Sims让非代码用户快速分析地理空间数据的相似性与聚类。
Sims: An Interactive Tool for Geospatial Matching and Clustering
- 基于Google Earth Engine构建无代码工具，支持区域特征聚类与相似搜索。
- 在卢旺达玉米产量模拟数据上验证，发现土壤、气候与农艺因素组合影响产区分群。
- 适合地理信息分析、农业建模等领域的研究人员快速探索特征模式。
获取、处理和可视化地理空间数据需要大量计算资源，尤其在大时空范围下更为显著，这限制了预测特征的快速发现，而预测特征对推进地理空间建模至关重要。为此，我们开发了相似性搜索工具（Sims），一个无需编写代码的Web工具，利用Google Earth Engine作为后端，使用户能够在感兴趣的区域执行聚类和相似性搜索。Sims旨在补充现有建模工具，聚焦于特征探索而非模型构建。通过在卢旺达模拟的玉米产量数据案例研究中展示其有效性，评估了土壤、天气与农艺特征的不同组合如何影响产量响应区的聚类结果。Sims为开源项目，可在https://github.com/microsoft/Sims 获取。
原文摘要 · Abstract (English)
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
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