Instructions for use

The Inverse Distance Weighting interpolation is based on the similarity of the sample points in the interpolation region, and calculates the Weighted Mean value of the sample points in the adjacent region to estimate the value of the cell, and then interpolates to obtain a surface.

• The Source Dataset used for interpolation must have a numeric field as the interpolation field.
• Inverse Distance Weighting interpolation method is a relatively accurate interpolation method, which is suitable for Point Dataset samples with uniform distribution and Density reflecting local differences.
• The Inverse Distance Weighting interpolation uses the Weighted Mean distance between the sample points. The average value cannot be greater than input maximum value or less than input minimum value. Therefore, in the generated Result Data, each grid value is within the range of the maximum value and the minimum value of the sampling data.
• If the known observation point data does not contain the maximum value of a local area (such as the peak value of a mountain peak), the interpolation value obtained at the maximum value will be lower than that of other nearby points, which may be inconsistent with the actual situation. Therefore, it is better to include the maximum and minimum sampling points of the interpolation area in the sample Point Dataset.

Open the "Precipitation" Datasource "under the" Exercise Data/RasterAnalysis "folder, where there is precipitation data of meteorological monitoring stations in some regions. We use this data as an example.

Function Entry

• Spatial Analysis tab-> Raster Analysis group-> Interpolation Analysis-> Inverse Distance Weighting.
• Toolbox-> Raster Analysis-> Interpolation Analysis-> Inverse Distance Weighting. (iDesktopX)

Parameter Description

• Set public parameters for Interpolation Analysis, including Source Data, Bounds, Result Data, and Environment Settings. For the settings of public parameters such as source data, Bounds, and Result Data, please refer to Public Parameter Description .
• Set the sample point search mode: Fixed Count and Fixed Radius are supported. For a detailed description of these two lookup methods, see: Inverse Distance Weighting interpolation .

Fixed Count: indicates to interpolate by using a fixed number of sample values within the maximum radius.

• Max Radius: Enter the size of the radius to use for Fixed Count. The default value is 0, which means to use the maximum radius to find.
• Find Points: Enter the number of points to use for Fixed Count. The default point is 12.

Fixed Radius: All points within the search radius are involved in the interpolation operation.

• Search radius: Enter the size of the set search radius. The default lookup radius is 1/5 of the larger value of the length or width of the range of the Dataset participating in the Interpolation Analysis. All the sampling points within the radius range shall participate in the interpolation operation.
• Minimum number of points: Enter the minimum number of points to be used for Fixed Count. The default is 5 points. When the number of points in the neighborhood is less than specified minimum, the lookup radius increases until it can contain the minimum number of points entered. The maximum value is 12.
• Power: The power is the exponent of the weight distance, which controls the weight of the surrounding points when interpolating. Can be a positive integer value greater than 0. The default value is 2.

Figure: Inverse Distance Weighting Interpolation Result