Classifying Features by Quantitative Attribute Values
Included in this tutorial
Accessing a quantitative symbology type
Classification method options
Accessing histograms and statistics
Software version in examples: ArcGIS Pro 2.9.1
Tutorial Data: The tutorial includes demonstration with sample data available here.
Credits: L. Meisterlin (2022)
This tutorial demonstrates accessing the classification options for symbolizing quantitative values per features within a vector feature class.
This tutorial does not define each classification type (simply where to find the options). Refer to course materials for discussion of each classification method.
Related: The Classified Quantitative Symbology for Vector Features tutorial demonstrates the symbology options for classified values.
Also: For large datasets, you may need to change the maximum sample size that your software uses to generate histograms and calculate break values. See Changing the Maximum Sample Size (Quantitative Symbology Options).
For the demonstration below, we will classify the features in the Tracts layer of the standard data package of the Tutorial Data using the values in the “Int_sm” field. (This is a field of random, positive integer values less than 400.)
Accessing a classified quantitative symbology type
Classification options appear automatically within the symbology options for any classified approach to symbolizing features.
If you expect to find classification options but do not see them, double-check that you have indeed chosen classified symbols. For example, “graduated symbols” are dependent upon grouping values into “graduated” classes whereas “proportional symbols” do not use classification for assigning symbol sizes.
Access the symbology options by right-clicking on the layer’s name in the Contents panel. (In the demonstrations below, right-clicks are yellow and left-clicks are magenta.) This will summon the Symbology panel. Choose a classified quantitative symbology type under Primary Symbology.
The Fields drop-down will automatically populate with only the numeric fields from the layer’s attribute table. (Notice that hovering over each field name prompts a pop-up with a brief description of the field properties.) Choose the numeric field to use as the basis for symbolizing your features in the Field drop-down. In this example, we are symbolizing based on values in the “Int_sm” field. If you want to normalize your values by those in another field, you can choose the normalization field in the next trop down.
Notice that the settings default here to using a Natural Breaks (Jenks) classification method, dividing the values of our dataset into 5 classes. The upper values of each range are included in the Classes list, along with the label applied to the legend.
accessing classification options by choosing a classified, quantitative symbology type
Classification method options
As seen above, the classification method options are indicated in the Method drop-down of the Primary Symbology menu. The demonstration below highlights 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 View and the legend in the Contents panel will update.
Accessing the list of classification methods from the Method drop down
Choosing a Quantile classification method with five classes (quintiles).
Choosing an Equal Interval classification method, and changing the number of classes to 6.
Choosing a Manual Interval (DIY) classification method (still with 6 classes) and manually typing the preferred upper value of each range. Then changing the color ramp to a pre-set graduated scheme of pale-to-dark green.
Choosing a Standard Deviation classification method, and changing the interval size to 1/2 standard deviation from the mean. Notice here that the option to choose how many 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. This is the case for the “Defined Interval” classification method for the same reason.) Finally, changing the color scheme to a divergent color ramp (to better reflect the direction from the mean).
walking through different classification method options
Accessing the histogram and statistics
Of course, choosing a classification method is generally best done with an understanding of the distribution of the values in your dataset. The demonstration below shows how to access the histogram while compare different classification options within the histogram view, as well as accessing descriptive statistics for the values in the chosen field.
accessing the histogram and field statistics within the symbology panel
NOTE: For large datasets, you may need to change the maximum sample size that your software uses to generate histograms and calculate break values. See Changing the Maximum Sample Size (Quantitative Symbology Options).