Résumé
Visualisation of spatial networks based on pairwise metrics such as (dis)similarity coefficients provides direct information on spatial organization of biological systems. However, for large networks, graphical representations are often unreadable as nodes (samples), and edges (links between samples) strongly overlap. We present a new method, MAPI (Mapping Averaged Pairwise Information), allowing translation from spatial networks to variation surfaces.