Spatial interpolation Python module
Project description
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:
- 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).
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!
Project details
Release history Release notifications | RSS feed
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