# IDW (3D Analyst)

## Summary

Interpolates a raster surface from points using an inverse distance weighted (IDW) technique.

## Usage

• The output value for a cell using inverse distance weighting (IDW) is limited to the range of the values used to interpolate. Because IDW is a weighted distance average, the average cannot be greater than the highest or less than the lowest input. Therefore, it cannot create ridges or valleys if these extremes have not already been sampled (Watson and Philip 1985).

• The best results from IDW are obtained when sampling is sufficiently dense with regard to the local variation you are attempting to simulate. If the sampling of input points is sparse or uneven, the results may not sufficiently represent the desired surface (Watson and Philip 1985).

• The influence of an input point on an interpolated value is isotropic. Since the influence of an input point on an interpolated value is distance related, IDW is not "ridge preserving" (Philip and Watson 1982).

• Some input datasets may have several points with the same x,y coordinates. If the values of the points at the common location are the same, they are considered duplicates and have no affect on the output. If the values are different, they are considered coincident points.

The various interpolation tools may handle this data condition differently. For example, in some cases the first coincident point encountered is used for the calculation; in other cases the last point encountered is used. This may cause some locations in the output raster to have different values than what you might expect. The solution is to prepare your data by removing these coincident points. The Collect Events tool in the Spatial Statistics toolbox is useful for identifying any coincident points in your data.

• The barriers option is used to specify the location of linear features known to interrupt the surface continuity. These features do not have z-values. Cliffs, faults, and embankments are typical examples of barriers. Barriers limit the selected set of the input sample points used to interpolate output z-values to those samples on the same side of the barrier as the current processing cell. Separation by a barrier is determined by line-of-sight analysis between each pair of points. This means that topological separation is not required for two points to be excluded from each other's region of influence. Input sample points that lie exactly on the barrier line will be included in the selected sample set for both sides of the barrier.

• Barrier features are input as polyline features. IDW only uses the x,y coordinates for the linear feature; therefore, it is not necessary to provide z-values for the left and right sides of the barrier. Any z-values provided will be ignored.

• Using barriers will significantly extend the processing time.

• This tool has a limit of approximately 45 million input points. If your input feature class contains more than 45 million points, the tool may fail to create a result. You can avoid this limit by interpolating your study area in several pieces, making sure there is some overlap in the edges, then mosaicking the results to create a single large raster dataset. Alternatively, you can use a terrain dataset to store and visualize points and surfaces comprised of billions of measurement points.

If you have the Geostatistical Analyst extension, you may be able to process larger datasets.

• The input feature data must contain at least one valid field.

## Syntax

Idw_3d (in_point_features, z_field, out_raster, {cell_size}, {power}, {search_radius}, {in_barrier_polyline_features})

## Code Sample

IDW example 1 (Python window)

This example inputs a point shapefile and interpolates the output surface as a TIFF raster.

```import arcpy
from arcpy import env
env.workspace = "C:/data"
arcpy.Idw_3d("ozone_pts.shp", "ozone", "C:/output/idwout.tif", 2000, 2, 10)
```
IDW example 2 (stand-alone script)

This example inputs a point shapefile and interpolates the output surface as a Grid raster.

```# Name: IDW_3d_Ex_02.py
# Description: Interpolate a series of point features onto a
#    rectangular raster using Inverse Distance Weighting (IDW).
# Requirements: 3D Analyst Extension

# Import system modules
import arcpy
from arcpy import env

# Set environment settings
env.workspace = "C:/data"

# Set local variables
inPointFeatures = "ca_ozone_pts.shp"
zField = "ozone"
outRaster = "C:/output/idwout01"
cellSize = 2000.0
power = 2

# Check out the ArcGIS 3D Analyst extension license
arcpy.CheckOutExtension("3D")

# Execute IDW
arcpy.Idw_3d(inPointFeatures, zField, outRaster, cellSize,