hello gregoire Happy new year
For network characterization and optimization, you have at disposal, dealing with the localization of samples, and not with measurements: - voronoi polygons, with statistics of area of polygons, distances between points - delaunay triangulation - Morishita diagram - entropy diagram - fractal dimension of monitoring network - declustering. Tehy are described, with their programs, in Chapter 2 "monitoring networks" of our book, "Analysis and Modelling of sptaial environmental and pollution data" (M. Kanevski, M. Maignan) and the software for it. This contributes to the analysis and optimal locations of measuring locations, wihtout considerations of the variable measured. In case your problem would be a classification problem, for instance the optimal locations of samples for separating two classes, then the SVM approach seems more adequate. Refer to our IAMG 2006 (in Toronto) publication, where the SVM Support Vector Machine shows the area for optimal additional sampling, based on conditional standard deviation of SISIM models (Re Chapter 9 Support Vector Machines for environmental spatial data). The SD at the border between the 2 regions separated by Support vectors is used for identification of the next optimal sampling locations, and this is different from the usual kriging estimation variance. best regards, Michel ******************************************************************************************************** De: "Gregoire Dubois" <[EMAIL PROTECTED]> >>A: <[email protected]> >>Date: Thu, 12 Jan 2006 16:00:33 +0100 >>Sujet: [ai-geostats] Optimization of monitoring networks >> >>Dear list, >> >>I am looking for references (and possibly software) on network >>optimization. The variable monitored has no importance and I am >>looking for references and topological algorithms. A question I have >>is the following: given an area A with a particular shape (e.g. >>defined by country borders) and a number of stations N (e.g. for >>mobile phone emitters), how do I define the optimal locations for >>these stations? >> Michel Maignan Prof. Uni. Lausanne Dir. Gestion des Risques, BC Genève 00 41 79 679 80 13
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