Matrix formulation of co-kriging

Myers, Donald E.

doi:10.1007/bf01032887

Cited by 448 publications

(154 citation statements)

References 1 publication

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“…For example, the nugget value for wind speed is large (≈0.5) compared with the other two which are close to zero. Cokriging involves more complicated algebraic calculations than kriging and the detailed principles are well explained by Myers (1982) and Cressie (1993).…”

Section: Kriging and Cokrigingmentioning

confidence: 99%

A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales

Luo¹,

Taylor²,

Parker³

2007

Intl Journal of Climatology

233

158

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Seven methods of spatial interpolation were compared to determine their suitability for estimating daily mean wind speed surfaces, from data recorded at nearly 190 locations across England and Wales. The eventual purpose of producing such surfaces is to help estimate the daily spread of pathogens causing crop diseases as they move across regions. The interpolation techniques included four deterministic and three geostatistical methods. Quantitative assessment of the continuous surfaces showed that there was a large difference between the accuracy of the seven interpolation methods and that the geostatistical methods were superior to deterministic methods. Further analyses, testing the reliability of the results, showed that measurement accuracy, density, distribution and spatial variability had a substantial influence on the accuracy of the interpolation methods. Independent wind speed data from ten other dates were used to confirm the robustness of the best interpolation methods.  Crown

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Section: Kriging and Cokrigingmentioning

confidence: 99%

A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales

Luo¹,

Taylor²,

Parker³

2007

Intl Journal of Climatology

233

158

View full text Add to dashboard Cite

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“…According to Journel and Huijbregts (1978), the major contribution of cokriging to mining is the possibility of co-estimating poorly sampled variables, but Olea (1991) and Myers (1982) mention that the best advantages are the reduction of error-variance estimation and the opportunity of estimating many attributes in the same domain.…”

Section: Cokrigingmentioning

confidence: 99%

[No Title Available]

Rocha

Yamamoto

Watanabe

et al. 2012

An. Acad. Bras. Ciênc.

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In this paper the influence of a secondary variable as a function of the correlation with the primary variable for collocated cokriging is examined. For this study five exhaustive data sets were generated in computer, from which samples with 60 and 104 data points were drawn using the stratified random sampling method. These exhaustive data sets were generated departing from a pair of primary and secondary variables showing a good correlation. Then successive sets were generated by adding an amount of white noise in such a way that the correlation gets poorer. Using these samples, it was possible to find out how primary and secondary information is used to estimate an unsampled location according to the correlation level.

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“…As in the case of classical multivariate geostatistics, estimates of the alr variables can be made at unsampled locations using the covariance structure defined in Equation [7]. For example, the local neighbourhood simple cokriging estimate ξ * SK (s 0 ) at location s 0 is given by [8] where μ is the mean of the alr-transformed data, W α (s 0 ) is the matrix of weights derived from the simple cokriging system (e.g., Myers, 1982), and n(s 0 ) is the number of data locations forming the local neighbourhood relevant to predicting x(s 0 ). The kriging estimates ξ *(s α ) need to be back-transformed to a composition.…”

Section: Regionalized Compositionsmentioning

confidence: 99%