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| Mfumo wa Mabadiliko ya Matumizi ya Ardhi CA-Markov× | Ugunduzi wa Jumuiya× | |
|---|---|---|
| Nyanja≠ | Uchanganuzi wa Kimaeneo | Uchanganuzi wa Mitandao |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1997 | 2002–2019 (algorithm family) |
| Mwanzilishi≠ | Cellular automata (Clarke) + Markov chain (Muller & Middleton) | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) |
| Aina≠ | Spatio-temporal land-use change simulation | Graph-partitioning / clustering algorithm family |
| Chanzo asilia≠ | 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 ↗ | Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008. DOI ↗ |
| Majina mbadala≠ | CA-Markov model, cellular automata Markov, land-use change simulation, CA-Markov arazi kullanımı modeli | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) |
| Zinazohusiana≠ | 3 | 5 |
| Muhtasari≠ | 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. | Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network? |
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