Articles | Volume 19, issue 10
https://doi.org/10.5194/gmd-19-4633-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
01 Jun 2026
Development and technical paper |
| 01 Jun 2026
| 01 Jun 2026
Spatialize v1.0: a Python/C+ + library for ensemble spatial interpolation
Cited articles
Abdulwadood, H. W., Al-Hassany, G. S., and Mustafa, R. I.: IDW Interpolation of Soil Moisture Retention Curve Utilizing GIS, IOP Conference Series: Earth and Environmental Science, 856, 012040, https://doi.org/10.1088/1755-1315/856/1/012040, 2021. a
Abzalov, M.: Applied Mining Geology, vol. 12 of Modern Approaches in Solid Earth Sciences, Springer, https://doi.org/10.1007/978-3-319-39264-6, 2016. a, b, c
Assibey-Bonsu, W.: Professor Danie Krige's First Memorial Lecture: The Basic Tenets of Evaluating the Mineral Resource Assets of Mining Companies, as Observed in Professor Danie Krige's Pioneering Work Over Half a Century, in: Geostatistics Valencia 2016, edited by: Gómez-Hernández, J., Rodrigo-Ilarri, J., Rodrigo-Clavero, M. E., Cassiraga, E., and Vargas-Guzmán, J. A., Quantitative Geology and Geostatistics, Springer, 3–25, https://doi.org/10.1007/978-3-319-46819-8_1, 2017. a
Boroh, A. W., Kouayep Lawou, S., Mfenjou, M. L., and Ngounouno, I.: Comparison of geostatistical and machine learning models for predicting geochemical concentration of iron: case of the Nkout iron deposit (south Cameroon), J. Afr. Earth Sci., 195, 104662, https://doi.org/10.1016/j.jafrearsci.2022.104662, 2022. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
