Available with Geostatistical Analyst license.
Summary
Fits the specified order (zero, first, second, third, and so on) polynomial, each within specified overlapping neighborhoods, to produce an output surface.
Usage
Use Local Polynomial Interpolation when your dataset exhibits short-range variation.
Global Polynomial Interpolation is useful for creating smooth surfaces and identifying long-range trends in the dataset. However, in earth sciences, the variable of interest usually has short-range variation in addition to long-range trend. When the dataset exhibits short-range variation, Local Polynomial Interpolation maps can capture the short-range variation.
Syntax
arcpy.ga.LocalPolynomialInterpolation(in_features, z_field, {out_ga_layer}, {out_raster}, {cell_size}, {power}, {search_neighborhood}, {kernel_function}, {bandwidth}, {use_condition_number}, {condition_number}, {weight_field}, {output_type})
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 in the Environments by the Cell Size parameter. 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 |
search_neighborhood (Optional) | Defines which surrounding points will be used to control the output. Standard is the default. The following are Search Neighborhood classes: SearchNeighborhoodStandard, SearchNeighborhoodSmooth, SearchNeighborhoodStandardCircular, and SearchNeighborhoodSmoothCircular. Standard
Smooth
Standard Circular
Smooth Circular
| Geostatistical Search Neighborhood |
kernel_function (Optional) |
The kernel function used in the simulation.
| String |
bandwidth (Optional) | Used to specify the maximum distance at which data points are used for prediction. With increasing bandwidth, prediction bias increases and prediction variance decreases. | Double |
use_condition_number (Optional) | Option to control the creation of prediction and prediction standard errors where the predictions are unstable. This option is only available for polynomials of order 1, 2, and 3.
| Boolean |
condition_number (Optional) | Every invertible square matrix has a condition number that indicates how inaccurate the solution to the linear equations can be with a small change in the matrix coefficients (it can be due to imprecise data). If the condition number is large, a small change in the matrix coefficients results in a large change in the solution vector. | Double |
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 |
output_type (Optional) | Surface type to store the interpolation results. For more information about the output surface types, see What output surface types can the interpolation models generate?
| String |
Code sample
Interpolate point features onto a rectangular raster.
import arcpy
arcpy.env.workspace = "C:/gapyexamples/data"
arcpy.LocalPolynomialInterpolation_ga(
"ca_ozone_pts", "OZONE", "outLPI", "C:/gapyexamples/output/lpiout", "2000",
"2", arcpy.SearchNeighborhoodSmooth(300000, 300000, 0, 0.5), "QUARTIC",
"", "", "", "", "PREDICTION")
Interpolate point features onto a rectangular raster.
# Name: LocalPolynomialInterpolation_Example_02.py
# Description: Local Polynomial interpolation fits many polynomials, each
# within specified overlapping neighborhoods.
# 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 = "outLPI"
outRaster = "C:/gapyexamples/output/lpiout"
cellSize = 2000.0
power = 2
kernelFunction = "QUARTIC"
bandwidth = ""
useConNumber = ""
conNumber = ""
weightField = ""
outSurface = "PREDICTION"
# Set variables for search neighborhood
majSemiaxis = 300000
minSemiaxis = 300000
angle = 0
smoothFactor = 0.5
searchNeighbourhood = arcpy.SearchNeighborhoodSmooth(majSemiaxis, minSemiaxis,
angle, smoothFactor)
# Execute LocalPolynomialInterpolation
arcpy.LocalPolynomialInterpolation_ga(inPointFeatures, zField, outLayer, outRaster,
cellSize, power, searchNeighbourhood,
kernelFunction, bandwidth, useConNumber,
conNumber, weightField, outSurface)
Environments
Licensing information
- Basic: Requires Geostatistical Analyst
- Standard: Requires Geostatistical Analyst
- Advanced: Requires Geostatistical Analyst