পদ্ধতির তুলনা করুন

	Urban Simulation Model	Land-Use Change Modeling
ক্ষেত্র	Urban Studies	Human Geography
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2002	2002
প্রবর্তক	Paul Waddell (UrbanSim); related lineage: cellular automata and agent-based modelling	Peter H. Verburg and colleagues (CLUE-S); broader land-change-science community
ধরন	Dynamic computational model of urban development and land use	Family of spatially explicit models simulating land-use and land-cover change
মৌলিক উৎস	Waddell, P. (2002). UrbanSim: Modeling urban development for land use, transportation, and environmental planning. Journal of the American Planning Association, 68(3), 297–314.	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.
অপর নাম	Land-Use Microsimulation, Urban Growth Simulation, Agent-Based Urban Model, Integrated Land-Use Transport Simulation	Land Change Modeling, LUCC Simulation, Spatial Land-Use Allocation Modeling, Land-Use Scenario Modeling
সম্পর্কিত	4	4
সারসংক্ষেপ	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.	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.