COMPARISON OF SPATIAL POINT PATTERNS AND PROCESSES CHARACTERIZATION METHODS

The topographical analysis of spatial point patterns is a way to quant itatively characterize the organization of those patterns in either co mputer-based (percolation, cellular automata,...) or experimental (thi n films, alloys, cell biology, astronomy...) models. We have tested th e five most used methods (nearest-neighbour distribution, radial distr ibution, Voronoi paving, quadrat count, minimal spanning tree graph) w hich generate nine parameters on stochastic models (random point proce ss, hard disks model and cluster models) and locally perturbed lattice s models. The methods of topographical analysis were compared in terms of discriminant power, sensitivity to local order perturbations, stab ility of parameters, methodological bias and algorithmic. The method w hich offers the best discrimination power and stability appears to be the minimal spanning tree graph edge length distribution.