Skip to content

Simple Functions for spatial interpolation using machine learning

Notifications You must be signed in to change notification settings

Trailmarker/skspatial

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

57 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Alt text

skspatial

Simple functions for geospatial interpolation using sklearn's KNN machine learning algorithm, simple scipy interpolation routines ie. "linear", or "cubic" and now pykrige for kriging functions.

Simply load a projected point shapefile with geopandas as a GeoDataFrame, and use skspatial to create interpolated rasters and countor shapefiles that you can bring into your favorite mapping application such as QGIS.

Currently in development by:

Alt text

Written by Ross Kushnereit, Intera Geoscience & Engineering Solutions: Austin, TX https://www.intera.com/

Installation

skspatial supports Python 3.6

    $ git clone https://github.com/rosskush/skspatial.git
    $ cd skspatial
    $ python setup.py install

Reqiuerments

skspatial in its current state reqiures at a minimum

geopandas

rasterio

and sklearn

Refrences

http://chris35wills.github.io/gridding_data/

https://timogrossenbacher.ch/2018/03/categorical-spatial-interpolation-with-r/

http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html#sklearn.neighbors.KNeighborsRegressor

About

Simple Functions for spatial interpolation using machine learning

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%