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Space–Time Geostatistics for Geography: A Case Study of Radiation Monitoring Across Parts of Germany. 地理学的时空地统计学：横跨德国部分区域的辐射监测的案例研究
Author(s) -
Heuvelink Gerard B. M.,
Griffith Daniel A.
Publication year - 2010
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/j.1538-4632.2010.00788.x
Subject(s) - geostatistics , variogram , kriging , space time , interpolation (computer graphics) , spacetime , metric (unit) , multivariate interpolation , spatial variability , space (punctuation) , variable (mathematics) , stochastic modelling , computer science , mathematics , econometrics , statistics , artificial intelligence , mathematical analysis , operations management , economics , bilinear interpolation , operating system , motion (physics) , physics , engineering , quantum mechanics , chemical engineering
Many branches within geography deal with variables that vary not only in space but also in time. Therefore, conventional geostatistics needs to be extended with methods that estimate and quantify spatiotemporal variation and use it in spatiotemporal interpolation and stochastic simulation. This article briefly summarizes the main concepts of space–time geostatistics. Kriging in space and time can be done in much the same way as it is in a purely spatial setting. The main difficulties are in defining a realistic stochastic model that is assumed to have generated data and in characterizing and estimating the space–time correlation of that model. This article uses a model‐based geostatistical approach to characterize space–time variability. The space–time variable of interest is treated as a sum of independent stationary spatial, temporal, and spatiotemporal components, which leads to a sum‐metric space–time variogram model. Methods are illustrated with a case study of space–time interpolation of monthly averages of detected background radiation for a 5‐year period in four German states.