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Spatial scale problems and geostatistical solutions: a review

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Spatial scale problems and geostatistical solutions: a review. / Atkinson, Peter M.; Tate, Nicholas J.
In: Professional Geographer, Vol. 52, No. 4, 2000, p. 607-623.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Atkinson, PM & Tate, NJ 2000, 'Spatial scale problems and geostatistical solutions: a review', Professional Geographer, vol. 52, no. 4, pp. 607-623. https://doi.org/10.1111/0033-0124.00250

APA

Vancouver

Atkinson PM, Tate NJ. Spatial scale problems and geostatistical solutions: a review. Professional Geographer. 2000;52(4):607-623. doi: 10.1111/0033-0124.00250

Author

Atkinson, Peter M. ; Tate, Nicholas J. / Spatial scale problems and geostatistical solutions : a review. In: Professional Geographer. 2000 ; Vol. 52, No. 4. pp. 607-623.

Bibtex

@article{29301c0dae4340b8987ee56adb0f1c18,
title = "Spatial scale problems and geostatistical solutions: a review",
abstract = "The concept of spatial scale is fundamental to geography, as are the problems of integrating data obtained at different scales. The availability of GIS has provided an appropriate environment to re-scale data prior to subsequent integration, but few tools with which to implement the re-scaling. This sparsity of appropriate tools arises primarily because the nature of the spatial variation of interest is often poorly understood and, specifically, the patterns of spatial dependence and error are unknown. Spatial dependence can be represented and modelled using geostatistical approaches providing a basis for the subsequent re-scaling of spatial data (e.g., via spatial interpolation). Geostatistical techniques can also be used to model the effects of re-scaling data through the geostatistical operation of regularization. Regularization provides a means by which to re-scale the statistics and functions that describe the data rather than the data themselves. These topics are reviewed in this paper and the importance of the spatial scale problems that remain is emphasized.",
keywords = "geostatistics, re-scaling, sampling, scale",
author = "Atkinson, {Peter M.} and Tate, {Nicholas J.}",
note = "M1 - 4",
year = "2000",
doi = "10.1111/0033-0124.00250",
language = "English",
volume = "52",
pages = "607--623",
journal = "Professional Geographer",
issn = "1467-9272",
publisher = "Taylor and Francis Ltd.",
number = "4",

}

RIS

TY - JOUR

T1 - Spatial scale problems and geostatistical solutions

T2 - a review

AU - Atkinson, Peter M.

AU - Tate, Nicholas J.

N1 - M1 - 4

PY - 2000

Y1 - 2000

N2 - The concept of spatial scale is fundamental to geography, as are the problems of integrating data obtained at different scales. The availability of GIS has provided an appropriate environment to re-scale data prior to subsequent integration, but few tools with which to implement the re-scaling. This sparsity of appropriate tools arises primarily because the nature of the spatial variation of interest is often poorly understood and, specifically, the patterns of spatial dependence and error are unknown. Spatial dependence can be represented and modelled using geostatistical approaches providing a basis for the subsequent re-scaling of spatial data (e.g., via spatial interpolation). Geostatistical techniques can also be used to model the effects of re-scaling data through the geostatistical operation of regularization. Regularization provides a means by which to re-scale the statistics and functions that describe the data rather than the data themselves. These topics are reviewed in this paper and the importance of the spatial scale problems that remain is emphasized.

AB - The concept of spatial scale is fundamental to geography, as are the problems of integrating data obtained at different scales. The availability of GIS has provided an appropriate environment to re-scale data prior to subsequent integration, but few tools with which to implement the re-scaling. This sparsity of appropriate tools arises primarily because the nature of the spatial variation of interest is often poorly understood and, specifically, the patterns of spatial dependence and error are unknown. Spatial dependence can be represented and modelled using geostatistical approaches providing a basis for the subsequent re-scaling of spatial data (e.g., via spatial interpolation). Geostatistical techniques can also be used to model the effects of re-scaling data through the geostatistical operation of regularization. Regularization provides a means by which to re-scale the statistics and functions that describe the data rather than the data themselves. These topics are reviewed in this paper and the importance of the spatial scale problems that remain is emphasized.

KW - geostatistics

KW - re-scaling

KW - sampling

KW - scale

U2 - 10.1111/0033-0124.00250

DO - 10.1111/0033-0124.00250

M3 - Journal article

VL - 52

SP - 607

EP - 623

JO - Professional Geographer

JF - Professional Geographer

SN - 1467-9272

IS - 4

ER -