השוואת שיטות

	מודלים של אינטראקציה מרחבית (כבידה)	ניתוח החלטות רב-קריטריוני מבוסס מערכות מידע גאוגרפי (GIS-MCDA)	מודלים של הקצאת מיקום
תחום	ניתוח מרחבי	ניתוח מרחבי	ניתוח מרחבי
משפחה	Regression model	Process / pipeline	Process / pipeline
שנת המקור	1971	2006	1963
הוגה השיטה	Alan Wilson (entropy-maximizing family)	Jacek Malczewski (GIS-MCDA synthesis)	Leon Cooper; S. L. Hakimi
סוג	Model of flows between spatial origins and destinations	Spatial multi-criteria suitability/decision analysis	Spatial facility-location optimization
מקור מכונן	Wilson, A. G. (1971). A family of spatial interaction models, and associated developments. Environment and Planning A, 3(1), 1–32.	Malczewski, J. (2006). GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726.	Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343.
כינויים	gravity model, spatial interaction model, competing destinations model, mekânsal etkileşim modeli	GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability	facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri
קשורות	4	4	4
תקציר	Spatial 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.	Location-allocation models decide where to place a set of facilities and simultaneously assign demand points to them so as to optimize an objective such as total travel cost, worst-case distance, or population covered. Rooted in the operations-research work of Cooper (1963) and Hakimi (1964) and central to network GIS, they answer questions like where to site warehouses, hospitals, fire stations, or schools to best serve a spatially distributed population.