Geographical information systems principles of ordinary kriging interpolator

The aim of this study resumes the main linear stochastic view for spatial interpolation within Geographical Information Systems (GIS) and still unknown by major GIS users: Ordinary Kriging. To review the geostatistical background to involve complex spatial tasks is, thus, central. It starts with the main concepts of the regionalized data nature, exploratory data analysis and distribution standardization since Mother Nature does not follow, most of the times, the Gaussian curve. Sampling considerations follows next while a deep variography inspection is presented later. Cressie's automatic fitness and Kriging equation system are mentioned, as well. It is expected that this article might be used by Geographical Information System (GIS) users to get acquainted with a more complex but better interpolated technique with two major features: BLUE (Best Linear Unbiased Estimator) and BUE (Best Unbiased Estimator) if data holds a Normal distribution.