Classification panel

WARNING

The english version of the documentation is currently under construction.

Some parts are not yet translated and some translations may be incomplete or inaccurate.

Several methods are proposed for transforming a continuous series of values into a discrete series, i.e. into a finite number of classes.

When creating a choropleth representation, the number of classes and their limit values must be justified statistically and/or thematically.

The methods proposed by the tool can be used as they stand, or as reading and analysis guides prior to manual entry of the desired class limits.

Overview of the classification panel

Several elements are present in this window:

• A summary of the series of values to be classified (number of non-zero values, mean, median, minimum, maximum, etc.),
• A graphical overview of the distribution of values (histogram, density curve and whisker box),
• A section dedicated to classification (choice of method, number of classes, choice of color palette and visualization of the number of entities per class).

Classification methods

Quantiles

This method, sometimes also described by the term "classification into classes of equal frequencies", allows the formation of classes that all have the same number of entities.

Equal intervals

This method, sometimes also called "equal amplitudes", allows the creation of classes that all have the same range.

Q6

This method (popularized by the PhilCarto tool), allows classification according to the quartile method while isolating extreme values: it thus produces 6 classes.

Natural thresholds (CKMeans algorithm)

This method allows the creation of homogeneous classes. Indeed, the algorithm aims to find the desired number of classes by minimizing the intra-class variance and maximizing the inter-class variance.

Natural thresholds (Fisher-Jenks algorithm)

This method allows the creation of homogeneous classes. Indeed, the algorithm aims to find the desired number of classes by minimizing the intra-class variance and maximizing the inter-class variance.

Deprecation

This method is now deprecated in favor of the CKMeans method, which gives better results (entities are generally classified as in the Fisher-Jenks method but with class limits that are easier to read) in a much shorter calculation time. We therefore recommend using the CKMeans method instead.

Mean and standard deviation

This method proposes to form classes based on the value of the standard deviation and the mean. This method of classification does not allow you to directly choose a number of classes, but allows you to choose the portion of the standard deviation which corresponds to the size of a class as well as the role of the mean (used as a class boundary or as a class center).

Head/tail breaks

Nested means

Geometric progression

This classification method allows you to create classes whose limits are defined by a geometric progression: each class is defined by a multiple of the previous one.

User-defined

This method allows you to define the class limits manually.