GeoAdjust: Adjusting for Positional Uncertainty in Geostatistial Analysis of DHS Data

TL;DR

GeoAdjust is an R package that performs fast Bayesian geostatistical analysis of DHS survey data, explicitly accounting for GPS positional uncertainty to improve spatial inference.

Contribution

It introduces the first software specifically designed to handle positional uncertainty in DHS geostatistical data using empirical Bayesian methods.

Findings

Supports multiple likelihood functions including Gaussian, binomial, and Poisson.

Allows flexible model specification and hyperparameter tuning.

Enables prediction at unobserved locations considering positional uncertainty.

Abstract

The R-package GeoAdjust https://github.com/umut-altay/GeoAdjust-package implements fast empirical Bayesian geostatistical inference for household survey data from the Demographic and Health Surveys Program (DHS) using Template Model Builder (TMB). DHS household survey data is an important source of data for tracking demographic and health indicators, but positional uncertainty has been intentionally introduced in the GPS coordinates to preserve privacy. GeoAdjust accounts for such positional uncertainty in geostatistical models containing both spatial random effects and raster- and distance-based covariates. The R package supports Gaussian, binomial and Poisson likelihoods with identity link, logit link, and log link functions respectively. The user defines the desired model structure by setting a small number of function arguments, and can easily experiment with different hyperparameters for the priors. GeoAdjust is the first software package that is specifically designed to address positional uncertainty in the GPS coordinates of point referenced household survey data. The package provides inference for model parameters and can predict values at unobserved locations.