Articles | Volume 15, issue 6
Model description paper
25 Mar 2022
Model description paper |  | 25 Mar 2022

SciKit-GStat 1.0: a SciPy-flavored geostatistical variogram estimation toolbox written in Python

Mirko Mälicke

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Cited articles

Atkinson, P. M. and Tate, N. J.: Spatial Scale Problems and Geostatistical Solutions: A Review, Prof. Geogr., 52, 607–623,, 2000. a
Bárdossy, A.: Copula-based geostatistical models for groundwater quality parameters, Water Resour. Res., 42, W11416,, 2006. a
Bárdossy, A. and Lehmann, W.: Spatial distribution of soil moisture in a small catchment. Part 1: Geostatistical analysis, J. Hydrol., 206, 1–15,, 1998. a, b
Bárdossy, A. and Li, J.: Geostatistical interpolation using copulas, Water Resour. Res., 44, W07412,, 2008. a
Bivand, R. S., Pebesma, E. J., Gómez-Rubio, V., and Pebesma, E. J.: Applied spatial data analysis with R, vol. 747248717, Springer,, ISBN 978-1-4614-7617-7, 2008. a, b, c, d
Short summary
I preset SciKit-GStat, a well-documented and tested Python package for variogram estimation. The variogram is the core means of geostatistics, which almost all other methods rely on. Geostatistical interpolation and field generation are widely spread in geoscience, i.e., for data assimilation or modeling. While SciKit-GStat focuses on effective and intuitive variogram estimation, it can interface with other prominent packages and make its variograms available for a multitude of methods.