Salīdzināt metodes

	Hafa modelis	Location-Allocation	Telpiskās mijiedarbības (gravitācijas) modeļi
Nozare	Telpiskā analīze	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Process / pipeline	Regression model
Izcelsmes gads	1964	1963	1971
Autors	David Huff	Leon Cooper; S. L. Hakimi	Alan Wilson (entropy-maximizing family)
Tips	Probabilistic spatial interaction model	Spatial facility-location optimization	Model of flows between spatial origins and destinations
Pirmavots	Huff, D. L. (1964). Defining and estimating a trading area. Journal of Marketing, 28(3), 34–38.	Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343.	Wilson, A. G. (1971). A family of spatial interaction models, and associated developments. Environment and Planning A, 3(1), 1–32.
Citi nosaukumi	Huff Gravity Model, Probabilistic Retail Gravity Model, Huff Trade Area Model, Huff Çekim Modeli	facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri	gravity model, spatial interaction model, competing destinations model, mekânsal etkileşim modeli
Saistītās	3	4	4
Kopsavilkums	Proposed by David Huff in 1964, the Huff Model is a probabilistic spatial interaction model that estimates the likelihood that consumers located in a given geographic zone will choose to shop at a particular retail outlet. It extends deterministic gravity models by assigning each consumer zone a probability of patronage across all competing stores, weighting store attractiveness (typically measured by floor area) against the friction of travel time or distance. The model is widely used in retail site selection, trade area delineation, and market share forecasting.	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.	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.