Data Lions sponsored development of pyinterpolate package. Pyinterpolate is designed as the Python library for geostatistics. It’s role is to provide access to spatial statistics tools used in a wide range of studies. If you’re:
- GIS expert,
- mining engineer,
- public health specialist,
- data scientist,
Then this package may be useful for you. You could use it for:
- spatial interpolation and spatial prediction,
- alone or with machine learning libraries,
- for point and areal datasets.
Package was tested in commercial and research projects:
- Tick-borne Disease Detector (prediction of areas of infection risk), research project funded by the European Space Agency,
- commercial project related to the prediction of demand for specific flu medications,
- commercial project related to the large-scale infrastructure maintenance.
Pyinterpolate allows you to perform:
- Ordinary Kriging and Simple Kriging (spatial interpolation from points),
- Centroid-based Kriging of Polygons (spatial interpolation from blocks and areas),
- Area-to-area and Area-to-point Poisson Kriging of Polygons (spatial interpolation and data deconvolution from areas to points).
The example how Area-to-point Poisson Kriging works with epidemiological data. Top figure shows disease rates over areas and bottom figure shows exact population at risk.
Before deconvolution – our variable is aggregated over large area, it’s hard to decide how to deal with it:
After deconvolution – our variable is deconvoluted based on the relation to the process at a finer grid:
Package is still actively developed by our team members. You may download it from pip here: https://pypi.org/project/pyinterpolate/