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. This package helps you interpolate spatial data with Kriging technique. In the close future you'll use more spatial interpolation tools.

If you’re:

• GIS expert,
• geologist,
• mining engineer,
• ecologist,
• 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.

Pyinterpolate allows you to perform:

1. Ordinary Kriging and Simple Kriging (spatial interpolation from points),
2. Centroid-based Kriging of Polygons (spatial interpolation from blocks and areas),
3. Area-to-area and Area-to-point Poisson Kriging of Polygons (spatial interpolation and data deconvolution from areas to points).

Status

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

Setup

Setup by pip: pip install pyinterpolate / Python 3.7 is required!

Manual setup is described in the file SETUP.md: https://github.com/szymon-datalions/pyinterpolate/blob/master/SETUP.md We pointed there most common problems related to third-party packages.

Commercial and scientific projects where library has been used

• Tick-Borne Disease Detector (Data Lions company) for the European Space Agency (2019-2020).
• B2C project related to the prediction of demand for specific flu medications,
• B2G project related to the large-scale infrastructure maintenance.

Community

Join our community in Discord: https://discord.gg/3EMuRkj

Bibliography

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: 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 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
  • distance - distance calculation
  • io_ops - reads and prepares input spatial datasets,
  • transform - transforms spatial datasets,
  • viz - interpolation of smooth surfaces from points into 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

Pyinterpolate https://pyinterpolate.readthedocs.io/en/latest/

Development

• inverse distance weighting,
• semivariogram analysis and visualization methods,
• see Projects page of this repository!

Known Bugs

• (still) not detected!