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Spatial scale problems and geostatistical solutions: a review

Spatial scale problems and geostatistical solutions: a review
Spatial scale problems and geostatistical solutions: a review
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
607-623
Atkinson, P.M.
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Tate, N.J.
387d1d25-ba66-4de5-9add-f92015306ae3
Atkinson, P.M.
aaaa51e4-a713-424f-92b0-0568b198f425
Tate, N.J.
387d1d25-ba66-4de5-9add-f92015306ae3

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

Record type: Article

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

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Published date: 2000

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Local EPrints ID: 17311
URI: https://eprints.soton.ac.uk/id/eprint/17311
PURE UUID: 61d7d75f-54db-42d6-a25a-67409f56ffae

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Date deposited: 24 Aug 2005
Last modified: 17 Jul 2017 16:39

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