Spatial misalignment problems arise from both data aggregation and attempts to align misaligned data, leading to information loss. We propose a Bayesian disaggregation framework that links misaligned data to a continuous domain model using an iteratively linearised integration method via integrated nested Laplace approximation (INLA). The framework supports point pattern and aggregated count models under four covariate field scenarios: \textit{Raster at Full Resolution (RastFull), Raster Aggregation (RastAgg), Polygon Aggregation (PolyAgg), and Point Values (PointVal)}. The first three involve aggregation, while the latter two have incomplete fields. For PolyAgg and PointVal, we estimate the full covariate field using \textit{Value Plugin, Joint Uncertainty, and Uncertainty Plugin} methods, with the latter two accounting for uncertainty propagation. These methods demonstrate superior performance, and remain more robust even under model misspecification (i.e.\ modelling a nonlinear field as linear). In landslide studies, landslide occurrences are often aggregated into counts based on slope units, reducing spatial detail. The results indicate that point pattern observations and full-resolution covariate fields should be prioritized. For incomplete fields, methods incorporating uncertainty propagation are preferred. This framework supports landslide susceptibility and other spatial mapping, integrating seamlessly with INLA-extension packages.

翻译：空间失准问题既源于数据聚合，也源于对失准数据进行对齐的尝试，这会导致信息损失。我们提出一个贝叶斯解聚框架，通过集成嵌套拉普拉斯近似（INLA）的迭代线性化积分方法，将失准数据与连续域模型相连接。该框架支持四种协变量场情景下的点模式和聚合计数模型：\textit{全分辨率栅格（RastFull）、栅格聚合（RastAgg）、多边形聚合（PolyAgg）和点值（PointVal）}。前三种涉及聚合，而后两种具有不完整的协变量场。对于PolyAgg和PointVal，我们使用\textit{值插件法、联合不确定性法和不确定性插件法}来估计完整的协变量场，其中后两种方法考虑了不确定性传播。这些方法展现出优越的性能，即使在模型设定错误（例如将非线性场建模为线性）的情况下也保持更强的稳健性。在滑坡研究中，滑坡发生通常基于斜坡单元聚合成计数，从而降低了空间细节。结果表明，应优先考虑点模式观测和全分辨率协变量场。对于不完整的协变量场，推荐采用包含不确定性传播的方法。该框架支持滑坡易发性及其他空间制图，并能与INLA扩展包无缝集成。