PyInterpolate

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.

Status

Pyinterpolate is still a pre-release version. It is used by the Data Lions company in the project Tick-Borne Disease Detector for the European Space Agency but we do not recommend to use all library functions. The most stable part is Kriging (Ordinary and Simple) and semivariogram calculations.

Bibliography

PyInterpolate was created thanks to many resources and all of them are pointed here:

GIS Algorithms by Ningchuan Xiao: https://uk.sagepub.com/en-gb/eur/gis-algorithms/book241284
Pardo-Iguzquiza E., VARFIT: a fortran-77 program for fitting variogram models by weighted least squares, Computers & Geosciences 25, 251-261, 1999
Goovaerts P., Kriging and Semivariogram Deconvolution in the Presence of Irregular Geographical Units, Mathematical Geology 40(1), 101-128, 2008
Deutsch C.V., Correcting for Negative Weights in Ordinary Kriging, Computers & Geosciences Vol.22, No.7, pp. 765-773, 1996

Requirements

Python 3.7+
Numpy 1.17.3+
Pandas 0.25.2+
GeoPandas 0.6.1+
Matplotlib 3.1.1+

Package structure

- [-] Kriging implementation
       - [X] Distance calculation
       - [X] Empirical semivariogram calculation
       - [X] Theoretical semivariogram modeling
       - [X] Ordinary Kriging
       - [X] Simple Kriging
       - [ ] Regression Kriging
       - [ ] Area-to-Point interpolation
       - [ ] Area-to-Area interpolation
       - [X] Poisson Kriging - centroid based approach

- [X] Data visualization and interpolation
       - [X] Experimental semivariogram
       - [X] Experimental and Theoretical semivariogram
       - [X] 2D point grid
       - [X] 2D raster

- [X] Additional scripts
       - [X] Read and prepare data
       - [X] Interpolation results as a matrix
       - [X] False administrative units development
       - [X] Get areal centroids

- [ ] Tutorials
       - [X] Distance calculation
       - [X] Semivariance Regularization
       - [-] Ordinary Kriging
       - [-] Simple Kriging
       - [ ] Regression Kriging
       - [ ] Poisson Kriging
       - [ ] Why I have obtained negative weights?

Bugs

Issues

[-] Complete documentation and description of "Random geographical units" class

Closed cases

[-] Negative values in estimated error variance in ordinary kriging: DataverseLabs#3

science4fun / pyinterpolate