Definition
Space-time geostatistics is concerned with the statistical modeling of environmental variables that vary in space as well as in time. It is an extension of conventional geostatistics, which only considers spatial variation. Common geostatistical concepts, such as the variogram, kriging, stochastic simulation, and sampling design optimization, have a natural extension in the space-time domain, although extra effort is required to model the joint variation in space and time effectively and realistically. The space-time variogram will have spatial and temporal components which may be very different because variation in space is not the same as variation in time. Space-time kriging takes these differences into account and yields optimal predictions at any point in the space-time domain of interest. The interpolation results can be displayed as a series or animations of spatial maps over time or as time series of predictions at as many spatial points as desired.
Space-time...
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References
Bakar KS, Sahu SK (2015) spTimer: spatio-temporal bayesian modelling using R. J Stat Softw 63:1–32
Bardossy A, Pegram GGS (2009) Copula based multisite model for daily precipitation simulation. Hydrol Earth Syst Sci 13:2299–2314
Brus DJ, Heuvelink GBM (2007) Optimization of sample patterns for universal kriging of environmental variables. Geoderma 138:86–95
Cressie N, Wikle CK (2011) Statistics for spatio-temporal data. Wiley, Hoboken
De Cesare L, Myers DE, Posa D (2001) Estimating and modeling space-time correlation structures. Stat Probab Lett 51:9–14
De Cesare L, Myers DE, Posa D (2001) Product-sum covariance for space-time modeling: an environmental application. Environmetrics 12:11–23
Erhardt TM, Czado C, Schepsmeier U (2015) R-vine models for spatial time series with an application to daily mean temperature. Biometrics 71:323–332
Fuentes M, Chen L, Davis JM (2008) A class of nonseparable and nonstationary spatial temporal covariance functions. Environmetrics 19:487–507
Gasch CK, Hengl T, Gräler B, Meyer H, Magney TS, Brown DJ (2015) Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D+T: The Cook Agronomy Farm data set. Spat Stat 14:70–90
Gething PW, Noor AM, Goodman CA, Gikandi PW, Hay SI, Sharif SK, Atkinson PM, Snow RW (2007) Information for decision making from imperfect national data: tracking major changes in health care use in kenya using geostatistics. BMC Med 5:37
Gneiting T (2002) Nonseparable, stationary covariance functions for space-time data. J Am Stat Assoc 97:590–600
Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York
Gräler B (2014) Modelling skewed spatial random fields through the spatial vine copula. Spat Stat 10:87–102
Gräler B, Pebesma E, Heuvelink GBM (2016, in review) Spatio-temporal interpolation using gstat. R Journal
Heuvelink GBM, Griffith DA (2010) Space-time geostatistics for geography: a case study of radiation monitoring across parts of Germany. Geogr Anal 42:161–179
Heuvelink GBM, van Egmond FM (2010) Space-time geostatistics for precision agriculture: a case study of NDVI mappping for a dutch potato field. In: Oliver MA (ed) Geostatistical applications for precision agriculture. Springer, Dordrecht/New York, pp 117–137
Johannesson G, Cressie N, Huang H-C (2007) Dynamic multi-resolution spatial models. Environ Ecol Stat 14:5–25
Jost G, Heuvelink GBM, Papritz A (2005) Analysing the space-time distribution of soil water storage of a forest ecosystem using spatio-temporal kriging. Geoderma 128:258–273
Kilibarda M, Hengl T, Heuvelink GBM, Gräler B, Pebesma E, Perčec Tadić M, Bajat B (2014) Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. J Geophys Res Atmos 119:2294–2313
Kyriakidis PC, Journel AG (1999) Geostatistical space-time models: a review. Math Geol 31:651–684
Lindgren F, Rue H, Lindstrőm J (2011) An explicit link between gaussian random fields and gaussian markov random fields: the stochastic partial differential equation approach. J R Stat Soc B 73:423–498
Mugglin AS, Cressie N, Gemmell I (2002) Hierarchical statistical modelling of influenza epidemic dynamics in space and time. Stat Med 21:2703–2721
Pebesma E (2012) spacetime: spatio-temporal data in R. J Stat Softw 51:1–30
Pebesma EJ (2004) Multivariable geostatistics in S: the gstat package. Comput Geosci 30:683–691
Porcu E, Gregori P, Mateu J (2006) Nonseparable stationary anisotropic space–time covariance functions. Stoch Environ Res Risk Assess 21:113–122
R Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
Schlather M, Malinowski A, Menck PJ, Oesting M, Strokorb K (2015) Analysis, simulation and prediction of multivariate random fields with package randomfields. J Stat Softw 63:1–25
Sigrist F, Künsch HR, Stahel WA (2015) Spate: an R package for spatio-temporal modeling with a stochastic advection-diffusion process. J Stat Softw 63:1–23
Snepvangers JJJC, Heuvelink GBM, Huisman JA (2003) Soil water content interpolation using spatio-temporal kriging with external drift. Geoderma 112:253–271
Stein A, Kocks CG, Zadoks JC, Frinking HD, Ruissen MA, Myers DE (1994) A geostatistical analysis of the spatio-temporal development of downy mildew epidemics in cabbage. Ecol Epidemiol 84:1227–1239
Stein ML (2005) Space-time covariance functions. J Am Stat Assoc 100:310–321
Torabi M Spatiotemporal modeling of odds of disease. Environmetrics 25:341–350 (2014)
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Heuvelink, G.B.M., Pebesma, E., Gräler, B. (2017). Space-Time Geostatistics. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_1647
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