Moving Window Kriging (Geostatistical Analyst Tools)

Summary

Recalculates the Range, Nugget, and Partial Sill semivariogram parameters based on a smaller neighborhood, moving through all location points.

Learn more about how Moving Window Kriging works

Usage

The geostatistical model source is either a geostatistical layer or a geostatistical model (XML) representing a kriging model other than empirical Bayesian kriging.
The input dataset must contain more than 10 points for the tool to execute. However, the tool is most effective with large datasets that have nonstationary trends.
In Python, the GeostatisticalDatasets ArcPy class will be useful for populating the Input dataset(s) parameter.
For data formats that support Null values, such as file geodatabase feature classes, a Null value will be used to indicate that a prediction could not be made for that location or that the value should be ignored when used as input. For data formats that do not support Null values, such as shapefiles, the value of -1.7976931348623158e+308 is used (this is the negative of the C++ defined constant DBL_MAX) to indicate that a prediction could not be made for that location.

Label	Explanation	Data type
Input geostatistical model source	The geostatistical model source to be analyzed.	File; Geostatistical Layer
Input dataset(s)	The name of the input datasets and field names used in the creation of the output layer.	Geostatistical Value Table
Input point observation locations	Point locations where predictions will be performed.	Feature Layer
Maximum neighbors to include	Number of neighbors to use in the moving window.	Long
Output feature class	Feature class storing the results.	Feature Class
Output 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
Output surface raster (Optional)	The prediction values in the output feature class are interpolated onto a raster using the Local polynomial interpolation method.	Raster Dataset

arcpy.ga.GAMovingWindowKriging(in_ga_model_source, in_datasets, in_locations, neighbors_max, out_featureclass, {cell_size}, {out_surface_grid})

Name	Explanation	Data type
in_ga_model_source	The geostatistical model source to be analyzed.	File; Geostatistical Layer
in_datasets	A `GeostatisticalDatasets` object. Alternatively, it can be a semicolon-delimited string of elements. Each element is comprised of the following components: The catalog path and name to a dataset or the name of a layer in the current table of contents, followed by a space. A sequence of field names, each field name separated by a space. In the case of a raster, the cell values will be used.	Geostatistical Value Table
in_locations	Point locations where predictions will be performed.	Feature Layer
neighbors_max	Number of neighbors to use in the moving window.	Long
out_featureclass	Feature class storing the results.	Feature Class
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
out_surface_grid (Optional)	The prediction values in the output feature class are interpolated onto a raster using the Local polynomial interpolation method.	Raster Dataset

Code sample

MovingWindowKriging example 1 (Python window)

Predict values at select point locations.

import arcpy
arcpy.env.workspace = "C:/gapyexamples/data"
arcpy.ga.GAMovingWindowKriging(
    "C:/gapyexamples/data/kriging.lyr", "C:/gapyexamples/data/ca_ozone_pts.shp OZONE",
    "C:/gapyexamples/data/obs_pts.shp", "10", "C:/gapyexamples/output/outMWK", "", "")

MovingWindowKriging example 2 (stand-alone script)

Predict values at select point locations.

# Name: MovingWindowKriging_Example_02.py
# Description: The kriging model is automatically estimated for each neighborhood
#              as the kriging interpolation moves through all the location points.
# Requirements: Geostatistical Analyst extension

# Import system modules
import arcpy

# Set environment settings
arcpy.env.workspace = "C:/gapyexamples/data"

# Set local variables
inLayer = "C:/gapyexamples/data/kriging.lyr"
inPoints = "C:/gapyexamples/data/ca_ozone_pts.shp OZONE"
obsPoints = "C:/gapyexamples/data/obs_pts.shp"
maxNeighbors = 10
outPoints = "C:/gapyexamples/output/outMWK"

# Run MovingWindowKriging
arcpy.ga.GAMovingWindowKriging(inLayer, inPoints, obsPoints, maxNeighbors,
                               outPoints)

Environments

Cell Size, Coincident Points, Current Workspace, Extent, Geographic Transformations, Mask, Output Coordinate System, Parallel Processing Factor, Scratch Workspace, Snap Raster

Licensing information