How can I interpolate georeferenced data in python?
python, spatial-interpolation, statistics
Solution
I haven't had a chance to dig into it, but the `hpgl` (High Performance Geostatistics Library) provides a number of kriging (geospatial interpolation) methods:
Algorithms
- Simple Kriging (SK)
- Ordinary Kriging (OK)
- Indicator Kriging (IK)
- Local Varying Mean Kriging (LVM Kriging)
- Simple CoKriging (Markov Models 1 & 2)
- Sequential Indicator Simulation (SIS)
- Corellogram Local Varying Mean SIS (CLVM SIS)
- Local Varying Mean SIS (LVM SIS)
- Sequential Gaussian Simulation (SGS)
Problem
I have many lines of georeferenced hydrological data with weekly resolution: `Station name, Lat, Long, Week 1 average, Week 2 average ... Week 52 average` Unfortunately, I also have some data with only monthly resolution: `Station name, Lat, Long, January average, February average ... December average` Rather than "reinventing the wheel," can anyone recommend a favorite module, package, or technique that would provide a reasonable interpolation of weekly values from monthly values? Linear would be fine, but it would be nice if we could use the coordinates to improve the interpolation based on nearby stations. I've tagged this post with python because it's the language I've been using recently (although not its statistical functions). If the answer is "use a stats program like `r`" so be it, but I'm curious as to what's out there for python. Thanks!