Geographical variable distributions often exhibit both macroscale geographic smoothness and microscale discontinuities or local step changes. Nonetheless, accounting for both effects in a unified statistical model is challenging, especially when the data under study involve a multiscale structure and non-Gaussian response variables. This study develops a locally adaptive spatial multilevel logistic model to examine binomial response variables that integrates an innovative locally adaptive spatial econometric model with a multilevel model. It takes into account global spatial autocorrelation, local step changes, and vertical dependence effects arising from the multiscale data structure. Another appealing feature is that the spatial correlation structure, implied by a spatial weights matrix, is learned along with other model parameters via an iterative estimation algorithm, rather than being presumed to be invariant. Bayesian Markov chain Monte Carlo (MCMC) samplers are derived to implement this new spatial multilevel logistic model. A data augmentation approach, drawing on recently devised Pólya-gamma distributions, is adopted to reduce computational burdens of calculating binomial likelihoods with a logit link function. The validity of the developed model is evaluated by a set of simulation experiments, before being applied to analyze self-rated health for the elderly in Shijiazhuang, the capital city of Hebei Province, China. Model estimation results highlight a nuanced geography of self-rated health and identify a range of individual- and area-level correlates of health for the elderly. Key Words: geography of health, local spatial modeling, multilevel models, spatial autocorrelation, spatial econometrics.

地理变量分布通常同时显示宏观尺度的地理平滑度和微观尺度的不连续性或局部阶跃变化。尽管如此，要在统一的统计模型中同时考虑这两种影响是具有挑战性的，特别是当所研究的数据涉及多尺度结构和非高斯响应变量时。这项研究开发了一种局部自适应空间多级逻辑模型，以研究将创新的局部自适应空间计量经济模型与多级模型集成在一起的二项式响应变量。它考虑了全局空间自相关，局部步长变化以及多尺度数据结构产生的垂直依存关系。另一个吸引人的特征是空间权重矩阵所暗示的空间相关结构，通过迭代估计算法，与其他模型参数一起学习，而不是被认为是不变的。贝叶斯马尔可夫链蒙特卡洛（MCMC）采样器被派生来实现这种新的空间多级逻辑模型。采用了一种基于最近设计的Pólya-gamma分布的数据增强方法，以减少计算二项式似然的计算负担。Logit链接功能。在将其用于分析河北省省会石家庄市老年人的自我评估健康水平之前，通过一组模拟实验评估了该模型的有效性。模型估计结果突出显示了自我评估健康状况的细微差别，并确定了老年人健康状况的一系列个人和区域关联。关键词：健康地理，局部空间建模，多层模型，空间自相关，空间计量经济学。