Abstract
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.
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Acknowledgments
The authors thank the anonymous referees for their comments and thank the editors for their patience while this chapter was finalized. The Northern Ireland Statistics and Research Agency are thanked for access to data. Census output is Crown copyright and is reproduced with the permission of the Controller of HMSO and the Queen’s Printer for Scotland. Northern Ireland Statistics and Research Agency, 2001 Census: Standard Area Statistics (Northern Ireland) [computer file]. ESRC/JISC Census Programme, Census Dissemination Unit, Mimas (University of Manchester).
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Atkinson, P.M., Lloyd, C.D. (2019). Geostatistical Models and Spatial Interpolation. In: Fischer, M., Nijkamp, P. (eds) Handbook of Regional Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36203-3_75-1
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DOI: https://doi.org/10.1007/978-3-642-36203-3_75-1
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