Moving Window Kriging (Geostatisical Analyst)
Summary
Recalculates the Range, Nugget, and Partial Sill semivariogram parameters based on a smaller neighborhood, moving through all location points.
Usage
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The geostatistical model source is either a geostatistical layer or a geostatistical model (XML).
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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 scripting, the GeostatisticalDatasets ArcPy class will be useful for populating the Input dataset(s) parameter.
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For data formats that support Null values, such as file and personal 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.
Syntax
| Parameter | 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:
| 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 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 |
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
Predict values at select point locations.
import arcpy
arcpy.env.workspace = "C:/gapyexamples/data"
arcpy.GAMovingWindowKriging_ga(
"C:/gapyexamples/data/kriging.lyr", "C:/gapyexamples/data/ca_ozone_pts.shp OZONE",
"C:/gapyexamples/data/obs_pts.shp", "10", "C:/gapyexamples/output/outMWK", "", "")
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"
# Check out the ArcGIS Geostatistical Analyst extension license
arcpy.CheckOutExtension("GeoStats")
# Execute MovingWindowKriging
arcpy.GAMovingWindowKriging_ga(inLayer, inPoints, obsPoints, maxNeighbors,
outPoints)