• Corpus ID: 254221262

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

@inproceedings{Altay2022FastGI,
  title={Fast geostatistical inference under positional uncertainty: Analysing DHS household survey data},
  author={Umut Altay and John Paige and Andrea Riebler and Geir-Arne Fuglstad},
  year={2022}
}
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… 

References

SHOWING 1-10 OF 23 REFERENCES

Geostatistical inference in the presence of geomasking: A composite-likelihood approach

Estimation of health and demographic indicators with incomplete geographic information.

Influence of Demographic and Health Survey Point Displacements on Distance-Based Analyses

Results suggest that RC outperforms analyses involving naive distance-based covariate assignments by reducing the bias and MSE of the main estimator in most settings.

Geographic displacement procedure and georeferenced data release policy for the Demographic and Health Surveys.

Georeferencing population-based surveys such as the Demographic and Health Surveys (DHS) have many benefits. Most important researchers can analyze respondent locations spatially to identify

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

This work considers approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models, where the latent field is Gaussian, controlled by a few hyperparameters and with non‐Gaussian response variables and can directly compute very accurate approximations to the posterior marginals.

Spatial prediction in the presence of positional error

This paper shows how the predictive distributions of quantities of interest change after allowing for the positional error, and describes scenarios in which positional errors may affect the qualitative conclusions of an analysis.

Spatial Statistics in the Presence of Location Error with an Application to Remote Sensing of the Environment

Techniques for the analysis of spatial data have, to date, tended to ignore any effect caused by error in specifying the spatial locations at which measurements are recorded. This paper reviews the

Markov chain Monte Carlo with the Integrated Nested Laplace Approximation

A novel approach is presented that combines INLA and Markov chain Monte Carlo (MCMC) and can be used to fit models with Laplace priors in a Bayesian Lasso model, imputation of missing covariates in linear models, fitting spatial econometrics models with complex nonlinear terms in the linear predictor and classification of data with mixture models.

TMB: Automatic Differentiation and Laplace Approximation

TMB is an open source R package that enables quick implementation of complex nonlinear random effect (latent variable) models in a manner similar to the established AD Model Builder package, and is designed to be fast for problems with many random effects and parameters.

Confidentiality and spatially explicit data: Concerns and challenges

This paper presents four sometimes conflicting principles for the conduct of ethical and high-quality science using spatially explicit social survey or census data files: protection of confidentiality, the social–spatial linkage, data sharing, and data preservation.