Spatio-Temporal Data Analysis: an overview

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  • Date: 08 Mar 2013

Statistical Methods are widely used in the analysis of spatial data and spatio-temporal data. These methods have been found to be extremely useful in modelling and understanding the underlying dynamics of many data sets arising from fields, such as climatology, environmental sciences, geophysics etc. One of the important problems often considered is the estimation of an observation at a known location at a future time given the observations in the neighbourhood. The data observed can be continuous (measured on a continuous scale, like temperature) or discrete (such as number of events occuring in a small region). To analyse the data, one needs to make several assumptions, for example, the data is spatially and temporally stationary (or intrinsically stationary), isotropic, the covariances are separable etc. Methods are required to test the validity of these hypotheses and are available in literature in the form of publications. In this book the author devotes considerable time and space collating the methods and discusses these tests and illustrates them with real examples. For practioners, the detailed illustrations must be useful. It should be pointed out that in this book the emphasis is on the description of the methodology (and applications) rather than on actual derivation and rigour in the study of asymptotics. (Excerpt from 'Spatial Statistics and Spatio-Temporal Data', Journal of Time Series Analysis, 34, p280).

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