Sims: An Interactive Tool for Geospatial Matching and Clustering

TL;DR

Sims is a no-code web tool that enables geospatial clustering and similarity search over large regions, facilitating feature exploration for geospatial modeling without requiring extensive computing resources.

Contribution

We introduce Sims, an interactive, open-source tool that simplifies geospatial data exploration and clustering using Google Earth Engine as a backend.

Findings

Effective clustering of yield response zones in Rwanda case study

Sims reduces computational barriers for geospatial feature exploration

Demonstrates utility in analyzing complex geospatial datasets

Abstract

Acquiring, processing, and visualizing geospatial data requires significant computing resources, especially for large spatio-temporal domains. This challenge hinders the rapid discovery of predictive features, which is essential for advancing geospatial modeling. To address this, we developed Similarity Search (Sims), a no-code web tool that allows users to perform clustering and similarity search over defined regions of interest using Google Earth Engine as a backend. Sims is designed to complement existing modeling tools by focusing on feature exploration rather than model creation. We demonstrate the utility of Sims through a case study analyzing simulated maize yield data in Rwanda, where we evaluate how different combinations of soil, weather, and agronomic features affect the clustering of yield response zones. Sims is open source and available at https://github.com/microsoft/Sims