השוואת שיטות

	מודל שינוי שימושי קרקע CA-Markov	מודלים של הקצאת מיקום
תחום	ניתוח מרחבי	ניתוח מרחבי
משפחה	Process / pipeline	Process / pipeline
שנת המקור	1997	1963
הוגה השיטה	Cellular automata (Clarke) + Markov chain (Muller & Middleton)	Leon Cooper; S. L. Hakimi
סוג	Spatio-temporal land-use change simulation	Spatial facility-location optimization
מקור מכונן	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.	Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343.
כינויים	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
קשורות	3	4
תקציר	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.