Local Polynomial Interpolation (Geostatisical Analyst)
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 shortrange variation.

Global Polynomial Interpolation is useful for creating smooth surfaces and identifying longrange trends in the dataset. However, in earth sciences the variable of interest usually has shortrange variation in addition to longrange trend. When the dataset exhibits shortrange variation, Local Polynomial Interpolation maps can capture the shortrange variation.
Syntax
Parameter  Explanation  Data Type 
in_features 
The input point features containing the zvalues 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 zvalues or mvalues.  Field 
out_ga_layer 
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 
search_neighborhood (Optional) 
Defines which surrounding points will be used to control the output. There are two options: Standard and Smooth. Standard is the default. This is a Search Neighborhood class (SearchNeighborhoodStandard and SearchNeighborhoodSmooth). Standard
Smooth
 Geostatistical Search Neighborhood 
kernel_function (Optional) 
The kernel function used in the simulation.
 String 
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 
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 
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.
 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) # Check out the ArcGIS Geostatistical Analyst extension license arcpy.CheckOutExtension("GeoStats") # Execute LocalPolynomialInterpolation arcpy.LocalPolynomialInterpolation_ga(inPointFeatures, zField, outLayer, outRaster, cellSize, power, searchNeighbourhood, kernelFunction, bandwidth, useConNumber, conNumber, weightField, outSurface)