sandwichr: Spatial prediction in R based on spatial stratified heterogeneity

Abstract

AbstractSpatial prediction is commonly used in social and environmental research to estimate values at unobserved locations using sampling data. However, most existing spatial prediction methods and software packages are based on the assumption of spatial autocorrelation (SAC), which may not apply when spatial dependence is weak or non‐existent. In this article, we develop a modeling framework for spatial prediction based on spatial stratified heterogeneity (SSH), a common feature of geographical variables, as well as an R package called sandwichr that implements this framework. For populations that can be stratified into homogeneous strata, the proposed framework enables the estimation of values for user‐defined reporting units (e.g., administrative units or grid cells) based on the mean of each stratum, even if SAC is weak or absent. The estimated values can be used to create predicted surfaces and mapping. The framework also includes procedures for selecting appropriate stratifications of the populations and assessing prediction uncertainty and model accuracy. The sandwichr package includes functions to implement each step of the framework, allowing users to implement SSH‐based spatial prediction effectively and efficiently. Two case studies are provided to illustrate the effectiveness of the proposed framework and the sandwichr package.