The process begins with transforming the original data into indicator values, which are typically binary (0 or 1). For example, in a study of disease prevalence, 1 might indicate the presence of a disease, while 0 might indicate its absence. This transformation simplifies the complex spatial structure into a format that can be analyzed using kriging methods. The indicator values are then used to estimate the probability of the event occurring at unsampled locations.
