To calculate this range of reasonable values, multiply the standard error by 2, add this value to the predicted value to get the upper end of the range, and subtract it from the predicted value to get the lower end of the range.ĭetermines how predicted values will be classified into areas: This means that this task's best guess is that the true value at that location is 50, but it reasonably could be as low as 40 or as high as 60. For example, suppose a new location gets a predicted value of 50 with a standard error of 5. A simple rule of thumb is that the true value will fall within two standard errors of the predicted value 95 percent of the time. Standard errors are useful because they provide information about the reliability of the predicted values. If True, a polygon layer of standard errors for the interpolation predictions will be returned in the predictionError output parameter. More accurate predictions take longer to calculate. Integer value declaring your preference for speed versus accuracy, from 1 (fastest) to 9 (most accurate). Name of the numeric field containing the values you wish to interpolate.
If a value of 9 is provided for interpolateOption, empirical Bayesian kriging will use the following parameters: If a value of 5 is provided for interpolateOption, empirical Bayesian kriging will use the following parameters: If a value of 1 is provided for interpolateOption, empirical Bayesian kriging will use the following parameters: The parameters that are supplied to the Empirical Bayesian Kriging tool are controlled by the interpolateOption request parameter.
Interpolate Points uses the Empirical Bayesian Kriging geoprocessing tool to perform the interpolation. Meteorological applications include prediction of temperatures, rainfall, and associated variables (such as acid rain).Precise amounts of fertilizer for each location in the field. Order to study their relationships to crop yield and prescribe So on) and other indicators (such as electrical conductivity) in Predict soil nutrient levels (nitrogen, phosphorus, potassium, and.Predict heavy metal concentrations in crops based on samples taken from individual plants.Interpolate Points can be used to predict pollution levels at locations that don't have sensors, such as locations with at-risk populations-schools or hospitals, for example. An air quality management district has sensors that measure pollution levels.The task takes point data with values at each point and returns areas classified by predicted values. The Interpolate Points task allows you to predict values at new locations based on measurements from a collection of points.
0 kommentar(er)
