Spatial misalignment arises when datasets are aggregated or collected at different spatial scales, leading to information loss. We develop a Bayesian disaggregation framework that links misaligned data to a continuous-domain model through an iteratively linearised integration scheme implemented with the Integrated Nested Laplace Approximation (INLA). The framework accommodates different ways of handling observations depending on the application, resulting in four variants: (i) \textit{Raster at Full Resolution}, (ii) \textit{Raster Aggregation}, (iii) \textit{Polygon Aggregation} (PolyAgg), and (iv) \textit{Point Values} (PointVal). The first three represent increasing levels of spatial averaging, while the last two address situations with incomplete covariate information. For PolyAgg and PointVal, we reconstruct the covariate field using three strategies -- \textit{Value Plugin}, \textit{Joint Uncertainty}, and \textit{Uncertainty Plugin} -- with the latter two propagating uncertainty. We illustrate the framework with an example motivated by landslide modelling, focusing on methodology rather than interpreting landslide processes. Simulations show that uncertainty-propagating approaches outperform \textit{Value Plugin} method and remain robust under model misspecification. Point-pattern observations and full-resolution covariates are therefore preferable, and when covariate fields are incomplete, uncertainty-aware methods are most reliable. The framework is well suited to landslide susceptibility modelling and other spatial mapping tasks, and integrates seamlessly with INLA-based tools.

翻译：空间错位是指数据集在不同空间尺度上聚合或收集时导致信息丢失的现象。本文提出一种贝叶斯解聚框架，通过基于集成嵌套拉普拉斯近似（INLA）的迭代线性化积分方案，将错位数据与连续域模型相连接。该框架根据应用需求支持四种处理观测数据的方式：（i）全分辨率栅格，（ii）栅格聚合，（iii）多边形聚合（PolyAgg），以及（iv）点值（PointVal）。前三种代表空间平均化程度的递增，后两种则针对协变量信息不完整的情况。对于PolyAgg和PointVal，我们采用三种策略重建协变量场——值插件、联合不确定性和不确定性插件——其中后两种策略可传播不确定性。我们以滑坡建模为例说明该框架，重点在于方法论而非滑坡过程解释。模拟实验表明，传播不确定性的方法优于值插件法，且在模型设定错误时仍保持稳健。因此，点模式观测与全分辨率协变量更为理想；当协变量场不完整时，具备不确定性感知的方法最为可靠。该框架适用于滑坡敏感性建模及其他空间制图任务，并能与基于INLA的工具无缝集成。