Spatial misalignments arise from data aggregation or attempts to align misaligned data, leading to information loss. We propose a disaggregation framework that combines the finite element method (FEM) with a first-order Taylor approximation via integrated nested Laplace approximation (INLA). In landslide studies, landslide occurrences are often aggregated into counts based on slope units, reducing spatial detail. Our framework examines 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 these, we estimate the full covariate field using \textit{Value Plugin, Joint Uncertainty, and Uncertainty Plugin} methods, with the latter two accounting for uncertainty propagation and showing superior performance. Even under model misspecification (i.e.\ modelling a nonlinear field as linear), these methods remain more robust. Whenever possible, 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 R-INLA \ extension packages.

翻译：空间失配源于数据聚合或对失配数据进行对齐的尝试，常导致信息损失。本文提出一种解聚框架，将有限元方法（FEM）与通过集成嵌套拉普拉斯近似（INLA）实现的一阶泰勒近似相结合。在滑坡研究中，滑坡事件常基于斜坡单元被聚合成计数数据，从而降低了空间细节。本框架在四种协变量场情景下检验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）}。前三种涉及聚合过程，后两种则存在不完整的协变量场。针对后两种情况，我们采用\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，并展现出更优的性能。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，这些方法仍表现出更强的稳健性。在可能的情况下，应优先使用点模式观测数据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应用，并能与R-INLA \ 扩展包无缝集成。