Interpolation & surfaces
IDW, kriging, TIN and variograms — turning scattered samples into a surface, with its support attached.
Interpolation turns scattered measurements into a continuous surface. Every method honours the samples exactly; they differ everywhere else, which is most of the map.
Kriging is the one worth understanding properly, because it comes with a variance — and that variance is a statement about the geometry of the sampling, not about accuracy. It is low where samples are dense and high where they are sparse, regardless of whether the values there are predictable. Kriging weights can also be negative, which is what lets a surface run below its lowest sample and produce a physically impossible number if the variable has a floor.
Below: the concepts cover IDW, kriging, splines, variograms and what a support is; the how-to guides fit and predict; and the fixes are the surfaces that come out flat, spiky or out of range.
🧭 Start with the concepts
The ideas the how-to guides below assume you already have.
⚙️ Then do the task
Step-by-step walkthroughs with code you can copy and adapt.
- How to Interpolate Points to a Grid with IDW in Python
- How to Fit a Variogram and Krige a Surface in Python
- How to Build a TIN and Interpolate Elevations
- How to Cross-Validate an Interpolated Surface
- How to Choose a Cell Size for an Interpolated Surface
- How to Interpolate to Polygons Instead of a Grid
Show every how-to guide in this topic
🔧 When it breaks
The errors this topic produces, with the cause explained rather than just the command.
Learning paths through this topic
A curated route rather than a list — the guides in the order that builds on itself.
Related topics
Subjects that keep coming up alongside this one, most overlap first.