Fast geostatistical inference under positional uncertainty: Analysing DHS household survey data

TL;DR

This paper introduces a fast geostatistical model that accounts for GPS coordinate jittering in DHS survey data, improving inference accuracy for spatial analyses involving Gaussian and non-Gaussian likelihoods.

Contribution

It develops a novel method to incorporate positional uncertainty into geostatistical models and provides an efficient implementation using Template Model Builder.

Findings

The new approach yields more accurate parameter estimates.

It improves predictive performance over ignoring jittering.

The method is computationally fast and practical for real-world data.

Abstract

Household survey data from the Demographic and Health Surveys (DHS) Program is published with GPS coordinates. However, almost all geostatistical analyses of such data ignore that the published GPS coordinates are randomly displaced (jittered). In this short report, we develop a geostatistical model that accounts for the positional uncertainty when analysing DHS surveys, and provide a fast implementation using Template Model Builder. The key focus is inference with Gaussian random fields under positional uncertainty, and our approach works for both Gaussian and non-Gaussian likelihoods. A simulation study with a binomial observation model shows that the new approach performs equally or better than the common approach of ignoring jittering, both in terms of more accurate parameter estimates and improved predictive measures. We demonstrate that the improvement would be larger under stronger jittering. An analysis of contraceptive use in Kenya shows that the approach is fast and easy to use in practice.