Abstract / Summary
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 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 French air pollution data from the spring of 2020 and the winter of 2024 and we identified highly localized and interpretable clusters that are characterized by more time and transitions in “degraded” and “bad” states.