pyinterpolate 0.2.0

Spatial interpolation Python module

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

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

Setup

Setup is described in the file SETUP.md: https://github.com/szymon-datalions/pyinterpolate/blob/master/SETUP.md

Commercial and scientific projects where library has been used

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

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
  • 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

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

Development

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

Known Bugs

• (still) not detected!