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Model-based small area estimation at two scales using Moran's spatial filtering.

Phuong N Truong1, Alfred Stein1

  • 1Department of Earth observation science, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands.

Spatial and Spatio-Temporal Epidemiology
|November 4, 2019
PubMed
Summary

This study introduces Moran's spatial filtering for small area estimation of spatial binary data. The deterministic model proved more accurate and simpler for estimating child underweight in Vietnamese districts.

Keywords:
Childhood malnutritionMoran's spatial filteringSample survey dataSmall area estimationSpatial binary data

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Area of Science:

  • Spatial Epidemiology
  • Public Health
  • Statistical Modeling

Background:

  • Estimating spatial binary data in small areas is challenging, especially when including covariates.
  • Existing methods struggle to effectively model complex spatial dependencies and covariate effects.

Purpose of the Study:

  • To propose and evaluate Moran's spatial filtering for modeling two-scale spatial binary data.
  • To develop and compare two novel models for small area estimation using spatial filtering.
  • To apply these models to estimate child underweight in Vietnamese districts.

Main Methods:

  • Development of two models: one with deterministic sample size estimation, another with multinomial random sample size.
  • Application of Moran's spatial filtering to model spatial binary data.
  • Utilizing eigenvector maps to enhance parameter estimation and account for spatial spillover effects.

Main Results:

  • The deterministic estimation model demonstrated superior accuracy and simplicity compared to the random sample size model.
  • Eigenvector maps were found to improve model parameter estimation and incorporate covariate effects.
  • District-level predictions identified mountainous areas with a high prevalence of underweight children in 2014.

Conclusions:

  • The proposed Moran's spatial filtering models offer effective alternatives for small area estimation of spatial binary data.
  • The deterministic model is recommended for its balance of accuracy and simplicity.
  • The findings highlight the spatial distribution of child underweight in Vietnam, emphasizing vulnerable regions.