misalignedata

This repository contains the code to reproduce the simulation study in the paper "Coherent Disaggregation and Uncertainty Quantification for Spatially Misaligned Data".

Abstract

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.

Code

The code to perform the simulation in the paper consists of two main files, to be executed in the following order in R:

1. source("compile_mod.R"): This file loads the necessary libraries, data and compiles the INLA models.
2. source("score.R"): This file assesses the models with Squared Error (SE) and Dawid-Sebastiani (DS) scores.

Description

1. load_data.RData: This file contains the Nepal map and the simulated data.
2. covariate.R: This file creates the covariate field.
3. mesh.R : This file creates the mesh.
4. joint_model.R: This file creates the Observation Plugin (OP) models.
5. juvpup.R: This file fits the Joint Uncertainty (JU), Value Plugin (VP) and Uncertainty Plugin (UP) models.
6. juvpup_nl.R: This file fits the JU, VP and UP models with non-linear misspecification (NL).
7. function.R: This file contains the helper functions used in the paper.

Citation

For attribution, please cite this work as: Suen, M. H., Naylor, M., & Lindgren, F. (2025). Coherent Disaggregation and Uncertainty Quantification for Spatially Misaligned Data. arXiv preprint arXiv:2502.10584.

BibTeX citation:

@article{suen2025coherent,
  title={Coherent Disaggregation and Uncertainty Quantification for Spatially Misaligned Data},
  author={Suen, Man Ho and Naylor, Mark and Lindgren, Finn},
  journal={arXiv preprint arXiv:2502.10584},
  year={2025}
}