방법 비교

	경관 패턴 지표	CA-Markov 토지 이용 변화 모델	커뮤니티 탐지
분야	공간분석	공간분석	네트워크 분석
계열	Process / pipeline	Process / pipeline	Process / pipeline
기원 연도	1988	1997	2002–2019 (algorithm family)
창시자	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)
유형	Quantitative landscape pattern description	Spatio-temporal land-use change simulation	Graph-partitioning / clustering algorithm family
원전	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.
별칭	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)
관련	3	3	5
요약	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?