Confronta i metodi

	Urban Growth Boundary Analysis	Land-Use Change Modeling
Campo	Urban Studies	Human Geography
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1997	2002
Ideatore	Cellular-automata urban growth lineage (Clarke et al., SLEUTH); UGB policy from Oregon land-use planning	Peter H. Verburg and colleagues (CLUE-S); broader land-change-science community
Tipo	Scenario simulation and evaluation of urban containment policies	Family of spatially explicit models simulating land-use and land-cover change
Fonte seminale	Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24(2), 247–261.	Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., & Mastura, S. S. A. (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental Management, 30(3), 391–405.
Alias	UGB Analysis, Urban Containment Modelling, Growth Boundary Scenario Simulation, Urban Containment Policy Evaluation	Land Change Modeling, LUCC Simulation, Spatial Land-Use Allocation Modeling, Land-Use Scenario Modeling
Correlati	4	4
Sintesi	Urban growth boundary (UGB) analysis uses spatial simulation to design and evaluate containment lines that separate land where urban development is allowed from land to be kept rural. Built on the cellular-automata urban-growth tradition exemplified by Clarke, Hoppen, and Gaydos's self-modifying SLEUTH model, it calibrates how a region urbanizes, then imposes candidate boundaries as hard or soft constraints and simulates land conversion forward in time. By comparing scenarios with and without a boundary, the method estimates how much farmland and open space a UGB would protect, how much it would densify the interior, and whether it would push leapfrog development beyond the line.	Land-use change modeling is the umbrella family of methods that simulate how the land surface is converted between uses — forest to farmland, farmland to city — by combining where change is likely with how much change is demanded. A typical model statistically relates observed change to spatial drivers such as slope, roads, and population, sets future demand for each land-use class from scenarios, and then allocates that demand across space to the most suitable cells, iterating until supply meets demand. The CLUE-S model of Verburg and colleagues, alongside the Land Change Modeler and SLEUTH, exemplifies this demand-plus-allocation architecture that underpins much of land-change science.