Unveiling Land Use Dynamics: Insights from a Hierarchical Bayesian Spatio-Temporal Modelling of Compositional Data

Mario Figueira (), Carmen Guarner, David Conesa, Antonio López-Quílez and Tamás Krisztin
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Mario Figueira: University of Valencia
Carmen Guarner: University of Valencia
David Conesa: University of Valencia
Antonio López-Quílez: University of Valencia
Tamás Krisztin: International Institute for Applied Systems Analysis (IIASA)

Journal of Agricultural, Biological and Environmental Statistics, 2025, vol. 30, issue 2, No 3, 283-308

Abstract: Abstract Changes in land use patterns have significant environmental and socio-economic impacts, making it crucial for policymakers to understand their causes and consequences. This study, part of the European LAMASUS (Land Management for Sustainability) project, aims to support the EU’s climate neutrality target by developing a governance model through collaboration among policymakers, land users, and researchers. We present a methodological synthesis for treating land use data using a Bayesian approach within spatial and spatio-temporal modelling frameworks. The study tackles the challenges of analysing land use changes, particularly the presence of zero values and computational issues with large datasets. It introduces joint model structures to address zeros and employs sequential inference and consensus methods for Big Data problems. Spatial downscaling models approximate smaller scales from aggregated data, circumventing high-resolution data complications. We explore Beta regression and Compositional Data Analysis (CoDa) for land use data, review relevant spatial and spatio-temporal models, and present strategies for handling zeros. The paper demonstrates the implementation of key models, downscaling techniques, and solutions to Big Data challenges with examples from simulated data and the LAMASUS project, providing a comprehensive framework for understanding and managing land use changes.

Keywords: Land use; Compositional data; Spatio-temporal models; Downscaling; Big Data; INLA (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13253-025-00678-6

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