Hierarchical dynamic spatiotemporal models; Geostatistical models
Definition
A hierarchical spatial model is the product of conditional distributions for data conditioned on a spatial process and parameters, the spatial process conditioned on the parameters defining the spatial dependencies between process locations and the parameters themselves.
Historical Background
Scientists across a wide range of disciplines have long recognized the importance of spatial dependencies in their data and the underlying process of interest. Initially due to computational limitations, they dealt with such dependencies by randomization and blocking rather than the explicit characterization of the dependencies in their models. Early developments in spatial modeling started in the 1950s and 1960s motivated by problems in mining engineering and meteorology (Cressie 1993), followed by the introduction of Markov random fields (Besag 1974). The application of hierarchical spatial and...
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Recommended Reading
Wikle CK (2015) Modern perspectives on statistics for spatio-temporal data. Wiley Interdiscip Rev Comput Stat 7(1):86–98
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Arab, A., Hooten, M.B., Wikle, C.K. (2017). Hierarchical Spatial Models. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_564
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