Year: 2025
Type: article
Abstract: The increasing availability of complex survey data, and the continued need for estimates of demographic and health indicators at a fine spatial and temporal scale, has led to the need for spatio-temporal smoothing methods that acknowledge the manner in which the data were collected. The open source R package SUMMER implements a variety of methods for spatial or spatio-temporal smoothing of survey outcomes. In this paper, we focus primarily on demographic and health indicators. Our methods are particularly useful for data from Demographic Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). We build upon functions within the survey package, and use INLA for fast Bayesian computation. This paper includes a brief overview of these methods and illustrates the workflow of processing surveys, fitting space-time smoothing models for both binary and composite indicators, and visualizing results with both simulated data and DHS surveys.
Source: The R Journal
DOI: 10.32614/rj-2025-005
Authors: Zehang Richard Li, Bryan D Martin, Tracy Qi Dong, Geir-Arne Fuglstad, Jessica Godwin, John Paige, Andrea Riebler, Samuel J Clark, Jon Wakefield
Language: English
Topic: Data Analysis with R
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7093106254