Understanding geostatistical analysis

Generating a continuous surface used to represent a particular attribute is a key capability required in most geographic information system (GIS) applications. Perhaps the most commonly used surface type is a digital elevation model of terrain. These datasets are readily available at small scales for various parts of the world. However, just about any measurement taken at locations across a landscape, subsurface, or atmosphere can be used to generate a continuous surface. A major challenge facing most GIS modelers is to generate the most accurate surface possible from existing sample data as well as characterize the error and variability of the predicted surface. Newly generated surfaces are used in further GIS modeling and analysis as well as in 3D visualization. Understanding the quality of this data can greatly improve the utility and purpose of GIS modeling.

Geostatistical Analyst uses sample points taken at different locations in a landscape and creates (interpolates) a continuous surface. The sample points are measurements of some phenomenon, such as radiation leaking from a nuclear power plant, an oil spill, or elevation heights. Geostatistical Analyst derives a surface using the values from the measured locations to predict values for each location in the landscape.

Geostatistical Analyst provides two groups of interpolation techniques: deterministic and geostatistical. All methods rely on the similarity of nearby sample points to create the surface. Deterministic techniques use mathematical functions for interpolation. Geostatistics relies on both statistical and mathematical methods, which can be used to create surfaces and assess the uncertainty of the predictions.