Add to ORKGThe aim of this work is to detect spatial clusters. We link Erdös graph and Poisson point process. We give the probability distribution function (pdf) of the number of connected component for an Erdös graph and obtain the pdf of the number of cluster for a Poisson process. Using this result, we directly obtain a test for complete spatial randomness and also obtain the clusters that violates the CSR hypothesis. Border effects are computed. We illustrate our results on a tropical forest example
Background\ud The task of spatial cluster detection involves finding spatial regions where some prop...
The level of spatial aggregation is a major concern in cluster investigations. Combining regions to ...
The task of spatial cluster detection involves finding spatial regions where some property deviates ...
The aim of this work is to detect spatial clusters. We link Erdös graph and Poisson point process. W...
International audienceClusters in a data point field exhibit spatially specified regions in the obse...
Abstract – In the analysis of spatial point patterns, complete spatial randomness (CSR) hypothesis, ...
A new topic of great relevance and concern has been the design of efficient early warning systems to...
Abstract Background The spatial scan statistic is widely used by public health professionals in the ...
In spatial disease surveillance, geographic areas with large numbers of disease cases are to be iden...
Abstract Background In geographic surveillance of disease, areas with large numbers of disease cases...
Motivated by the analysis of the impact of ecological processes on spatial distribution of tree spec...
This work proposes a cluster detection method that adapts the traditional circular scan method, in t...
This thesis consists of two parts. In Chapter 2, we focus on the spatial scan statistics with overdi...
This work proposes a cluster detection method that adapts the traditional circular scan method, in t...
We propose a novel tool for testing hypotheses concerning the adequacy of environmentally defined f...
Background\ud The task of spatial cluster detection involves finding spatial regions where some prop...
The level of spatial aggregation is a major concern in cluster investigations. Combining regions to ...
The task of spatial cluster detection involves finding spatial regions where some property deviates ...
The aim of this work is to detect spatial clusters. We link Erdös graph and Poisson point process. W...
International audienceClusters in a data point field exhibit spatially specified regions in the obse...
Abstract – In the analysis of spatial point patterns, complete spatial randomness (CSR) hypothesis, ...
A new topic of great relevance and concern has been the design of efficient early warning systems to...
Abstract Background The spatial scan statistic is widely used by public health professionals in the ...
In spatial disease surveillance, geographic areas with large numbers of disease cases are to be iden...
Abstract Background In geographic surveillance of disease, areas with large numbers of disease cases...
Motivated by the analysis of the impact of ecological processes on spatial distribution of tree spec...
This work proposes a cluster detection method that adapts the traditional circular scan method, in t...
This thesis consists of two parts. In Chapter 2, we focus on the spatial scan statistics with overdi...
This work proposes a cluster detection method that adapts the traditional circular scan method, in t...
We propose a novel tool for testing hypotheses concerning the adequacy of environmentally defined f...
Background\ud The task of spatial cluster detection involves finding spatial regions where some prop...
The level of spatial aggregation is a major concern in cluster investigations. Combining regions to ...
The task of spatial cluster detection involves finding spatial regions where some property deviates ...