## Inverse Distance Weighting Model

Uses the measured values surrounding the prediction location to predict a value for any unsampled location, based on the assumption that things that are close to one another are more alike than those that are farther apart.

Interpolation
183

#### Authorship

Affiliation:
Brandeis University
Email:
shepard@brandeis.edu
Homepage:
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#### Classification(s)

Method-focused categoriesData-perspectiveGeoinformation analysis

#### Model Description

English {{currentDetailLanguage}} English

Summary

Uses the measured values surrounding the prediction location to predict a value for any unsampled location, based on the assumption that things that are close to one another are more alike than those that are farther apart.

Usage

·      The predicted value 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.

·      IDW can produce "bulls eyes" around data locations.

·      There are no assumptions required of the input data.

·      This method is well suited to be used with very large input datasets.

Syntax

IDW_ga (in_features, z_field, {out_ga_layer}, {out_raster}, {cell_size}, {power}, {search_neighborhood}, {weight_field})

 Parameter Explanation Data Type in_features The input point features containing the z-values to be interpolated. Feature Layer z_field Field that holds a height or magnitude value for each point. This can be a numeric field or the Shape field if the input features contain z-values or m-values. Field out_ga_layer (Optional) The geostatistical layer produced. This layer is required output only if no output raster is requested. Geostatistical Layer out_raster (Optional) The output raster. This raster is required output only if no output geostatistical layer is requested. Raster Dataset cell_size (Optional) The cell size at which the output raster will be created. This value can be explicitly set under Raster Analysis from the Environment Settings. If not set, it is the shorter of the width or the height of the extent of the input point features, in the input spatial reference, divided by 250. Analysis Cell Size power (Optional) The exponent of distance that controls the significance of surrounding points on the interpolated value. A higher power results in less influence from distant points. Double search_neighborhood (Optional) Defines which surrounding points will be used to control the output. Standard is the default. This is a Search Neighborhood class SearchNeighborhoodStandard,SearchNeighborhoodSmooth, SearchNeighborhoodStandardCircular, and SearchNeighborhoodSmoothCircular. Standard ·         Major semiaxis—The major semiaxis value of the searching neighborhood. ·         Minor semiaxis—The minor semiaxis value of the searching neighborhood. ·         Angle—The angle of rotation for the axis (circle) or semimajor axis (ellipse) of the moving window. ·         Maximum neighbors—The maximum number of neighbors that will be used to estimate the value at the unknown location. ·         Minimum neighbors—The minimum number of neighbors that will be used to estimate the value at the unknown location. ·         Sector type—The geometry of the neighborhood. §  One sector—Single ellipse. §  Four sectors—Ellipse divided into four sectors. §  Four sectors shifted—Ellipse divided into four sectors and shifted 45 degrees. §  Eight sectors—Ellipse divided into eight sectors. Smooth ·         Major semiaxis—The major semiaxis value of the searching neighborhood. ·         Minor semiaxis—The minor semiaxis value of the searching neighborhood. ·         Angle—The angle of rotation for the axis (circle) or semimajor axis (ellipse) of the moving window. ·         Smoothing factor—The Smooth Interpolation option creates an outer ellipse and an inner ellipse at a distance equal to the Major Semiaxis multiplied by the Smoothing factor. The points that fall outside the smallest ellipse but inside the largest ellipse are weighted using a sigmoidal function with a value between zero and one. StandardCircular ·         Radius—The length of the radius of the search circle. ·         Angle—The angle of rotation for the axis (circle) or semimajor axis (ellipse) of the moving window. ·         Maximum neighbors—The maximum number of neighbors that will be used to estimate the value at the unknown location. ·         Minimum neighbors—The minimum number of neighbors that will be used to estimate the value at the unknown location. ·         Sector type—The geometry of the neighborhood. §  One sector—Single ellipse. §  Four sectors—Ellipse divided into four sectors. §  Four sectors shifted—Ellipse divided into four sectors and shifted 45 degrees. §  Eight sectors—Ellipse divided into eight sectors. SmoothCircular ·         Radius—The length of the radius of the search circle. ·         Smoothing factor—The Smooth Interpolation option creates an outer ellipse and an inner ellipse at a distance equal to the Major Semiaxis multiplied by the Smoothing factor. The points that fall outside the smallest ellipse but inside the largest ellipse are weighted using a sigmoidal function with a value between zero and one. Geostatistical Search Neighborhood weight_field (Optional) Used to emphasize an observation. The larger the weight, the more impact it has on the prediction. For coincident observations, assign the largest weight to the most reliable measurement. Field

Code Sample

IDW (Python window)

Interpolate a series of point features onto a raster.

import arcpy

arcpy.env.workspace = "C:/gapyexamples/data"

arcpy.IDW_ga("ca_ozone_pts", "OZONE", "outIDW", "C:/gapyexamples/output/idwout", "2000", "2",

arcpy.SearchNeighborhoodStandard(300000, 300000, 0, 15, 10, "ONE_SECTOR"), "")

IDW (stand-alone script)

Interpolate a series of point features onto a raster.

# Name: InverseDistanceWeighting_Example_02.py

# Description: Interpolate a series of point features onto a rectangular raster

#              using Inverse Distance Weighting (IDW).

# Requirements: Geostatistical Analyst Extension

# Import system modules

import arcpy

# Set environment settings

arcpy.env.workspace = "C:/gapyexamples/data"

# Set local variables

inPointFeatures = "ca_ozone_pts.shp"

zField = "OZONE"

outLayer = "outIDW"

outRaster = "C:/gapyexamples/output/idwout"

cellSize = 2000.0

power = 2

# Set variables for search neighborhood

majSemiaxis = 300000

minSemiaxis = 300000

angle = 0

maxNeighbors = 15

minNeighbors = 10

sectorType = "ONE_SECTOR"

searchNeighbourhood = arcpy.SearchNeighborhoodStandard(majSemiaxis, minSemiaxis,

angle, maxNeighbors,

minNeighbors, sectorType)

# Check out the ArcGIS Geostatistical Analyst extension license

arcpy.CheckOutExtension("GeoStats")

# Execute IDW

arcpy.IDW_ga(inPointFeatures, zField, outLayer, outRaster, cellSize,

power, searchNeighbourhood)

#### How to Cite

Donald Shepard (2019). Inverse Distance Weighting Model, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/87d1b78a-808e-414b-83be-072b5c846e61

#### History

Last modifier :
haolingchen
Last modify time :
2020-12-29
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#### Authorship

Affiliation:
Brandeis University
Email:
shepard@brandeis.edu
Homepage:
View
Is authorship not correct? Feedback

#### History

Last modifier :
haolingchen
Last modify time :
2020-12-29
Modify times :
View History

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