![]() Hierarchical Modeling and Analysis for Spatial Data. Longman, Harlow.īanerjee, S., Carlin, B. P., and Gelfand, A. E. This process is experimental and the keywords may be updated as the learning algorithm improves.īailey, T. C. ![]() ![]() These keywords were added by machine and not by the authors. Statistical inference for such spatial processes is often challenging, but is necessary when we try to draw conclusions about questions that interest us. These questions refer to hypothetical processes that generate the observed data. Beyond creating and viewing maps, spatial data analysis is concerned with questions not directly answered by looking at the data themselves. Making a map that is suited to its purpose and does not distort the underlying data unnecessarily is however not easy. Besides those we collect ourselves (‘is it raining?’), they confront us on television, in newspapers, on route planners, on computer screens, on mobile devices, and on plain paper maps. Spatial and spatio-temporal data are everywhere.
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