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Spatial interpolation Python module

Project description

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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.


Beta version: package is tested and the main structure is preserved but future changes are very likely to occur.


Setup is described in the file

Commercial and scientific projects where library has been used

  • Tick-Borne Disease Detector (Data Lions company) for the European Space Agency (2019-2020).


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

  • Armstrong M., Basic Linear Geostatistics, Springer 1998,
  • GIS Algorithms by Ningchuan Xiao:
  • 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 and dependencies

  • Python 3.7.6

  • Numpy 1.18.3

  • Scipy 1.4.1

  • GeoPandas 0.7.0

  • Fiona 1.18.13.post1 (Mac OS) / Fiona 1.8 (Linux)

  • Rtree 0.9.4 (Mac OS), Rtree >= 0.8 & < 0.9 (Linux)

  • Descartes 1.1.0

  • Pyproj 2.6.0

  • Shapely 1.7.0

  • Matplotlib 3.2.1

Package structure

High level overview:


  • pyinterpolate
    • calculations - distance calculation
    • data_processing - preparation of spatial data and data processing tasks,
    • data visualization - interpolation of smooth surfaces as rasters,
    • kriging - Ordinary Kriging, Simple Kriging, Poisson Kriging: centroid based, area-to-area, area-to-point,
    • misc - compare different kriging techniques,
    • semivariance - calculate semivariance, fit semivariograms and regularize semivariogram,
    • tutorials - tutorials (Basic, Intermediate and Advanced)

Functions documentation



  • inverse distance weighting,
  • point cloud variograms,
  • semivariogram params management,
  • semivariogram regularization with epidemiological data tutorial

Known Bugs

  • (still) not detected!

Project details

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