Spatial Cluster-based Copula Model to Interpolate Skewed Conditional Spatial Random Field

TL;DR

This paper introduces a novel spatial interpolation method using cluster-based copulas and modified kernels to effectively estimate skewed spatial data with missing values, demonstrated on Delhi's air pollution data.

Contribution

It develops a new spatial copula model combining clustering, Bayesian ideas, and kernel methods for improved interpolation of skewed spatial fields.

Findings

Effective interpolation of skewed spatial data demonstrated on air pollution data.

Enhanced spatial homogeneity and continuity in the interpolated surface.

Improved efficiency and compatibility of the copula-based interpolation algorithm.

Abstract

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.