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Surface interpolation functions make predictions from sample measurements for all locations in a raster data set whether a measurement has been taken at the location or not. There are a variety of ways to derive a prediction for each location. The many interpolation methods each make different assumptions of the data, and certain methods are more applicable for specific data; for example, one method may account for local variation better than another. Each method produces predictions using different calculations.
The inverse distance weighted and spline methods are referred to as deterministic interpolation methods, because they assign values to locations based on the surrounding measured values and on specified mathematical formulas that determine the smoothness of the resulting surface. A second family of interpolation methods consists of geostatistical methods (such as kriging), which are based on statistical models that include autocorrelation (the statistical relationship among the measured points). Because of this, not only do geostatistical techniques have the capability of producing a prediction surface, but they can also provide some measure of the certainty or accuracy of the predictions.
Available Surface Interpolation tools include:
- Inverse Distance Weighted
- Minimum Curvature Spline
- Natural Neighbors
- Ordinary Kriging
- Universal Kriging
- Polynomial Trend
- Topo To Raster (ANUDEM)
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