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arXiv · 2607.01451

Sampling for Region-Aggregated Spatial Scan Statistics

Foad Namjoo·Drew McClelland·Michael Matheny·Jeff M. Phillips
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Abstract

Anomaly detection in geospatial data is a crucial tool in geographic information science (GIS), with applications ranging from national security to public-health surveillance to the study of societal disparities. This work focuses on spatial scan statistics and addresses a key mismatch: spatial counts are typically aggregated into predefined regions (census tracts, zip codes, counties), whereas the most efficient scan algorithms operate on spatial point data. The standard remedy -- collapsing each region to its centroid, as in widely used tools such as SaTScan -- is convenient but, as we show, discards the region's spatial extent and causes a significant loss in statistical power. To resolve this, we propose a simple yet scalable fix: replace each spatial region with 20-50 points sampled uniformly from its geometry, and divide the region's measured and baseline counts evenly among them. This approach improves statistical power while maintaining computational tractability. A convergence analysis explains why so few samples per region suffice. We recommend this sampling-based conversion as the default way to apply point-based spatial scan statistics to region-aggregated data for anomaly detection.

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Foad Namjoo, Drew McClelland, Michael Matheny, Jeff M. Phillips. 2026-08-20. Sampling for Region-Aggregated Spatial Scan Statistics. https://doi.org/10.1145/3841645.3842988

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