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interpolationKriging



Description:
Based on spatial autocorrelation in data, Kriging uses a semivariogram model to perform an unbiased estimation of un-sampled points in a limited region. When spatial autocorrelation or a directional trend exists in sample points, Kriging is the best interpolation method. You can create an interpolationKrigingResult resource by implementing the POST request on this resource.

Interpolation analysis:
Kriging methods: Detailed Interpolation
Pixel format: Pixel format for result grid dataset storage.
Interpolation field:
Scale ratio:
Resolution: Resolution for interpolation.
Bounds: Bounds for interpolation.
Search mode:
Search radius: While calculating the Z value of a position, all points in the circle, with the specified position as center and the specified search radius being as radius, will be involved in the interpolation.
Expected count: While calculating the Z value of a position, N points in the circle, with the specified position as the center, will be involved in the interpolation.
Max point count:
Max points in block
Rotation:
Nugget effect:
Range:
Sill:
Attribute filter: Only points satisfying certain conditions will participate in the interpolation.
ID filter: Only points with ID values satisfying certain conditions will participate in the interpolation.
Result datasource:
Result dataset:
Clipping datasource:
Clipping dataset: The input clip dataset needs to overlap with the dataset to be analyzed.
  
HTTP methods

Output formats