DECLUS

DECLUS: a Fortran 77 program for determining optimal spatial declustering weights. Most data collected in the Earth Sciences are clustered preferentially. The clustering may be in high or low “grade” zones or the data may be clustered in areas accessible easily to sampling. Because all statistical and geostatistical analysis requires a distribution that is representative of the entire area of interest, a declustering procedure is necessary. This paper presents a FORTRAN 77 program to compute declustering weights by a modified cell declustering procedure. An example is given and the results are compared to polygonal declustering and global kriging.

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References in zbMATH (referenced in 7 articles )

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  1. Li, Zhanglin; Zhang, Xialin; Zhu, Rui; Zhang, Zhiting; Weng, Zhengping: Integrating data-to-data correlation into inverse distance weighting (2020)
  2. Bourgault, Gilles: Revisiting multi-Gaussian kriging with the Nataf transformation or the Bayes’ rule for the estimation of spatial distributions (2014)
  3. Olea, Ricardo A.; Pawlowsky-Glahn, Vera: Kolmogorov-Smirnov test for spatially correlated data (2009)
  4. Olea, Ricardo A.: Declustering of clustered preferential sampling for histogram and semivariogram inference (2007)
  5. Pardo-Igúzquiza, Eulogio; Dowd, Peter A.: Normality tests for spatially correlated data (2004)
  6. Bourgault, G.: Spatial declustering weights (1997)
  7. Deutsch, Clayton V.: Constrained smoothing of histograms and scatterplots with simulated annealing (1996)