Geostatistical models and spatial interpolation

Peter M. Atkinson*, Christopher D. Lloyd

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)


Characterizing the spatial structure of variables in the regional sciences is important for several reasons. Firstly, the spatial structure may itself be of interest. The structure of a population variable tells us something about how the population is configured spatially. For example, is the population clustered by some properties, but not others? Secondly, mapping variables from sparse sample observations or transferring values between areal units requires knowledge of how the property of interest varies spatially. Thirdly, we require knowledge of spatial variation in order to design sampling strategies which make the most of the effort, time, and money expended in sampling. Geostatistics comprises a set of principles and tools which can be applied to characterize or model spatial variation and use that model to optimize the mapping, simulation, and sampling of spatial properties. This chapter provides an introduction to some key ideas in geostatistics, with a particular focus on the kinds of applications which may be of interest for regional scientists.

Original languageEnglish
Title of host publicationHandbook of Regional Science
PublisherSpringer Berlin Heidelberg
Number of pages16
ISBN (Electronic)9783642234309
ISBN (Print)9783642234293
Publication statusPublished - 01 Jan 2014
Externally publishedYes

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)
  • Business, Management and Accounting(all)


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