Urban sprawl is a concept commonly used to describe the physical expansion of urban areas. It is traditionally associated with lower residential density, poorer connectivity, and higher energy costs for heating and transport. From the period of 1980 to 2000, the extent of the built-up area in Europe has increased at a rate three times higher than that of population increase, and urban sprawl is now recognized as a major challenge. However, for policies to address this issue, it is essential to be able to identify and quantify sprawl.

Climate data availability plays a key role in development processes of policies, services, and planning in the agricultural sector. However, data at the spatial or temporal resolution required is often lacking, or certain values are missing. In this work, we propose to use a Bayesian network approach to generate data for missing variables. As a case study, we use relative humidity, which is an important indicator of land suitability for coffee production.

Resource-use decisions affect the ecological and human components of the coupled human and natural system (CHANS), but a critique of some frameworks is that they do not address the complexity and tradeoffs within and between the two systems. Land system architecture (LA) was suggested to account for these tradeoffs at multiple levels/scales. LA and landscape ecology (LE) focus on landscape structure (i.e., composition and configuration of land-use and land-cover change [LULCC]) and the processes (social-ecological) resulting from and shaping LULCC.