arXiv subjects

Marian Scott

Publications and source records attributed to Marian Scott.

4 recordsLinked to original sources

arXiv2025

A Bayesian INLA-SPDE Approach to Spatio-Temporal Point-Grid Fusion with Change-of-Support and Misaligned Covariates

We propose a spatio-temporal data-fusion framework for point data and gridded data with variables observed on different spatial supports. A latent Gaussian field with a Mat\'ern-SPDE prior provides a continuous space representation, while source-specific observation operators map observations to both point measurements and gridded averages, addressing change-of-support and covariate misalignment. Additionally incorporating temporal dependence enables prediction at unknown locations and time points. Inference and prediction are performed using the Integrated Nested Laplace Approximation and the Stochastic Partial Differential Equations approach, which delivers fast computation with uncertainty quantification. Our contributions are: a hierarchical model that jointly fuses multiple data sources of the same variable under different spatial and temporal resolutions and measurement errors, and a practical implementation that incorporates misaligned covariates via the same data fusion framework allowing differing covariate supports. We demonstrate the utility of this framework via simulations calibrated to realistic sensor densities and spatial coverage. Using the simulation framework, we explore the stability and performance of the approach with respect to the number of time points and data/covariate availability, demonstrating gains over single-source models through point and gridded data fusion. We apply our framework to soil moisture mapping in the Elliot Water catchment (Angus, Scotland). We fuse in-situ sensor data with aligned and misaligned covariates, satellite data and elevation data to produce daily high resolution maps with uncertainty.

stat.ME↗

arXiv2025

Spatio-temporal fusion of reanalysis and in situ data for censored threshold exceedances of PM2.5

Data fusion models are widely used in air quality monitoring to integrate in situ and large-scale gridded products, offering spatially complete and temporally detailed estimates. However, traditional Gaussian-based models often underestimate extreme pollution values, leading to biased risk assessments. To address this, we present a Bayesian hierarchical data fusion framework rooted in extreme value theory, using the Dirac-delta generalised Pareto distribution to jointly account for threshold and non-threshold exceedances while preserving the timing of exceedance and non-exceedance episodes. Our model is used to describe and predict censored threshold exceedances of PM2.5 pollution in the Greater London region by using CAMS atmospheric composition reanalysis, and in situ observation stations from the automatic urban and rural network (AURN) run by the UK government. Key features of our approach include combining data with varying spatio-temporal resolutions and fully accounting for parameter uncertainties. Results show that our model outperforms Gaussian-based alternatives and standalone reanalysis data in predicting threshold exceedances at the majority of observation sites and can even result in improved spatial patterns of PM2.5 pollution than those discernible from the background data. Moreover, our approach captures greater variability and spatial patterns, such as higher PM2.5 concentrations near coastal areas, which are not evident in the reanalysis data alone.

stat.AP↗

arXiv2024

A bivariate spatial extreme mixture model for unreplicated heavy metal soil contamination

Geostatistical models for multivariate applications such as heavy metal soil contamination work under Gaussian assumptions and may result in underestimated extreme values and misleading risk assessments (Marchant et al, 2011). A more suitable framework to analyse extreme values is extreme value theory (EVT). However, EVT relies on replications in time, which are generally not available in geochemical datasets. Therefore, using EVT to map soil contamination requires adaptation to be used in the usual single-replicate data framework of soil surveys. We propose a bivariate spatial extreme mixture model to model the body and tail of contaminant pairs, where the tails are described using a stationary generalised Pareto distribution. We demonstrate the performance of our model using a simulation study and through modelling bivariate soil contamination in the Glasgow conurbation. Model results are given as maps of predicted marginal concentrations and probabilities of joint exceedance of soil guideline values. Marginal concentration maps show areas of elevated lead levels along the Clyde River and elevated levels of chromium around the south and southeast villages such as East Kilbride and Wishaw. The joint probability maps show higher probabilities of joint exceedance to the south and southeast of the city centre, following known legacy contamination regions in the Clyde River basin.

stat.AP↗

arXiv2019

Exploiting new forms of data to study the private rented sector: strengths and limitations of a database of rental listings

Reviews of official statistics for UK housing have noted that developments have not kept pace with real-world change, particularly the rapid growth of private renting. This paper examines the potential value of big data in this context. We report on the construction of a dataset from the on-line adverts of one national lettings agency, describing the content of the dataset and efforts to validate it against external sources. Focussing on one urban area, we illustrate how the dataset can shed new light on local changes. Lastly, we discuss the issues involved in making more routine use of this kind of data.

stat.AP↗