Classifying Features by Quantitative Attribute Values
This tutorial demonstrates accessing classification options for symbolizing quantitative values per features within a vector feature class.
Included in this tutorial:
Accessing Graduated symbology
Classification method options
Accessing histogram
Software version in examples: QGIS-LTR 3.40.5-Bratislava
Tutorial Data: The tutorial includes demonstration with sample data available here.
Credits: Sally Kaye and L. Meisterlin (2025)
This tutorial does not define all classification methods (simply where to find these options and a brief description of some of them).
Related Tutorial: Classified Quantitative Symbology for Vector Features
Accessing the graduated symbology option and classifying
Information on accessing the Graduated symbology option and applying graduated colors and symbols is provided in Classified Quantitative Symbology for Vector Features.
We also demonstrate it below, choosing “int_sm” as our field to symbolize.
accessing graduated symbology and classification
Classification method options
In the demonstration below, we will classify features in the Tracts_wTable layer using the values in the “Int_sm” field (This is a field of random, positive integer values less than 400).
As seen above, the classification method options are indicated in the Mode dropdown menu at the bottom left of the Classes list. The settings default here to using an Equal Count (Quantile) classification method, dividing the values of our dataset into 5 classes with an equal number of values in each.
The demonstration below highlights choosing some of these options and changing some of their parameters. The steps demonstrated are listed in order below. Notice that with each change, the map visualization in the map canvas and the legend in the Layers Panel will update only after Apply is clicked.
The demonstration shows…
Within the default Equal Count (Quantile) method, changing the number of classes to 6.
Accessing the list of classification methods from the Mode dropdown
Choosing a Fixed Interval classification method and changing the interval size from 1 to 50. Notice that the option to choose the number of classes is “greyed out” because we are specifying the interval size instead. (We will have as many classes as the distribution of our dataset values require based on the interval size we choose.) In our example, 8 classes are created with cutoff value intervals of 50.
Choosing an Equal Interval classification method and changing the number of classes back to 5. Like Fixed Interval, this option creates classes at consistent intervals, but you are able to choose the number of classes rather than the interval size.
Choosing a Standard Deviation classification method. The default interval size is ½ standard deviation from the mean. When the number of classes is changed, the interval size will adjust.
Choosing a Natural Breaks (Jenks) classification method. This option classifies values based on groupings, emphasizing clustering and differences in the data.
Upper and lower bounds for individual classes can be manually changed after choosing a classification method. This is demonstrated within a Natural Breaks classification.
walking through different classification method options
Accessing the histogram
Of course, choosing a classification method is generally best done with an understanding of the distribution of the values in your dataset.
To access a histogram of values in classes based on the current classification method, click the Histogram tab next to the Classes tab, and click Load Values. You can view these values in relation to the mean and standard deviations by checking the corresponding boxes. You can also change the number of Histogram bins; the visualization will update automatically.
accessing the histogram and statistics within the symbology window