Two models for jointly analysing the spatial variation of incidences of three (or more) diseases, with common and uncommon risk factors, are compared via a simulation experiment. In both models, the linear predictor can be decomposed into shared and disease-specific spatial variability components (named shared clustering and specific clustering respectively). The two models are the shared model...

Spatial clustering has been widely applied in various applications, especially in remote sensing technology. Clustering the geographical nature of the remote sensing imagery is challenging due to its wide and dense spatial distribution. Renowned clustering algorithms such as k-means and other probabilistic clustering algorithms have been reported in the literature. However, they are not robust ...

Spatial clustering has been widely applied in various applications, especially in remote sensing technology. Clustering the geographical nature of the remote sensing imagery is challenging due to its wide and dense spatial distribution. Renowned clustering algorithms such as k-means and other probabilistic clustering algorithms have been reported in the literature. However, they are not robust ...

Spatial clustering has been widely applied in various applications, especially in remote sensing technology. Clustering the geographical nature of the remote sensing imagery is challenging due to its wide and dense spatial distribution. Renowned clustering algorithms such as k-means and other probabilistic clustering algorithms have been reported in the literature. However, they are not robust ...

Spatial clustering has been widely applied in various applications, especially in remote sensing technology. Clustering the geographical nature of the remote sensing imagery is challenging due to its wide and dense spatial distribution. Renowned clustering algorithms such as k-means and other probabilistic clustering algorithms have been reported in the literature. However, they are not robust ...

Spatial clustering has been widely applied in various applications, especially in remote sensing technology. Clustering the geographical nature of the remote sensing imagery is challenging due to its wide and dense spatial distribution. Renowned clustering algorithms such as k-means and other probabilistic clustering algorithms have been reported in the literature. However, they are not robust ...

Spatial clustering has been widely applied in various applications, especially in remote sensing technology. Clustering the geographical nature of the remote sensing imagery is challenging due to its wide and dense spatial distribution. Renowned clustering algorithms such as k-means and other probabilistic clustering algorithms have been reported in the literature. However, they are not robust ...

Spatial clustering has been widely applied in various applications, especially in remote sensing technology. Clustering the geographical nature of the remote sensing imagery is challenging due to its wide and dense spatial distribution. Renowned clustering algorithms such as k-means and other probabilistic clustering algorithms have been reported in the literature. However, they are not robust ...

Spatial clustering has been widely applied in various applications, especially in remote sensing technology. Clustering the geographical nature of the remote sensing imagery is challenging due to its wide and dense spatial distribution. Renowned clustering algorithms such as k-means and other probabilistic clustering algorithms have been reported in the literature. However, they are not robust ...

Spatial clustering has been widely applied in various applications, especially in remote sensing technology. Clustering the geographical nature of the remote sensing imagery is challenging due to its wide and dense spatial distribution. Renowned clustering algorithms such as k-means and other probabilistic clustering algorithms have been reported in the literature. However, they are not robust ...