Spatialize v1.0: a Python/C+ + library for ensemble spatial interpolation

Read full text
Authors: Felipe Navarro, Álvaro F. Egaña, Alejandro Ehrenfeld, Felipe Garrido, María Jesús Valenzuela, Juan Francisco Sánchez Pérez
Cited by: 0
References: 46

Abstract

Abstract. In this paper, we present Spatialize, 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 geostatistical tools, like Kriging. It leverages the richness of stochastic modelling and ensemble learning, making it robust, scalable and suitable for large datasets. In addition, Spatialize 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, and is designed to be easy to use, requiring minimal user intervention. This library aims to bridge the gap between expert and non-expert users of geostatistics by providing automated tools that rival traditional geostatistical methods. Here, we present a detailed description of Spatialize along with a wealth of examples of its use.

Fields of Study & Subjects

Soil Geostatistics and Mapping Soil Moisture and Remote Sensing Advanced Multi-Objective Optimization Algorithms

Publication details

Journal / source
Geoscientific model development
Publisher
Copernicus GmbH
Volume
19
Issue
10
Pages
4633–4660
Publication date
1 June 2026
Language
EN