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K nearest neighbour and Regression Kriging


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Hello every body!

I study topic: Estimation forest base on K nearest neighbour and Regression Kriging method. I search in internet and literature about K nearest neighbour and Regression Kriging. They use R software to process. I know little about it. I want use another software to process. Example: Arcgis. I dont know Arcgis can process it?

If someone know how to process K nearest neighbour and Regression Kriging in Arcgis. Please help me (stuff, tutorial or guide....).

Regard.

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The k-NN algorithm can also be adapted for use in estimating continuous variables. One such implementation uses an inverse distance weighted average of the k-nearest multivariate neighbors. This algorithm functions as follows:

  • Compute Euclidean or Mahalanobis distance from target plot to those that were sampled.
  • Order samples taking for account calculated distances.
  • Choose heuristically optimal k nearest neighbor based on RMSE done by cross validation technique.
  • Calculate an inverse distance weighted average with the k-nearest multivariate neighbors.

Using a weighted k-NN also significantly improves the results: the class (or value, in regression problems) of each of the k nearest points is multiplied by a weight proportional to the inverse of the distance between that point and the point for which the class is to be predicted.

http://en.wikipedia....ghbor_algorithm

if you looking for idw tools in arcgis, it's under interpolation toolset of spatial analyst tools ;)

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Natural neighbor interpolation is a method of spatial interpolation, developed by Robin Sibson. The method is based on Voronoi tessellation of a discrete set of spatial points. This has advantages over simpler methods of interpolation, such as nearest neighbor, in that it provides a more smooth approximation to the underlying "true" function.

http://en.wikipedia.org/wiki/Natural_neighbor

Natural-neighbors-coefficients-example.png

it's under interpolation toolset of spatial analyst tools ;)

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  • 3 months later...

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