Interpolation is mentioned because it is important for the production of our own geostatistic layers.

Interpolation (spatial) is a method and at the same time a procedure for determining the plot value (for example, the attribute z) or the value of the requested function at an unknown point and location based on the known values ​​of the sought attribute at the surrounding points. (Šumrada, 2005)

This means that by interpolating data, we can obtain information about values ​​in unknown areas. We know point and surface interpolations, which are additionally divided into global and local methods. In the case of global interpolation, all the data of the area under consideration is taken into account at the same time. The principle of local interpolations is based on the assumption that values ​​at closer locations have a greater impact than more remote ones. (Cerar, 2012).

QGIS offers various methods of interpolation of 2D vector and raster data, such as triangular irregular network (TIN), inverse distance weighted (IDW) method, modified IDW (modified quadratic Shepard interpolation), b-spline interpolation and kriging (ordinary, simple, universal, regression). There are also tools for 3D interpolation from the GRASS tools, such as, for example, v.surf.bspline.

With the interpolation of point geospatial data, it is possible to create a raster layer, which may be used for additional spatial analysis or visualization.