| Sumario: | This Learning Note explores the application of spatial K-means clustering as a data-driven approach to inform land use policy planning. By grouping geo-referenced spatial units based on key environmental and socio-economic variables, this method helps reconcile the need for localized data with the broader scale requirements of policy design. The note demonstrates the implementation of the approach in Ethiopia and China using 10x10 km pixels and variables related to soil health, food security, and infrastructure. It outlines a systematic process involving gap statistics, silhouette scores, and Principal Component Analysis to identify meaningful clusters and assess changes over time. Key methodological considerations such as data harmonization, scaling, and validation are discussed, highlighting the potential and limitations of the method. This work contributes to the broader MELIAF learning agenda by offering a replicable approach for spatially explicit policy analysis.
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