Create a Raster using Inverse Weighted Distance

  1. On the Raster ribbon tab, in the Interpolation group, expand Create Raster.
  2. Select the interpolation method from:
    • Inverse Weighted Distance

    The Create Raster) panel displays.

  3. Select the Input point table.
  4. Select the Fields to interpolate.
  5. Set the Output raster, where the raster is saved.
  6. Select the Color ramp for the raster.
  7. If there are Breaklines select the Source file and the Attribute field to load the breaklines from.

    Note: Breaklines are defined as multi-segment lines in which the slope is monotonically increasing or decreasing along each segment. Examples of breaklines include stream or river traces, cliff faces, dredged channels, topographic ridge lines or roadways. By incorporating break lines into the gridding process the output grid is forced to conform specific slope requirements in critical areas.

  8. Expand Inverse Distance Weighted.
    1. Select the Model from:
      • Gaussian—The weight assigned to each input value is determined according to a 2D Gaussian function centred on the grid node. The shape and standard deviation of the Gaussian function is proportional to the Range value. Larger range values produce flatter functions Gaussian functions and a smoother grid. The nugget, range and distance radius values are measured in increments of the output grid cell size.
      • Linear—Each input point's weight is proportional to its Euclidian distance from the grid node being interpolated. The linear weight model enables the nugget and range parameters to be adjusted in order to vary the weight assignments. At distances less than the nugget distance the weight model is be 1 (i.e. all data contributes equally). At distances beyond the nugget value the weighting factor is applied according to the selected model. The range parameter is used to set the outer distance threshold for which the weight model is applied. Any samples which exceed the Range and are less than the distance radius is assigned an equal weight. The nugget, range and distance radius values are measured in increments of the output grid cell size.
      • Exponential—Each input point's weight is proportional to its Euclidian distance from the grid node being interpolated raised to the specified power. Increasing the power value causes smaller weights to be assigned to closer points and more distant points to be assigned equal but large weights. Increasing power values therefore, cause each interpolated grid node to more closely approximate the sample values closest to it. As with the Linear model the nugget and range properties can be modified to constrain that distance over which the exponential weight model is most effective.
      • Power—The default option, each input point's weight is proportional to the inverse of its distance to the specified power from the grid node. Increasing the weighting power reduces the influence distant points have on the calculated value of each grid node. Large power values cause grid cell values to approximate the value of the nearest data point, while smaller power values results in data values being more evenly distributed among neighbouring grid nodes. The weighting value defaults to 2 (i.e. the weight of any data point is inversely proportional to the square of its distance from the grid cell) which is appropriate for most situations. If required, the weighting value can be altered to any positive value.
    2. Enter the X Radius size.
    3. Enter the Y Radius size.
    4. Select whether to check Distance Taper, the distance taper controls allow you to apply a taper function to the interpolated value of each grid node based on its distance to the nearest valid sample point.
  9. Expand Geometry and set Cell size.

    Note: Cell size is the size (or resolution) of each cell in the output grid file. The size is measured in the spatial units of the output grid coordinate system.

  10. Expand Advanced. 
    • If Define maximum memory usage is checked, this control is used to constrain the amount of physical memory (RAM) that the software attempts to use during the gridding process. The default control setting is unchecked and in this state the software attempts to use up to 80% of available physical memory (to a maximum of 2 GB) during gridding. While it is possible to constrain the amount of RAM that is available to the software during the gridding process; doing so reduces the gridding performance significantly if the software has to repeatedly page tiles of data between disk and memory in order to perform the gridding operation. To achieve best performance on large datasets it is advisable that you close all running applications and free up as much physical memory (RAM) as possible before commencing gridding. For very large datasets (>50 million points) it is recommended that the software is run on a machine with between 2 and 4 GB of RAM.
    • To Scan data extents controls the resolution at which the software initially scans the input data files to establish the preliminary spatial statistics during the first phases of gridding. The default behaviour is to scan all lines of the input data. It is possible to speed up the initial scan of the input files by adjusting the scan data extents control to one of the following settings:
      • Complete—Scans every line of each input data file.
      • Fine—Scans approximately 12% (1-in-8) of the lines from each input data file.
      • Course—Scans approximately 3% (1-in-32) of the lines from each input data file.
      • Overview—Scans approximately 0.75% (1-in-128) of the lines from each input data file.
      • Bounds—Acquires the data extents from information stored in the files if available or performs an overview scan if unavailable.

        Note: For datasets that have a relatively even spatial distribution of input points setting the scan data extents control to Overview provides the best compromise between speed and a representative statistical sample.

    • Select the Temp Folder, this folder is used to temporarily store the spatially sorted input data tiles which are used during the gridding process. If all of the input data can fit into system memory then no temporary files are created and the entire process occurs in RAM. If the input dataset is very large (>10 million points) then it is necessary to store a copy of the input data on disk during the gridding process. By default the temporary directory is set to the Windows system temporary directory; however it may be necessary to map it to an alternative storage location depending on the size of the input dataset. Note You should always ensure that you have at least the same amount of free temporary storage space as the total size of the input dataset. A good rule of thumb is to set the temp path to a location that has 2x as much storage space as the size of the input dataset.
    • Select the Output Raster data type, the data type control is used to set the numeric storage type for the interpolated values in the output grid. It is advisable to select the appropriate data type that most efficiently represents the range of data that is stored in the output grid. For example, a signed 2-byte integer is generally suitable for storing typical elevation data at 1m vertical resolution. The data types available are:
      • Automatic
      • Unsigned byte
      • Signed byte
      • Unsigned short
      • Signed short
      • Unsigned int
      • Signed int
      • Float

      Note: The automatic option sets the output format to an appropriate data type based on an analysis of the input data range.

  11. Select whether to Clip output raster.
  12. Click Run.

    The grid should display in the Map window.