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Stochastic Interpolation

Stochastic Interpolation

Copyright: © 2018 |Pages: 46
ISBN13: 9781522532705|ISBN10: 1522532706|EISBN13: 9781522532712
DOI: 10.4018/978-1-5225-3270-5.ch009
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MLA

João Garrott Marques Negreiros. "Stochastic Interpolation." Spatial Analysis Techniques Using MyGeoffice®, IGI Global, 2018, pp.202-247. https://doi.org/10.4018/978-1-5225-3270-5.ch009

APA

J. Negreiros (2018). Stochastic Interpolation. IGI Global. https://doi.org/10.4018/978-1-5225-3270-5.ch009

Chicago

João Garrott Marques Negreiros. "Stochastic Interpolation." In Spatial Analysis Techniques Using MyGeoffice®. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3270-5.ch009

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Abstract

Spatial analysis includes an expanding array of methods which address different spatial issues, ranging from remote sensing to spatial error uncertainty. Each of these methods focuses on geographically raw data correlated by statistical methods. In general, spatial interpolation and stochastic Kriging, in particular, will be addressed in this chapter. Ordinary Kriging (OK) foundations are presented in the first section which encompasses eight sub-sections (in accordance with the eight myGeoffice© options). Section two introduces Kriging with Trend (KT but sometimes known as Universal Kriging) including five sub-sections: Geocomputation of KT, estimation mapping, the cross-validation procedure, validation using an extra dataset and KT versus OK comparison. Finally, Indicator Kriging (IK) is explored in section three together with nine sub-sections: First and second cutoff definition, first and second probabilistic interpolation maps, construction of the conditional cumulative distribution function, entropy of Shannon, E-type spatial estimation (including misclassification risks and economic classification), morphologic geostatistics and probabilistic interval mapping.

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