পদ্ধতির তুলনা করুন

	ভূদৃশ্য বিন্যাস পরিমাপক	CA-Markov ভূমি-ব্যবহার পরিবর্তন মডেল	কমিউনিটি ডিটেকশন
ক্ষেত্র	স্থানিক বিশ্লেষণ	স্থানিক বিশ্লেষণ	নেটওয়ার্ক বিশ্লেষণ
পরিবার	Process / pipeline	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1988	1997	2002–2019 (algorithm family)
প্রবর্তক	R. V. O'Neill et al.; McGarigal & Marks (FRAGSTATS)	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)
ধরন	Quantitative landscape pattern description	Spatio-temporal land-use change simulation	Graph-partitioning / clustering algorithm family
মৌলিক উৎস	O'Neill, R. V., et al. (1988). Indices of landscape pattern. Landscape Ecology, 1(3), 153–162.	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.	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.
অপর নাম	landscape pattern indices, FRAGSTATS metrics, fragmentation indices, peyzaj metrikleri	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)
সম্পর্কিত	3	3	5
সারসংক্ষেপ	Landscape metrics are quantitative indices that describe the composition and spatial configuration of a categorical map — typically land cover — at the patch, class, and whole-landscape levels. Developed in landscape ecology (O'Neill and colleagues, 1988) and made widely usable by the FRAGSTATS software, they turn maps into numbers like patch density, edge density, fragmentation, diversity, and connectivity for ecological, planning, and change analysis.	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?