Bayesian Hierarchical Modelling of Municipal Infrastructure Asset Adoption Rates in Senegal: A Methodological Evaluation Dataset (2000–2026)

{ "background": "Municipal infrastructure asset management in developing nations often relies on aggregated national statistics, which obscure sub-national variation and hinder localised planning. A methodological gap exists for robust, probabilistic models that can estimate adoption rates of diverse infrastructure systems at the municipal level while accounting for spatial and temporal heterogeneity.", "purpose and objectives": "This Data Descriptor presents a structured dataset and methodological framework for evaluating municipal infrastructure adoption. The primary objective is to provide a reproducible, Bayesian hierarchical modelling approach to estimate and project adoption rates for water, sanitation, and transport assets, enabling uncertainty quantification for engineering decision-making.", "methodology": "A longitudinal dataset was constructed from national surveys, municipal records, and engineering project inventories. The core statistical model is a Bayesian hierarchical logistic growth model: (pijt) = \ + \β1i Xijt + \εijt, where pijt is the adoption rate for infrastructure type i in municipality j at time t, \ \~ (\μ\α, \σ\α) are municipality-level random effects, and \εijt captures observation error. Hamiltonian Monte Carlo sampling was used for inference.", "findings": "The methodological evaluation demonstrates that the model successfully captures significant spatial clustering in adoption trajectories, with posterior credible intervals for municipal-level growth rates providing actionable uncertainty bounds. A key illustrative result is that the model estimates a central 90% credible interval for the adoption rate of piped water connections in a typical municipality to widen by approximately 15 percentage points when projecting five years forward, highlighting the critical importance of probabilistic forecasting.", "conclusion": "The dataset and modelling framework offer a substantial improvement over deterministic projection methods for infrastructure planning. The Bayesian hierarchical approach formally integrates uncertainty and spatial dependence, providing a more reliable evidence base for municipal engineers and policymakers.", "recommendations": "Adoption of this probabilistic modelling framework