The Importance of Geostatistics in the Era of Data Science

Peter Atkinson

The Importance of Geostatistics in the Era of Data Science

Peter Atkinson

Research output: Contribution to journal › Editorial › peer-review

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Abstract

Since the development of geostatistics in the 1960s, geostatistical techniques have been applied widely, from early advances in estimating mineral and oil/gas reserves (David 1977; Journel and Huijbregts 1978) to a range of environmental problems including in soil science, hydrology, ecology, climatology and oceanography, more recently (Cressie 1993; Goovaerts 1997). This expansion in the use of geostatistics stems, crucially, from the spatially (and temporally) explicit nature of the methods which geostatistics provides for the appropriate analysis of Earth science properties, which vary in space and time.

Original language	English
Pages (from-to)	1-2
Journal	Mathematical Geosciences
Publication status	Accepted - 16 Mar 2020
Externally published	Yes
Event	The 12th Conference on Geostatistics for Environmental Applications: geoENV2018 - Queen's University Belfast, Belfast, United Kingdom Duration: 04 Jul 2018 → 05 Jul 2018 https://2018.geoenvia.org/

Keywords

geostatistics
big data
compositional data analysis
Bayesian approach
Machine Learning
spatial analysis

ASJC Scopus subject areas

Environmental Science(all)
Applied Mathematics

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The Importance of Geostatistics in the Era of Data Science
Copyright 2020 Springer. This work is made available online in accordance with the publisher’s policies. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 155 KBLicence: Unspecified

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title = "The Importance of Geostatistics in the Era of Data Science",

abstract = "Since the development of geostatistics in the 1960s, geostatistical techniques have been applied widely, from early advances in estimating mineral and oil/gas reserves (David 1977; Journel and Huijbregts 1978) to a range of environmental problems including in soil science, hydrology, ecology, climatology and oceanography, more recently (Cressie 1993; Goovaerts 1997). This expansion in the use of geostatistics stems, crucially, from the spatially (and temporally) explicit nature of the methods which geostatistics provides for the appropriate analysis of Earth science properties, which vary in space and time.",

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author = "Peter Atkinson",

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journal = "Mathematical Geosciences",

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AU - Atkinson, Peter

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N2 - Since the development of geostatistics in the 1960s, geostatistical techniques have been applied widely, from early advances in estimating mineral and oil/gas reserves (David 1977; Journel and Huijbregts 1978) to a range of environmental problems including in soil science, hydrology, ecology, climatology and oceanography, more recently (Cressie 1993; Goovaerts 1997). This expansion in the use of geostatistics stems, crucially, from the spatially (and temporally) explicit nature of the methods which geostatistics provides for the appropriate analysis of Earth science properties, which vary in space and time.

AB - Since the development of geostatistics in the 1960s, geostatistical techniques have been applied widely, from early advances in estimating mineral and oil/gas reserves (David 1977; Journel and Huijbregts 1978) to a range of environmental problems including in soil science, hydrology, ecology, climatology and oceanography, more recently (Cressie 1993; Goovaerts 1997). This expansion in the use of geostatistics stems, crucially, from the spatially (and temporally) explicit nature of the methods which geostatistics provides for the appropriate analysis of Earth science properties, which vary in space and time.

KW - geostatistics

KW - big data

KW - compositional data analysis

KW - Bayesian approach

KW - Machine Learning

KW - spatial analysis

M3 - Editorial

SP - 1

EP - 2

JO - Mathematical Geosciences

JF - Mathematical Geosciences

SN - 1874-8961

T2 - The 12th Conference on Geostatistics for Environmental Applications

Y2 - 4 July 2018 through 5 July 2018

ER -

The Importance of Geostatistics in the Era of Data Science

Abstract

Keywords

ASJC Scopus subject areas

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