Local Polynomial Interpolation (Geostatisical Analyst)

サマリ

Fits the specified order (zero, first, second, third, and so on) polynomial, each within specified overlapping neighborhoods, to produce an output surface.

How Local Polynomial Interpolation works

使用法

• 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.

構文

LocalPolynomialInterpolation_ga (in_features, z_field, out_ga_layer, {out_raster}, {cell_size}, {power}, {search_neighborhood}, {kernel_function}, {use_condition_number}, {bandwidth}, {condition_number}, {weight_field}, {output_type})

パラメータ	説明	データ タイプ
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	The geostatistical layer produced. This layer is required output only if no output raster is requested.	Geostatistical Layer
out_raster （オプション）	The output raster. This raster is required output only if no output geostatistical layer is requested.	Raster Dataset
cell_size （オプション）	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 （オプション）	The order of the polynomial.	Long
search_neighborhood （オプション）	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 Major semiaxis — The major semi-axis value of the searching neighborhood. Minor semiaxis — The minor semi-axis value of the searching neighborhood. Angle — The angle of rotation for the axis (circle) or semi-major 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 in four sectors. Four sectors shifted - Ellipse divided in four sectors and shifted 45 degrees. Eight sectors - Ellipse divided into eight sectors. Smooth Major semiaxis — The major semi-axis value of the searching neighborhood. Minor semiaxis — The minor semi-axis value of the searching neighborhood. Angle — The angle of rotation for the axis (circle) or semi-major 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 smaller ellipse but inside the largest ellipse are weighted using a sigmoidal function with a value between zero and one.	Geostatistical Search Neighborhood
kernel_function （オプション）	The kernel function used in the simulation. QUARTIC — Fourth-order polynomial function. EXPONENTIAL — The function grows or decays proportionally. GAUSSIAN — Bell-shaped function that falls off quickly towards plus/minus infinity. EPANECHNIKOV — A discontinuous parabolic function. POLYNOMIAL5 — Fifth-order polynomial function. CONSTANT —An indicator function.	String
use_condition_number （オプション）	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. NO_USE_CONDITION_NUMBER —Prediction and prediction standard errors can be created where the predictions are unstable. This is the default. USE_CONDITION_NUMBER —Prediction and prediction standard errors will not be created where the predictions are unstable.	Boolean
bandwidth （オプション）	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 （オプション）	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 （オプション）	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 （オプション）	Surface type to store the interpolation results. PREDICTION —Prediction surfaces are produced from the interpolated values. PREDICTION_STANDARD_ERROR — Standard Error surfaces are produced from the standard errors of the interpolated values. CONDITION_NUMBER —The Spatial condition number surface indicates variation in the numerical model stability and provides additional information on the prediction uncertainty as the prediction standard error surface is created, assuming that the model is correct, which implies that there is no reason for model instability.	String

コードのサンプル

LocalPolynomialInterpolation example 1 (Python window)

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")

LocalPolynomialInterpolation example 2 (stand-alone script)

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)

環境

セル サイズ, 現在のワークスペース, 出力データの座標系, 範囲, テンポラリ ワークスペース, スナップ対象ラスタ, 一致ポイント

ライセンス情報

ArcView: 必須 Geostatistical Analyst

ArcEditor: 必須 Geostatistical Analyst

ArcInfo: 必須 Geostatistical Analyst