Build rigorous, reproducible
geostatistical pipelines in Python.
From variogram fitting to kriging interpolation, spatial regression, and memory-efficient processing — a production playbook for spatial data scientists, environmental analysts, and Python GIS teams.
What this site is for
Spatial data violates the i.i.d. assumption. Observations near each other are correlated, scale changes everything, and naive cross-validation silently inflates accuracy. We document the methods, code, and design patterns that let you ship spatial models that actually generalize.
Three pillars anchor the site: Core Concepts covers the mathematical foundations — spatial dependence, stationarity, variography, point processes. Kriging & Interpolation turns sparse point data into uncertainty-aware surfaces. Python Workflows wires it all together into end-to-end pipelines using GeoPandas, PySAL, PyKrige, scikit-gstat, and Dask.
Every page comes with copy-ready Python, validation diagnostics, and explicit failure modes — the kind of thing that matters in production but rarely makes it into tutorials.
The three pillars
Start with whichever fits your current question. Each pillar 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.
Read pillarKriging, Interpolation & Surface Generation Techniques
From IDW to ordinary, universal, and high-performance kriging. Quantify prediction variance and build production-grade surfaces.
Read pillarPython Workflows for Spatial Modeling & Regression
End-to-end pipelines: GeoPandas prep, spatial weights, regression, cross-validation, and memory-efficient processing at scale.
Read pillarCore 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.
Deep-dive articles
Implementation walkthroughs — copy the code, run it, ship it.