Approximate Bayesian Inference for High-Resolution Spatial Disaggregation Using Alternative Data Sources

Anis Pakrashi (), Arnab Hazra (), Sooraj M. Raveendran () and Krishnachandran Balakrishnan ()
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Anis Pakrashi: Pennsylvania State University
Arnab Hazra: Indian Institute of Technology Kanpur
Sooraj M. Raveendran: Indian Institute for Human Settlements
Krishnachandran Balakrishnan: MapSolve AI Bangalore, and Indian Institute for Human Settlements

Journal of Agricultural, Biological and Environmental Statistics, 2025, vol. 30, issue 2, No 15, 576-599

Abstract: Abstract This paper addresses the challenge of obtaining precise demographic information at a fine-grained spatial level, which is a necessity for planning localized public services such as water distribution networks, or understanding local human impacts on the ecosystem. While population sizes are commonly available for large administrative areas, such as wards in India, practical applications often demand knowledge of population density at smaller spatial scales. We explore the integration of alternative data sources, specifically satellite-derived products, including land cover, land use, street density, building heights, vegetation coverage, and drainage density. Using a case study focused on Bangalore City, India, with a ward-level population dataset for 198 wards and satellite-derived sources covering 786,702 pixels at a resolution of 30 m $$\times $$ × 30 m, we propose a semiparametric Bayesian spatial regression model for obtaining pixel-level population estimates. Given the high dimensionality of the problem, exact Bayesian inference is deemed impractical; we discuss an approximate Bayesian inference scheme based on the recently proposed max-and-smooth approach, a combination of Laplace approximation and Markov chain Monte Carlo. A simulation study validates the reasonable performance of our inferential approach. Mapping pixel-level estimates to the ward level demonstrates the effectiveness of our method in capturing the spatial distribution of population sizes. While our case study focuses on a demographic application, the methodology developed here readily applies to count-type spatial datasets from various scientific disciplines where high-resolution alternative data sources are available.

Keywords: Alternative data sources; Approximate Bayesian inference; Spatial Gaussian process; Nonhomogeneous Poisson process; Semiparametric regression; Spatial disaggregation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13253-025-00695-5

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