Methoden vergleichen

	Spatial Microsimulation	Land-Use Change Modeling
Fachgebiet	Human Geography	Human Geography
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2016	2002
Urheber	Developed in the IPF/microsimulation tradition; synthesized for geography by Lovelace & Dumont	Peter H. Verburg and colleagues (CLUE-S); broader land-change-science community
Typ	Method for generating and analysing synthetic individual-level populations within small areas	Family of spatially explicit models simulating land-use and land-cover change
Wegweisende Quelle	Lovelace, R., & Dumont, M. (2016). Spatial Microsimulation with R. Chapman and Hall/CRC, Boca Raton. ISBN: 9781498711548	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.
Aliasnamen	Small-Area Population Synthesis, Synthetic Population Generation, Geographical Microsimulation, Spatial Microdata Estimation	Land Change Modeling, LUCC Simulation, Spatial Land-Use Allocation Modeling, Land-Use Scenario Modeling
Verwandt	4	4
Zusammenfassung	Spatial microsimulation is a family of techniques for generating realistic synthetic populations of individuals within small geographic areas, by combining detailed but geographically coarse survey microdata with geographically fine but aggregate census tables. It estimates, for every neighbourhood, a population of individuals whose collective characteristics match the published margins — the right number of each age, sex, income, and tenure group — even though no survey directly samples individuals at that fine scale. Synthesized for the geographic community in Robin Lovelace and Morgane Dumont's 2016 book, it bridges the gap between rich individual data and small-area aggregates so that policy and behaviour can be modelled where people actually live.	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.