WITHDRAWN Dynamic visualization for spatial-temporal processes with complex structures applied to recent PM2.5 data

Recent studies link air pollution exposure to health and therefore it is critical to identify spatial-temporal regions where such exposure risks are high. It is known that people and the environment are more adversely affected by excessive levels of pollution, thus the need to visualize and model effects of various quantiles of fine particles, in addition to mean effects. We propose versatile tools to describe and visualize quantiles of complex space-time data with various degrees of missingness and show how dynamic views provide useful insights into where and when the process changes. This approach does not require Gaussianity or stationarity and helps to guide future modeling efforts. We study daily PM2.5 concentrations for the years 2020-2024 collected at 108 locations in the states of New York, New Jersey, and Pennsylvania, illustrate how the PM2.5 exposure risks evolve over space and time and identify possible clusters. This approach demonstrates the importance of effective dynamic visualizations of complex spatial-temporal datasets by providing relevant summaries with their corresponding confidence regions.

spatial-temporal modeling

dynamic visualization

quantiles

fine particles

PM2.5 

Data Visualization

Symposium on Data Science and Statistics (SDSS) 2025