Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Landvormpatroonmetrieken× | Community Detection× | |
|---|---|---|
| Vakgebied≠ | Ruimtelijke analyse | Netwerkanalyse |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 1988 | 2002–2019 (algorithm family) |
| Grondlegger≠ | R. V. O'Neill et al.; McGarigal & Marks (FRAGSTATS) | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) |
| Type≠ | Quantitative landscape pattern description | Graph-partitioning / clustering algorithm family |
| Oorspronkelijke bron≠ | O'Neill, R. V., et al. (1988). Indices of landscape pattern. Landscape Ecology, 1(3), 153–162. 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 ↗ |
| Aliassen≠ | landscape pattern indices, FRAGSTATS metrics, fragmentation indices, peyzaj metrikleri | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) |
| Verwant≠ | 3 | 5 |
| Samenvatting≠ | 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. | 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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