Global Polynomial Interpolation (Geostatisical Analyst)
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
| 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
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", "")
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)