The general purpose of this paper is to indroduce the most popular methods for integration of the different data sources measure the same characteristic in different way. The information available on a natural phenomenon is rarely limited to the values assumed by a single variable over a set of samples points. Direct measurements of the primary attribute, or variable under study, are often supplemented by secondary information originating from other related categorical or continuous attributes, or secondary (auxiliary) variables. Most real studies involve more than one variable. In general, the estimation improves when this additional information is taken into consideration, particularly when primary data are sparse or poorly correlated in space.