We have developed and tested a spatial scan statistic for categorical, functional data (CFSS) - a data structure within which current approaches cannot identify spatial clusters. Our methodology combines an encoding scheme for categorical, functional observations with a nonparametric scan statistic. In a simulation study with three distinct scenarios, the CFSS accurately recovered the simulated spatial clusters and gave very low false positive rates, high true positive rates, and high positive predictive values. We have also used the CFSS to identify and characterize spatial clusters in French air pollution data from the winter of 2024.

翻译：我们开发并测试了一种针对分类功能数据（CFSS）的空间扫描统计方法——该数据结构在当前方法下无法识别空间聚类。我们的方法将分类功能观测的编码方案与非参数扫描统计相结合。在包含三种不同场景的模拟研究中，CFSS准确还原了模拟的空间聚类，并表现出极低的假阳性率、较高的真阳性率以及较高的阳性预测值。我们还应用CFSS对2024年冬季法国空气污染数据进行了空间聚类的识别与特征分析。