The availability of accurate poverty data at the granular (subdistrict) level remains a significant challenge for official statistics in developing countries, due to limited survey samples, creating information “blind spots” for policy-making. This study aims to address this issue by estimating subdistrict per capita expenditure in Medan Municipality and Langkat Regency by integrating official survey data with geospatial big data. Utilizing a Spatio-Temporal Hierarchical Bayesian Small Area Estimation (ST HB SAE) framework, this research leverages the 2021–2024 National Socioeconomic Survey (Susenas) panel data structure and auxiliary variables from satellite imagery, including Night Time Light (NTL), vegetation index (NDVI), built-up index (NDBI), and air pollutants (NO2). The developed ST HB SAE model incorporates spatial effects (Leroux CAR) and temporal autoregressive (AR1) processes to enhance estimation precision. Results demonstrate that this model significantly outperforms direct estimation and nonspatio-temporal models in terms of Root Mean Squared Error (RMSE) reduction and Coefficient of Variation (CV) stability. Geospatial variables are strongly correlated with welfare indicators, enriching information in sample-deficient areas. Furthermore, a benchmarking process ensures that the estimates are consistent with official regency/municipality level aggregates and meet official statistical standards for operational adoption. This approach offers a cost-effective, robust solution for National Statistical Offices (NSOs) to monitor welfare dynamics in small areas, supporting evidence-based development planning.
Zebua et al. (Mon,) studied this question.