Salīdzināt metodes
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| CA-Markov zemes lietojuma pārmaiņu modelis× | Location-Allocation× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1997 | 1963 |
| Autors≠ | Cellular automata (Clarke) + Markov chain (Muller & Middleton) | Leon Cooper; S. L. Hakimi |
| Tips≠ | Spatio-temporal land-use change simulation | Spatial facility-location optimization |
| Pirmavots≠ | Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B, 24(2), 247–261. DOI ↗ | Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343. DOI ↗ |
| Citi nosaukumi | CA-Markov model, cellular automata Markov, land-use change simulation, CA-Markov arazi kullanımı modeli | facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri |
| Saistītās≠ | 3 | 4 |
| Kopsavilkums≠ | CA-Markov is a hybrid spatio-temporal model that projects land-use and land-cover change by combining a Markov chain — which predicts how much of each class will change — with cellular automata, which decide where that change happens. Widely used for urban-growth and land-cover forecasting, it answers both the quantity and the location of change, something neither component does well alone. | 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. |
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