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.

离散空间模型的重要性怎么强调都不为过，尤其是在衡量生活水平时。一系列测量结果通常是分类的，具有更接近的地理参考观测值，具有更强的依赖性。本研究提出了用于空间相关有序数据的裁剪高斯地理分类 (CGG-C) 模型，并将其性能与使用加纳生活水平调查 (GLSS 6) 数据对家庭贫困进行分类的现有方法进行比较。对 MCMC 采样的数据进行贝叶斯推理。模型评估基于分类和预测准确性的测量。考虑到一些家庭特征，对空间关联进行了量化，并绘制了加纳的贫困分类图。总体而言，估计结果表明，许多具有统计显着性的协变量通常与有序响应变量密切相关。特定地点的家庭往往都经历特定的贫困水平，从而提供了加纳贫困的经验空间特征。验证结果的比较分析表明，CGG-C模型（误分类率为14.2%）优于累积概率（CP）模型，误分类率为17.4%。这种贫困分析方法与政策设计和实施具有成本效益的计划相关，以减少类别和特定地点的贫困发生率，并监测其类别和地理趋势的变化。