Available with Spatial Analyst license.
Available with 3D Analyst license.
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).
The Output cell size can be defined by a numeric value or obtained from an existing raster dataset. If the cell size hasn’t been explicitly specified as the parameter value, it is derived from the Cell Size environment if it has been specified. If the parameter cell size or the environment cell size have not been specified, but the Snap Raster environment has been set, the cell size of the snap raster is used. If nothing is specified, the cell size is calculated from the shorter of the width or height of the extent divided by 250, in which the extent is in the Output Coordinate System specified in the environment.
If the cell size is specified using a numeric value, the tool will use it directly for the output raster.
If the cell size is specified using a raster dataset, the parameter will show the path of the raster dataset instead of the cell size value. The cell size of that raster dataset will be used directly in the analysis, provided the spatial reference of the dataset is the same as the output spatial reference. If the spatial reference of the dataset is different than the output spatial reference, it will be projected based on the selected Cell Size Projection Method.
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 effect 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 with the version of the IDW tool available there.
The input feature data must contain at least one valid field.
References:
Philip, G. M., and D. F. Watson. "A Precise Method for Determining Contoured Surfaces." Australian Petroleum Exploration Association Journal 22: 205–212. 1982.
Watson, D. F., and G. M. Philip. "A Refinement of Inverse Distance Weighted Interpolation." Geoprocessing 2:315–327. 1985.
Syntax
Idw(in_point_features, z_field, out_raster, {cell_size}, {power}, {search_radius}, {in_barrier_polyline_features})| Parameter | Explanation | Data Type |
in_point_features | The input point features containing the z-values to be interpolated into a surface raster. | Feature Layer |
z_field | The field that holds a height or magnitude value for each point. This can be a numeric field or the Shape field if the input point features contain z-values. | Field |
out_raster | The output interpolated surface raster. It is always a floating-point raster. | Raster Dataset |
cell_size (Optional) | The cell size of the output raster that will be created. This parameter can be defined by a numeric value or obtained from an existing raster dataset. If the cell size hasn't been explicitly specified as the parameter value, the environment cell size value will be used if specified; otherwise, additional rules will be used to calculate it from the other inputs. See the usage section for more detail. | Analysis Cell Size |
power (Optional) | The exponent of distance. Controls the significance of surrounding points on the interpolated value. A higher power results in less influence from distant points. It can be any real number greater than 0, but the most reasonable results will be obtained using values from 0.5 to 3. The default is 2. | Double |
search_radius (Optional) | Defines which of the input points will be used to interpolate the value for each cell in the output raster. There are two ways to specify the searching neighborhood: Variable and Fixed. Variable uses a variable search radius in order to find a specified number of input sample points for the interpolation. Fixed uses a specified fixed distance within which all input points will be used. Variable is the default. The syntax for these parameters are:
| Radius |
in_barrier_polyline_features (Optional) | Polyline features to be used as a break or limit in searching for the input sample points. | Feature Layer |
Code sample
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)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
searchRadius = 150000
# Execute IDW
arcpy.Idw_3d(inPointFeatures, zField, outRaster, cellSize,
power, searchRadius)Environments
Licensing information
- Basic: Requires 3D Analyst or Spatial Analyst
- Standard: Requires 3D Analyst or Spatial Analyst
- Advanced: Requires 3D Analyst or Spatial Analyst