Mapping food insecurity in the Brazilian Amazon using a spatial item factor analysis model

TL;DR

This paper introduces a spatial item factor analysis model that accounts for spatial dependence in food insecurity data, enabling better hotspot detection and prediction in the Brazilian Amazon.

Contribution

The paper develops a Bayesian spatial item factor analysis model that captures spatial dependence in latent constructs, improving analysis of food insecurity.

Findings

Model successfully identifies food insecurity hotspots.

Spatial dependence improves prediction accuracy.

Implementation available in R package spifa.

Abstract

Food insecurity, a latent construct defined as the lack of consistent access to sufficient and nutritious food, is a pressing global issue with serious health and social justice implications. Item factor analysis is commonly used to study such latent constructs, but it typically assumes independence between sampling units. In the context of food insecurity, this assumption is often unrealistic, as food access is linked to socio-economic conditions and social relations that are spatially structured. To address this, we propose a spatial item factor analysis model that captures spatial dependence, allowing us to predict latent factors at unsampled locations and identify food insecurity hotspots. We develop a Bayesian sampling scheme for inference and illustrate the explanatory strength of our model by analysing household perceptions of food insecurity in Ipixuna, a remote river-dependent urban centre in the Brazilian Amazon. Our approach is implemented in the R package spifa, with further details provided in the Supplementary Material. This spatial extension offers policymakers and researchers a stronger tool for understanding and addressing food insecurity to locate and prioritise areas in greatest need. Our proposed methodology can be applied more widely to other spatially structured latent constructs.