Methoden vergelijken

	Urban Simulation Model	Markov Land-Use Model
Vakgebied	Urban Studies	Human Geography
Familie	Process / pipeline	Process / pipeline
Jaar van ontstaan	2002	1994
Grondlegger	Paul Waddell (UrbanSim); related lineage: cellular automata and agent-based modelling	Mark R. Muller & John Middleton
Type	Dynamic computational model of urban development and land use	Stochastic projection of land-use/land-cover areas using a transition probability matrix
Oorspronkelijke bron	Waddell, P. (2002). UrbanSim: Modeling urban development for land use, transportation, and environmental planning. Journal of the American Planning Association, 68(3), 297–314.	Muller, M. R., & Middleton, J. (1994). A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9(2), 151–157.
Aliassen	Land-Use Microsimulation, Urban Growth Simulation, Agent-Based Urban Model, Integrated Land-Use Transport Simulation	Markov Chain Land-Cover Model, LULC Transition Matrix Model, CA-Markov Model, Markovian Land Change Model
Verwant	4	4
Samenvatting	Urban simulation models reproduce the dynamics of urban growth and land-use change by simulating, over time, the decisions of agents — households, firms, developers — or the transitions of cells on a grid. They span agent-based models, cellular automata such as SLEUTH, and microsimulation platforms such as Paul Waddell's UrbanSim, which represents individual households and jobs choosing locations through discrete-choice models linked to a transport network. Rather than predicting a single equilibrium, these models let many local rules and choices interact and feed back through prices and accessibility, generating emergent patterns of sprawl, densification, and redevelopment under alternative policies.	A Markov land-use model treats land-use and land-cover change as a stochastic process in which the area in each class evolves according to fixed probabilities of transitioning from one class to another between time steps. Estimated from two dated maps as a transition probability matrix, it projects how much of the landscape will convert from, say, forest to cropland or cropland to urban, assuming the future obeys the same transition tendencies as the recent past. Introduced to landscape ecology by Muller and Middleton in 1994, it is most powerful when coupled with a cellular automaton — the CA-Markov framework — that decides where, not just how much, change occurs.