Geostatistics in Physical Geography. Part I

Theory

Datos Bibliográficos

ID8964213
AutoresMargaret A Oliver (autor de correspondencia), Margaret Oliver, Richard Webster (0000-0002-7682-8993), John Gerrard
Año1989
Volumen14
Número3
Páginas259-259
Fecha de publicación1989-01-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaTransactions of the Institute of British Geographers (JOURNAL)
Identificadores de la revistaISSN: 0020-2754 • E-ISSN: 1475-5661
EditorialWiley (PUBLISHER • GB)
DOI10.2307/622687
OpenAlexW2006792815
IdiomaEN
Citas recibidas6
Referencias citadas3

Geostatistics grew from meteorologists' desire to interpolate weather characteristics from sparse data, and from mining engineers who wanted to estimate quantities of minerals in bodies of rock from drill cores. In both cases the properties of interest behaved as spatially correlated random variables. Practice in mining is now underpinned by sound theory, the theory of regionalized variables. It is widely recognized that the theory is properly applicable in many other branches of earth, atmospheric and marine science. Part I of this paper reviews the more elementary aspects of the theory and its assumptions. It describes the method of local estimation embodied in regionalized variable theory known as kriging. The central tool of geostatistics is the variogram, which expresses quantitatively and succinctly spatially correlated variation, and its estimation is described. The more common forms of variogram are illustrated, and authorized models for them are listed. There are sound procedures for choosing and fitting models to variograms now using well-tried computer programs such as MLP and Genstat. Regionalized variable theory provides geographers with a concise and coherent methodology for describing and analysing spatially distributed data

Economic geography · Geography · Geostatistics · Human geography · Regional science · Sociology · Spatial variability · Statistics · Geochemistry and Geologic Mapping · Mathematics · Soil Geostatistics and Mapping · Spatial and Panel Data Analysis