Global Polynomial Interpolation

Fits a smooth surface that is defined by a mathematical function (a polynomial) to the input sample points.

Interpolation
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Initial contribute: 2019-05-09

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Classification(s)

Method-focused categoriesData-perspectiveGeoinformation analysis

Model Description

English {{currentDetailLanguage}} English

Summary

Fits a smooth surface that is defined by a mathematical function (a polynomial) to the input sample points.

Usage

  • The result from this tool is a smooth surface that represents gradual trends in the surface over the area of interest.

  • The Local Polynomial Interpolation tool should be used when short-range variation exists in the data.

Syntax

GlobalPolynomialInterpolation_ga (in_features, z_field, {out_ga_layer}, {out_raster}, {cell_size}, {power}, {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 order of the polynomial.

Long
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

GlobalPolynomialInterpolation example 1 (Python window)

Interpolate point features onto a rectangular raster.

import arcpy
arcpy.env.workspace = "C:/gapysamples/data"
arcpy.GlobalPolynomialInterpolation_ga("ca_ozone_pts", "OZONE", "outGPI",
                                       "C:/gapyexamples/output/gpiout", "2000", "2", "")
GlobalPolynomialInterpolation example 2 (stand-alone script)

Interpolate point features onto a rectangular raster.

# Name: GlobalPolynomialInterpolation_Example_02.py
# Description: Global Polynomial interpolation fits a smooth surface that is
#              defined by a mathematical function (a polynomial) to the input 
#              sample points. The Global Polynomial surface changes gradually 
#              and captures coarse-scale pattern in the data.  Global Polynomial
#              interpolation is like taking a piece of paper and fitting it 
#              between the raised points (raised to the height of value).
# 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 = "outGPI"
outRaster = "C:/gapyexamples/output/gpiout"
cellSize = 2000.0
power = 2

# Check out the ArcGIS Geostatistical Analyst extension license
arcpy.CheckOutExtension("GeoStats")

# Execute GlobalPolynomialInterpolation
arcpy.GlobalPolynomialInterpolation_ga(inPointFeatures, zField, outLayer, 
                                       outRaster, cellSize, power)

How to Cite

ESRI (2019). Global Polynomial Interpolation, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/2eecfaf6-89c0-4712-8bb5-4b56c15e97ea
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Initial contribute: 2019-05-09

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