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Sims: An interactive tool for geospatial matching and clustering.

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This study introduces Similarity Search (Sims), a no-code tool for exploring geospatial data features. Sims aids in discovering predictive patterns for advanced geospatial modeling without requiring coding expertise.

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Area of Science:

  • Geospatial Science
  • Data Science
  • Agricultural Science

Background:

  • Geospatial data analysis demands substantial computational power, particularly for large spatio-temporal datasets.
  • This computational burden impedes the identification of crucial predictive features for geospatial modeling.

Purpose of the Study:

  • To develop a user-friendly tool for exploring geospatial data features.
  • To enable similarity search and clustering for defined regions of interest.
  • To complement existing geospatial modeling tools by focusing on feature discovery.

Main Methods:

  • Developed Similarity Search (Sims), a no-code web tool.
  • Utilized Google Earth Engine as the backend for data processing.
  • Performed clustering and similarity search on geospatial data.

Main Results:

  • Demonstrated the utility of Sims in a case study with simulated maize yield data in Rwanda.
  • Evaluated the impact of soil, weather, and agronomic features on yield response zone clustering.
  • Showcased Sims' capability for feature exploration in complex environmental scenarios.

Conclusions:

  • Sims effectively facilitates feature exploration in geospatial data without coding.
  • The tool aids in understanding feature interactions influencing spatial patterns, such as crop yield.
  • Sims is an open-source resource designed to enhance geospatial modeling workflows.