Regional Clustering
Definition
Regional clustering groups areas into similar classes based on socio-economic, environmental, or infrastructural attributes. Techniques include k-means on standardized variables, hierarchical clustering, spectral clustering on adjacency graphs, and spatially constrained algorithms (SKATER, AZP) that respect contiguity. Feature selection, scaling, and handling of outliers influence results. Clusters reveal patterns for targeted policies—innovation hubs, vulnerable regions, service deserts. Maps should include stability tests across parameter choices and time to avoid overinterpreting one run. Communication requires clear, intuitive labels and examples rather than numeric codes alone.
Application
Governments target development funds; health systems tailor interventions; retailers design region-specific strategies; emergency planners pre-stage resources. Researchers compare cluster profiles to outcomes to test hypotheses.
FAQ
How to pick the number of clusters?
Use elbow/silhouette metrics and domain judgement; test multiple k and report stability to avoid arbitrary choices.
Can clusters be contiguous by design?
Yes with spatially constrained methods that enforce adjacency, producing interpretable regions for administration.
What about variable redundancy?
Reduce with PCA or remove highly collinear variables; keep interpretable features so policymakers grasp drivers.
How to validate clusters?
Out-of-sample tests, temporal persistence, and qualitative expert review with ground knowledge.
SUPPORT
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