interpolationIDW

Description:
IDW is based on the principle that closer things are similar. The principle assumes that two close sample points have similar attributes, and further sample points have less similar attributes.For this method, the value of a cell is a weighted average of the values of sample points nearby. A point closer to the cell at question carries larger weight. This is a simple and effective interpolation method. The computation speed is relatively fast. You can create an interpolationIDWResult resource by implementing the POST request on this resource.

Interpolation analysis:

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
Power:		The higher the power, the finer the result will be. Please specify a vlaue greater than 0.
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

GET
POST
HEAD

Output formats