The interpolation tools are generally divided into deterministic and geostatistical methods. Each model produces predictions using different calculations. With each model, there are different assumptions made of the data, and certain models are more applicable for specific data-for example, one model may account for local variation better than another. There are a variety of ways to derive a prediction for each location each method is referred to as a model. Surface interpolation tools make predictions from sample measurements for all locations in an output raster dataset, whether or not a measurement has been taken at the location. The continuous surface representation of a raster dataset represents some measure, such as the height, concentration, or magnitude (for example, elevation, acidity, or noise level). Input points can be either randomly or regularly spaced or based on a sampling scheme. Instead, you can measure the phenomenon at strategically dispersed sample locations, and predicted values can be assigned to all other locations. Visiting every location in a study area to measure the height, concentration, or magnitude of a phenomenon is usually difficult or expensive. The Interpolation tools create a continuous (or prediction) surface from sampled point values.
