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Ruumiinteraktsiooni (gravitatsiooni) mudelid×GIS-põhine mitmekriteeriumiline otsustusanalyys (GIS-MCDA)×Multinomiaalne logistiline regressioon×
ValdkondRuumianalüüsRuumianalüüsÖkonomeetria
PerekondRegression modelProcess / pipelineRegression model
Tekkeaasta197120061974
LoojaAlan Wilson (entropy-maximizing family)Jacek Malczewski (GIS-MCDA synthesis)McFadden
TüüpModel of flows between spatial origins and destinationsSpatial multi-criteria suitability/decision analysisMultinomial logistic regression
AlgallikasWilson, A. G. (1971). A family of spatial interaction models, and associated developments. Environment and Planning A, 3(1), 1–32. DOI ↗Malczewski, J. (2006). GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726. DOI ↗McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503
Rööpnimetusedgravity model, spatial interaction model, competing destinations model, mekânsal etkileşim modeliGIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitabilitymultinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon
Seotud445
KokkuvõteSpatial interaction models predict the volume of flows — migrants, commuters, shoppers, trade, trips — between origins and destinations as a function of the size of each place and the distance or cost separating them. By analogy to Newton's gravity, interaction rises with the 'mass' of origin and destination and falls with separation, and Wilson's 1971 entropy-maximizing family put these models on a rigorous footing for transport, migration, and retail analysis.GIS-MCDA combines the map layers of a geographic information system with multi-criteria decision analysis to produce suitability or priority maps — ranking locations by how well they satisfy several weighted criteria at once. It is the standard framework for spatial decisions such as siting hospitals, solar farms, landfills, or evacuation areas, integrating methods like AHP, TOPSIS, and weighted overlay with spatial data.Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category.
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ScholarGateVõrdle meetodeid: Spatial Interaction Model · GIS-MCDA · Multinomial Logit. Loetud 2026-06-17 aadressilt https://scholargate.app/et/compare