Build rigorous, reproducible
geostatistical pipelines in Python.
From spatial autocorrelation and variogram fitting to kriging interpolation, spatial regression, and memory-efficient processing at scale — a production playbook for spatial data scientists, environmental analysts, and Python GIS teams.
Why spatial data needs its own methods
Spatial data violates the i.i.d. assumption. Observations near each other are correlated, scale changes everything, and naive cross-validation silently inflates accuracy. This site documents the methods, code, and design patterns that let you ship spatial models that actually generalise.
Four topic areas anchor the site: Core Concepts covers the mathematical foundations — spatial dependence, stationarity, autocorrelation, hot-spot analysis, weight matrices, and the scale effects that decide what your units can even measure. Kriging & Interpolation turns sparse point data into uncertainty-aware surfaces using IDW, ordinary, universal, regression, indicator and cokriging with PyKrige and GSTools. Variogram Modeling characterises spatial continuity with empirical variograms, theoretical model fitting, directional anisotropy, and the diagnostics that catch a bad fit before it corrupts a surface. Python Workflows wires everything together into end-to-end pipelines with GeoPandas, PySAL, scikit-gstat, scikit-learn and Dask — spatial regression, geographically weighted regression, spatial machine learning, cross-validation, and memory-efficient processing.
Every page ships copy-ready Python, explicit validation diagnostics, and documented failure modes — the parts that matter in production but rarely appear in tutorials. Where a method is easy to reach for and easy to misuse, there is a decision guide that says plainly which one to pick and what evidence should decide it.
The four areas
Start with whichever fits your current question. Each area links to sub-topics and deep-dive articles.
Core Concepts of Spatial Statistics & Geostatistics
The mathematical foundations: spatial dependence, stationarity, variography, point processes, and the geostatistical paradigm.
Explore areaKriging, Interpolation & Surface Generation Techniques
From IDW to ordinary, universal, and high-performance kriging. Quantify prediction variance and build production-grade surfaces.
Explore areaPython Workflows for Spatial Modeling & Regression
End-to-end pipelines: GeoPandas prep, spatial weights, regression, cross-validation, and memory-efficient processing at scale.
Explore areaVariogram Modeling & Semivariance Analysis
Characterise spatial continuity: empirical variograms, theoretical model fitting (spherical, exponential, Gaussian), and directional anisotropy.
Explore areaCore Concepts — topics
Sub-topics within Core Concepts of Spatial Statistics & Geostatistics.
Kriging & Interpolation — topics
Sub-topics within Kriging, Interpolation & Surface Generation Techniques.
Python Workflows — topics
Sub-topics within Python Workflows for Spatial Modeling & Regression.
Variogram Modeling — topics
Sub-topics within Variogram Modeling & Semivariance Analysis.
Start here — implementation guides
The most-used walkthroughs. Each one is self-contained: copy the code, run it, ship it.
Decision guides — which method, and why
Most spatial mistakes are choices made by habit. These pages put two methods side by side, run both on the same data, and name the evidence that should decide between them.
All implementation articles
Every hands-on guide on the site, organised by area.