Spatialize is an open source library that implements ensemble spatial interpolation, a novel method that combines the simplicity of basic interpolation methods with the power of classical geoestatistical tools, like Kriging.
This library aims to bridge the gap between expert and non-expert users of geostatistics by providing automated tools that rival traditional geostatistical methods.
Main features of the library include:
- Stochastic modelling and ensemble learning, making it robust, scalable and suitable for large datasets.
- Provides a powerful framework for uncertainty quantification, offering both point estimates and empirical posterior distributions.
- It is implemented in Python 3.x, with a C++ core for improved performance.
- It is designed to be easy to use, requiring minimal user intervention.
The source code is currently hosted on GitHub at: https://github.com/alges/spatialize
Direct installers for the latest released version are available at the Python Package Index (PyPI).
pip install spatialize- NumPy: Powerful n-dimensional arrays and numerical computing tools
- pandas: Fast, powerful, flexible and easy to use open source data analysis and manipulation tool
- Matplotlib: Visualization with Python
- scikit-learn: Machine Learning in Python
- SciPy: Fundamental algorithms for scientific computing in Python
Please cite the following paper when publishing work relating to this library:
@article{spatialize2025,
title = {Spatialize: A Python/C++ Library for Ensemble Spatial Interpolation},
author = {Ega{\~n}a, {\'A}lvaro F. and Ehrenfeld, Alejandro and Navarro, Felipe and Garrido, Felipe and Valenzuela, Mar{\'i}a Jes{\'u}s and S{\'a}nchez-P{\'e}rez, Juan F. },
date = {},
doi = {},
isbn = {},
journal = {},
number = {},
pages = {},
url = {},
volume = {},
year = {2025},
}