Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Location-Allocation× | Multinomiālā loģistiskā regresija× | Puasona un negatīvās binomiālās regresijas× | |
|---|---|---|---|
| Nozare≠ | Telpiskā analīze | Ekonometrija | Ekonometrija |
| Saime≠ | Process / pipeline | Regression model | Regression model |
| Izcelsmes gads≠ | 1963 | 1974 | 1998 |
| Autors≠ | Leon Cooper; S. L. Hakimi | McFadden | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Tips≠ | Spatial facility-location optimization | Multinomial logistic regression | Generalized linear model for count data |
| Pirmavots≠ | Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343. 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 | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| Citi nosaukumi | facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri | multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Saistītās≠ | 4 | 5 | 4 |
| Kopsavilkums≠ | 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. | 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. | Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred. |
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