

# of Classes

It is the best practice to select 5 to 7 classes for a step ramp. If you select too many classes for a step ramp, it may be difficult to gain insight from the color style on the map. If you would like to visually differentiate a wide range of values, use a smooth color ramp.