AI 中文总结
针对区域聚合数据与点扫描算法不匹配的问题，提出用20-50个均匀采样点代替区域质心，提升统计功效且保持计算可行性。
AI 中文摘要
地理空间数据中的异常检测是地理信息科学（GIS）的关键工具，应用范围从国家安全到公共卫生监测再到社会差异研究。本文聚焦空间扫描统计，并解决一个关键不匹配：空间计数通常按预定义区域（人口普查区、邮政编码、县）聚合，而最高效的扫描算法则作用于空间点数据。标准的补救措施——如广泛使用的SaTScan等工具中将每个区域折叠为其质心——虽然方便，但如我们所示，会丢弃区域的空间范围并导致统计功效显著下降。为解决此问题，我们提出一种简单且可扩展的修复方法：用从其几何形状中均匀采样的20-50个点替换每个空间区域，并将其值均匀分布到这些点上。该方法在保持计算可行性的同时提高了统计功效。收敛性分析解释了为什么每个区域只需如此少的样本。我们推荐这种基于采样的转换作为将基于点的空间扫描统计应用于区域聚合数据进行异常检测的默认方式。
英文摘要
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.
CommentsAccepted at ACM SIGSPATIAL 2026. 24 pages, 15 figures. Companion code at https://github.com/foadnamjoo/sampling-region-scan
Journal refProc. 34th ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems (SIGSPATIAL '26), 2026
