MAPPING FOOD INSECURITY IN THE BRAZILIAN AMAZON USING A SPATIAL ITEM FACTOR ANALYSIS MODEL

• 成果类型: Article
• 署名作者: Chacon-montalvan, Erick A.; Parry, Luke; Giorgi, Emanuele; Torres, Patricia; Orellana, Jesem Douglas Yamall; Moraga, Paula; Taylor, Benjamin M.
• 署名单位: University Nacional de Ingenieria Lima; Universidade Federal do Para; Lancaster University; Lancaster University; Fundacao Oswaldo Cruz; King Abdullah University of Science & Technology; University College Cork
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/25-AOAS2072
• 发表日期: 2025-12
• 页码: 3438-3463
• 关键词: Continuous spatial variation; factor analysis; Gaussian Processes; kriging; model-based geostatistics; structural equation modeling; BAYESIAN FACTOR-ANALYSIS; RESPONSE THEORY; error

摘要

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 socioeconomic 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.