Key points are not available for this paper at this time.
Abstract This paper presents a Bayesian hierarchical model designed to address spatial location perturbation in Demographic and Health Survey (DHS) data. This perturbation can matter significantly in analyses that rely on fine-scale geographic proximity and precise distance calculations. The method builds on measurement error modelling by incorporating prior information on the perturbation process reported by the DHS programme. To enhance estimation, we further adjust for error using a Moran’s I operator matrix, which links cluster mid-points with areal-level spatial units such as municipal districts or upazilas. Simulation studies and empirical applications with DHS data from Bangladesh and Ghana demonstrate that the proposed method produces more concise and reliable posterior estimates at the cluster level. While it does not recover the true cluster locations, the approach effectively mitigates bias introduced by spatial perturbation and provides a robust adjustment for measurement error. This framework offers a balance between protecting confidentiality and ensuring analytical validity, which supports more robust spatial inference in population health research.
Bakar et al. (Sun,) studied this question.