  Interpolating a skewed conditional spatial random field with missing data is
cumbersome in the absence of Gaussianity assumptions. Maintaining spatial
homogeneity and continuity around the observed random spatial point is also
challenging, especially when interpolating along a spatial surface, focusing on
the boundary points as a neighborhood. Otherwise, the point far away from one
may appear the closest to another. As a result, importing the hierarchical
clustering concept on the spatial random field is as convenient as developing
the copula with the interface of the Expectation-Maximization algorithm and
concurrently utilizing the idea of the Bayesian framework. This paper
introduces a spatial cluster-based C-vine copula and a modified Gaussian kernel
to derive a novel spatial probability distribution. Another investigation in
this paper uses an algorithm in conjunction with a different parameter
estimation technique to make spatial-based copula interpolation more compatible
and efficient. We apply the proposed spatial interpolation approach to the air
pollution of Delhi as a crucial circumstantial study to demonstrate this newly
developed novel spatial estimation technique.
