A Clipped Gaussian Geo-Classification model for poverty mapping

The importance of discrete spatial models cannot be overemphasized, especially when measuring living standards. The battery of measurements is generally categorical with nearer geo-referenced observations featuring stronger dependencies. This study presents a Clipped Gaussian Geo-Classification (CGG-C) model for spatially-dependent ordered data, and compares its performance with existing methods to classify household poverty using Ghana living standards survey (GLSS 6) data. Bayesian inference was performed on data sampled by MCMC. Model evaluation was based on measures of classification and prediction accuracy. Spatial associations, given some household features, were quantified, and a poverty classification map for Ghana was developed. Overall, the results of estimation showed that many of the statistically significant covariates were generally strongly related with the ordered response variable. Households at specific locations tended to uniformly experience specific levels of poverty, thus, providing an empirical spatial character of poverty in Ghana. A comparative analysis of validation results showed that the CGG-C model (with 14.2% misclassification rate) outperformed the Cumulative Probit (CP) model with misclassification rate of 17.4%. This approach to poverty analysis is relevant for policy design and the implementation of cost-effective programmes to reduce category and site-specific poverty incidence, and monitor changes in both category and geographical trends thereof.KEYWORDS: Ordered responses, spatial correlation, Bayesian estimation via MCMC, Gaussian random fields, poverty classification