Accounting for Multiple Covariates in Non-Stationary Geostatistical Modelling

TL;DR

This paper advances non-stationary geostatistical modeling by incorporating multiple covariates and proposing new methods for constructing non-stationary processes, demonstrated through simulations and malaria data analysis.

Contribution

It introduces novel approaches for modeling non-stationarity with multiple covariates in geostatistics, expanding beyond existing methods.

Findings

Proper selection of non-stationary processes improves model accuracy.

Simulation studies highlight the importance of process choice.

Application to malaria data demonstrates practical utility.

Abstract

Model-based geostatistics (MBG) is a subfield of spatial statistics focused on predicting spatially continuous phenomena using data collected at discrete locations. Geostatistical models often rely on the assumptions of stationarity and isotropy for practical and conceptual simplicity. However, an alternative perspective involves considering non-stationarity, where statistical characteristics vary across the study area. While previous work has explored non-stationary processes, particularly those leveraging covariate information to address non-stationarity, this research expands upon these concepts by incorporating multiple covariates and proposing different ways for constructing non-stationary processes. Through a simulation study, the significance of selecting the appropriate non-stationary process is demonstrated. The proposed approach is then applied to analyse malaria prevalence data in Mozambique, showcasing its practical utility