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| Модели за локация-разпределение× | Регресия на Поасон и отрицателна биномна регресия× | |
|---|---|---|
| Област≠ | Пространствен анализ | Иконометрия |
| Семейство≠ | Process / pipeline | Regression model |
| Година на възникване≠ | 1963 | 1998 |
| Създател≠ | Leon Cooper; S. L. Hakimi | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Тип≠ | Spatial facility-location optimization | Generalized linear model for count data |
| Основополагащ източник≠ | Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343. DOI ↗ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| Други названия | facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Свързани | 4 | 4 |
| Резюме≠ | 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. | 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. |
| ScholarGateНабор от данни ↗ |
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