Spherical k-Nearest Neighbors Interpolation

TL;DR

This paper introduces SkNNI, a spherical interpolation algorithm designed for challenging geospatial data, along with NDDNISD, an efficient interpolation function that leverages spatial proximity and distribution.

Contribution

The paper presents SkNNI and NDDNISD, novel methods for geospatial interpolation that handle sparse, heterogeneous, and inconsistent data effectively.

Findings

SkNNI effectively interpolates challenging geospatial data.

NDDNISD provides accurate and efficient interpolation considering spatial proximity.

Open source implementation of SkNNI is available for practical use.

Abstract

Geospatial interpolation is a challenging task due to real world data often being sparse, heterogeneous and inconsistent. For that matter, this work presents SkNNI, a spherical interpolation algorithm capable of working with such challenging geospatial data. This work also presents NDDNISD an accurate and efficient interpolation function for SkNNI which shines due to its spatial awareness in terms of proximity and distribution of observation neighbors. SkNNI's open source implementation is also discussed and illustrated with a simple usage example.