scikit-gstat

What problem does it solve?

Geostatistical analysis and modeling of spatial data often requires estimating variograms, selecting appropriate models, and performing kriging to generate predictions. This Skill provides a Pythonic, sklearn-style API (scikit-gstat) to streamline these tasks end-to-end.

Core Features & Use Cases

• Variogram estimation with multiple models (spherical, exponential, gaussian, matern)
• Spatial interpolation and prediction via OrdinaryKriging and directional variograms
• Anisotropy assessment, cross-validation, and robust estimator options
• Seamless integration into ML pipelines for geostatistical analysis and spatial analytics Use Case: A data scientist wants to quantify spatial correlation in environmental measurements and generate gridded predictions for a study area.

Quick Start

Load your coordinates and values, fit a variogram with a chosen model, and predict on a grid.