🤖 AI Summary
This study addresses the technical gap between domain experts and non-experts in geostatistics by proposing and implementing the first open-source, integrated spatial interpolation library. To bridge this gap, we develop a unified framework that synergistically combines classical geostatistical methods (e.g., ordinary and universal kriging) with modern machine learning techniques. Our approach employs ensemble learning to automatically select and optimize interpolation strategies, and integrates stochastic simulation for posterior distribution inference—enabling both point estimation and rigorous uncertainty quantification. Built on a hybrid Python/C++ architecture, the library balances usability, computational efficiency, and extensibility. Empirical evaluation across diverse real-world spatial datasets demonstrates accuracy competitive with or superior to conventional methods. Moreover, the library provides standardized APIs, comprehensive uncertainty assessment, and built-in visualization tools—collectively lowering the barrier to entry for spatial modeling.
📝 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.