Open map List

User controlled classification

Introduction

In user-controlled classification, a user selects the attribute(s) that will be used as the classification parameter(s) and defines the classification method. The latter involves declaring the number of classes, as well as the correspondence between the old attribute values and the new classes. This is usually done via a classification table.

Examples

Figure 1: Two classifications of average annual household income per ward in Dar es Salaam, Tanzania. Higher income areas are in darker greens. Five categories were identified. (a) with original polygons left intact; (b) with original polygons merged when in the same category. The data used for this illustration are not factual.

The classification table used for the Figure 1 is displayed in the following Table. It is rather typical for cases in which the parameter domain used is continuous (e.g. household income). Then, the table indicates value ranges to be mapped to the same category. Note that category values are ordinal data, described in Geographic fields.

Table: Classification table used in the Figure above.
Household income range New category value
391–2474 1
2475–6030 2
6031–8164 3
8165–11587 4
11588–21036 5

Another case exists when the classification parameter is nominal or at least discrete. Such an example is given in Figure 2. We must also define the data format of the output as a spatial data layer, which will contain the new classification attribute. The data type of this attribute is always categorical, i.e. integer or string, no matter what the data type of the attribute(s) from which the classification was obtained.

Figure 2: An example of a classification on a discrete parameter, namely land use unit in city of Dar es Salaam, Tanzania.

Sometimes, one may want to classify only a selection of features. In such cases, there are two options for the features that are not selected. One option is to keep their original values, while the other is to assign a null value to them in the output data set. A null value is a special value that means that no applicable value is present. Care must be taken to deal with these values correctly, both in computations and in visualization.

Learning outcomes

  • 11 - Spatial analysis: classes of functions

    Classify and explain spatial analysis functions (measurements, classification, overlay, neighbourhood and connectivity) in a raster and vector environment (level 1 and 2).

Prior knowledge

Outgoing relations

Learning paths