Hi J.B.K.,

By applying "Spatial Modeling"-typed methods based on Eigenfunction Spatial Analysis. Here is an exerpt from https://www.researchgate.net/profile/Daniel_Griffith3/publication/23539845_Semiparametric_filtering_of_spatial_autocorrelation_The_eigenvector_approach/links/55d4a29908ae6788fa352153/Semiparametric-filtering-of-spatial-autocorrelation-The-eigenvector-approach.pdf :

" In the context of spatial regression analysis, several methods can be used to control for the statistical effects of spatial dependencies among observations. Maximum likelihood or Bayesian approaches account for spatial dependencies in a parametric framework, whereas recent spatial filtering approaches focus on nonparametrically removing spatial autocorrelation. In this paper we propose a semiparametric spatial filtering approach that allows researchers to deal explicitly with (a) spatially lagged autoregressive models and (b) simultaneous autoregressive spatial models. As in one non-parametric spatial filtering approach, a specific subset of eigenvectors from a transformed spatial link matrix is used to capture dependencies among the disturbances of a spatial regression model. However, the optimal subset in the proposed filtering model is identified more intuitively by an objective function that minimizes spatial autocorrelation rather than maximizes a model fit."

More information may be found by following these links:

What is interesting: the common mathematical foundations shared by Spatial Modeling approaches and Spectral Clustering and Multidimensional Scaling (MDS) and Manifold Learning ones...

Hoping this will be helpfull.

Article Semiparametric Filtering of Spatial Autocorrelation: The Eig...

Stephane i appreciate your response, i will follow up from there.