Comparison of spatial point patterns and processes characterization methods

The topographical analysis of spatial point patterns is a way to quantitatively characterize the organization of those patterns in either computer-based (percolation, cellular automata, $\dots$) or experimental (thin films, alloys, cell biology, astronomy$\dots$) models. We have tested the five most...

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Bibliographic Details
Published in:Europhysics letters 1998-06, Vol.42 (5), p.493-498
Main Authors: Wallet, F, Dussert, C
Format: Article
Language:English
Subjects:
02.50.Ey
06.30.-k
42.30.Sy
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Summary:The topographical analysis of spatial point patterns is a way to quantitatively characterize the organization of those patterns in either computer-based (percolation, cellular automata, $\dots$) or experimental (thin films, alloys, cell biology, astronomy$\dots$) models. We have tested the five most used methods (nearest-neighbour distribution, radial distribution, Voronoï  paving, quadrat count, minimal spanning tree graph) which generate nine parameters on stochastic models (random point process, hard disks model and cluster models) and locally perturbed lattices models. The methods of topographical analysis were compared in terms of discriminant power, sensitivity to local order perturbations, stability of parameters, methodological bias and algorithmic. The method which offers the best discrimination power and stability appears to be the minimal spanning tree graph edge length distribution.
ISSN:0295-5075
1286-4854
DOI:10.1209/epl/i1998-00279-7