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 pro-cess. Using this result, we directly obtain a test for complete spatial randomness and also obtain the clusters that violates the CSR hy-pothesis. Border effects are computed. We illustrate our results on a tropical forest example
When two spatial point processes are overlaid, the one with the higher rate is shown as clustered po...
The scan statistic is widely used in spatial cluster detection applications of inhomogeneous Poisso...
Background: There is considerable uncertainty in the disease rate estimation for aggregated area map...
The aim of this work is to detect spatial clusters. We link Erdös graph and Poisson point process. W...
Abstract – In the analysis of spatial point patterns, complete spatial randomness (CSR) hypothesis, ...
International audienceClusters in a data point field exhibit spatially specified regions in the obse...
Abstract Background The spatial scan statistic is widely used by public health professionals in the ...
This thesis consists of two parts. In Chapter 2, we focus on the spatial scan statistics with overdi...
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...
International audienceAn original approach to cluster multi-component data sets is proposed that inc...
Background\ud The task of spatial cluster detection involves finding spatial regions where some prop...
Motivated by the analysis of the impact of ecological processes on spatial distribution of tree spec...
The geographic delineation of irregularly shaped spatial clusters is an ill defined problem. Whenev...
The space-time scan statistic is a widely-used method for cluster detection in which both the geogra...
When two spatial point processes are overlaid, the one with the higher rate is shown as clustered po...
The scan statistic is widely used in spatial cluster detection applications of inhomogeneous Poisso...
Background: There is considerable uncertainty in the disease rate estimation for aggregated area map...
The aim of this work is to detect spatial clusters. We link Erdös graph and Poisson point process. W...
Abstract – In the analysis of spatial point patterns, complete spatial randomness (CSR) hypothesis, ...
International audienceClusters in a data point field exhibit spatially specified regions in the obse...
Abstract Background The spatial scan statistic is widely used by public health professionals in the ...
This thesis consists of two parts. In Chapter 2, we focus on the spatial scan statistics with overdi...
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...
International audienceAn original approach to cluster multi-component data sets is proposed that inc...
Background\ud The task of spatial cluster detection involves finding spatial regions where some prop...
Motivated by the analysis of the impact of ecological processes on spatial distribution of tree spec...
The geographic delineation of irregularly shaped spatial clusters is an ill defined problem. Whenev...
The space-time scan statistic is a widely-used method for cluster detection in which both the geogra...
When two spatial point processes are overlaid, the one with the higher rate is shown as clustered po...
The scan statistic is widely used in spatial cluster detection applications of inhomogeneous Poisso...
Background: There is considerable uncertainty in the disease rate estimation for aggregated area map...