  Changes in land use patterns have significant environmental and socioeconomic
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 between policymakers, land
users, and researchers. We present a methodological synthesis for treating land
use data using a Bayesian approach within spatial and spatio-temporal modeling
frameworks.
  The study tackles the challenges of analyzing 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.
