Sampling for Region-Aggregated Spatial Scan Statistics

2026-07-01

AI Summary

This study addresses the mismatch between areal-aggregated geographic data and point-based spatial scan statistics, where representing regions by their centroids discards critical spatial information and reduces statistical power. To mitigate this limitation, the authors propose a simple yet scalable preprocessing strategy: uniformly sampling 20–50 points within each region’s geometry and distributing the region’s observed count equally among these points. This approach better preserves the underlying spatial distribution while remaining computationally tractable. Empirical evaluations demonstrate that the method substantially enhances the detection performance of spatial scan statistics on aggregated regional data across diverse scenarios. The authors advocate its adoption as a standard preprocessing step for analyzing areal-aggregated datasets in spatial anomaly detection tasks.

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 spread the region's values evenly across 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.

Problem

• spatial scan statistics
• region-aggregated data
• anomaly detection
• geospatial data
• statistical power

Innovation

• spatial scan statistics
• region-aggregated data
• uniform sampling
• anomaly detection
• geospatial analysis