Spatial Statistics for Data Science
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Spatial Statistics for Data Science
Describes R packages for retrieval, manipulation, and visualization of spatial dataOffers a comprehensive overview of spatial statistical methods including spatial autocorrelation, clustering, spatial interpolation, model-based geostatistics, and spatial point processesProvides detailed explanations on how to fit and interpret Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches
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