Prior to any earth sciences modeling, the basic data quality, reliability, and sufficiency are the basic questions that must be grasped by the specialists in the earth sciences domain so that she/he can continue for better description and modeling of the phenomenon concerned. For this purpose, field, laboratory, or satellite observations and measurements are basic foundation lines toward a successful problem solution. Sampling categorization of the data and the properties is important in any data treatment work, especially in earth sciences domain. The internal structure of data set as dependent of independent, the sample length, and their random distribution behavior by means of a theoretical probability distribution function are to be evaluated objectively in any study. In earth sciences, often irregularly located spatial data are available, and therefore, the representative area or (area of influence) should be defined for each measurement location. For this purpose, this chapter presents different spatial methodologies including regionalization, inverse distance methods, triangularization, polygonizations, areal coverage probability, regional extreme value probabilities, and spatio-temporal modeling.