Interpolation & surfaces
IDW, kriging, TIN and variograms — turning scattered samples into a surface, with its support attached.
Everything on Spatial Workflow about interpolation & surfaces, in the order that usually works: understand the idea, then run the task, then fix it when it goes wrong. 18 guides in total.
4 learning paths run through this topic — follow one if you would rather work through a route than pick a single guide.
🧭 Start with the concepts
The ideas the how-to guides below assume you already have.
- Spatial Interpolation Explained: Guessing Between the Samples spatial interpolation explained
- Sample Design Explained: Where to Measure sample design explained
- The Variogram Explained: How Far Does a Measurement Reach variogram explained
- IDW, Kriging, Splines or TIN? Choosing an Interpolator choosing an interpolator explained
- Regridding Explained: Bilinear, Conservative and Nearest for Gridded Data regridding explained conservative bilinear
⚙️ 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 idw interpolation python
- How to Fit a Variogram and Krige a Surface in Python kriging python
- How to Build a TIN and Interpolate Elevations tin interpolation python
- How to Cross-Validate an Interpolated Surface cross validate interpolation python
- How to Choose a Cell Size for an Interpolated Surface choose cell size interpolation
- How to Interpolate to Polygons Instead of a Grid areal interpolation python
- How to Compare Census Years Across Changed Boundaries census crosswalk python
- How to Redistribute Population with Dasymetric Mapping dasymetric mapping python
- How to lay out a soil sampling grid in Python soil sampling grid python
🔧 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.
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Interpolation & Surfaces
A continuous surface from scattered samples, with its support attached.
Open path → -
Gridded & Climate Data
A raster with a time axis, a calendar and forty years in one file.
Open path → -
Census & Demographics
The most-used open data there is, and the easiest to map wrongly.
Open path → -
Agriculture & Crops
One field, one season, and a dozen ways to measure it wrong.
Open path →
Related topics
Subjects that keep coming up alongside this one, most overlap first.