20200102 12203200 1.0 TRUE 20220407 074425 InterpolatePoints 20200102 Predicts values at new locations based on measurements from a collection of points. The tool takes point data with values at each point and returns a raster of predicted values. 005 Generate Surface Pattern Ebk Empirical Bayesian Kriging Geostat Interpolate Kriging ArcToolbox Tool 20200102 Environmental Systems Research Institute, Inc. (Esri) 380 New York Street Redlands California 92373-8100 info@esri.com United States 909-793-2853 909-793-5953 http://www.esri.com 007

Predicts values at new locations based on measurements from a collection of points. The tool takes point data with values at each point and returns a raster of predicted values.

InterpolatePoints_ra The input point features you want to interpolate. The input point features you want to interpolate. The field containing the data values you want to interpolate. The field must be numeric. The field containing the data values you want to interpolate. The field must be numeric. The name of the output raster service. The default name is based on the tool name and the input layer name. If the layer already exists, you will be prompted to provide another name. The name of the output raster service. The default name is based on the tool name and the input layer name. If the layer already exists, you will be prompted to provide another name. Choose your preference for speed versus accuracy. More accurate predictions will take longer to calculate. SPEED—The operation is optimized for speed. BALANCE—A balance between speed and accuracy. This is the default. ACCURACY—The operation is optimized for accuracy. Choose your preference for speed versus accuracy. More accurate predictions will take longer to calculate. Speed—The operation is optimized for speed. Balance—A balance between speed and accuracy. This is the default. Accuracy—The operation is optimized for accuracy. Choose whether to transform your data to a normal distribution before performing analysis. If your data values do not appear to be normally distributed (bell-shaped), it is recommended to perform a transformation. NO_TRANSFORM—No transformation is applied. This is the default. TRANSFORM—A transformation to the normal distribution is applied. Choose whether to transform your data to a normal distribution before performing analysis. If your data values do not appear to be normally distributed (bell-shaped), it is recommended to perform a transformation. Checked—A transformation to the normal distribution is applied. Unchecked—No transformation is applied. This is the default. Choose the number of points in each of the local models. A larger value will make the interpolation more global and stable, but small-scale effects may be missed. Smaller values will make the interpolation more local, so small-scale effects are more likely to be captured, but the interpolation may be unstable. Choose the number of points in each of the local models. A larger value will make the interpolation more global and stable, but small-scale effects may be missed. Smaller values will make the interpolation more local, so small-scale effects are more likely to be captured, but the interpolation may be unstable. The number of neighbors to use when calculating the prediction at a particular cell. The number of neighbors to use when calculating the prediction at a particular cell. Set the cell size and units of the output raster. If a prediction error raster is created, it will also use this cell size. The units can be Kilometers, Meters, Miles, or Feet. The default units are Meters. Set the cell size and units of the output raster. If a prediction error raster is created, it will also use this cell size. The units can be Kilometers, Meters, Miles, or Feet. The default units are Meters. Choose whether to output a raster of standard errors of the interpolated predictions. Standard errors are useful because they provide information about the reliability of the predicted values. 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. For example, suppose a new location gets a predicted value of 50 with a standard error of 5. 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. 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. If a raster of standard errors for the interpolated predictions is requested, it will have the same name as the Result layer name but with Errors appended. OUTPUT_ERROR—Create a prediction error raster. NO_OUTPUT_ERROR—Do not create a prediction error raster. This is the default. Choose whether to output a raster of standard errors of the interpolated predictions. Standard errors are useful because they provide information about the reliability of the predicted values. 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. For example, suppose a new location gets a predicted value of 50 with a standard error of 5. 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. 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. If a raster of standard errors for the interpolated predictions is requested, it will have the same name as the Result layer name but with Errors appended. Unchecked—No output prediction error is generated. This is the default. Checked—An output prediction error is generated. The interpolation is performed by generating many local interpolation models that are merged together to create the final output raster. The number of points in each local model can be controlled with the Size of local models parameter. The Empirical Bayesian Kriging tool is used to perform the underlying interpolation. This tool is part of the ArcGIS Geostatistical Analyst extension. Many parameters of the tool are exposed in Interpolate Points, but many are controlled automatically by the Optimize for parameter. This example interpolates a point feature service into an image service raster. import arcpy arcpy.InterpolatePoints_ra('https://MyPortal.esri.com/server/rest/services/Hosted/myPoints/FeatureServer/0', 'myField', 'outImgServ', 'SPEED', 'False', 50, 8, '10000 Meters', 'NO_OUTPUT_ERROR') This example interpolates a point feature service into an image service raster. #------------------------------------------------------------------------------- # Name: InterpolatePoints_example02.py # Description: Interpolates a point feature service into an image service raster. # # Requirements: ArcGIS Image Server # Import system modules import arcpy # Set local variables inPoints = 'https://MyPortal.esri.com/server/rest/services/Hosted/myPoints/FeatureServer/0' inField = 'myField' outRaster = 'outImgServ' optimizeFor = 'SPEED' transform = 'False' subsetSize = 50 numNeighbors = 8 outCellSize = '10000 Meters' error = 'NO_OUTPUT_ERROR' # Execute InterpolatePoints arcpy.InterpolatePoints_ra(inPoints, inField, outRaster, optimizeFor, transform, subsetSize, numNeighbors, outCellSize, error) ArcGIS geoprocessing tool for interpolating points into a raster surface. withheld