Geographic Clustering

Grid-based clustering with medoid finding and risk scoring from event density.

When to Use This Skill

• Clustering thousands of geo-located events for visualization
• Need O(n) performance instead of O(n²) distance-based clustering
• Want actual data points as map markers (medoids, not synthetic centroids)
• Calculating risk scores from cluster characteristics

Core Concepts

Grid-based clustering is much faster than distance-based algorithms:

• O(n) grouping by grid cell vs O(n²) for distance-based
• Medoid finding gives actual data point as center (better for map markers)
• Multi-factor risk scoring combines density, sentiment, and recency

Implementation