19: Dynamic Visualization of Complex Space-Time Processes Applied to Daily PM2.5 Concentrations

Music City Center 

Recent studies link air pollution exposure to human and environmental health. It is critical to identify time intervals and spatial regions where such exposure risks are high. For fine particles we need to be able to visualize and model effects of various quantiles, in addition to mean effects, since people are more adversely affected by excessive levels of pollution.
We propose versatile tools to describe and visualize quantiles of data with wide-ranging spatial-temporal structures and various degrees of missingness. We illustrate this methodology through dynamic visualization of spatial-temporal patterns to provide useful insights to where and when the process changes. This approach does not require strong theoretical assumptions and is useful to guide future modeling efforts.
The mentioned statistical framework is applied to daily PM2.5 concentrations for the years 2020-24 collected at 108 locations across NY, NJ, PA. We show how the PM2.5 exposure risks evolve over space and time, identifying possible clusters. Our approach demonstrates the importance of effective dynamic visualizations of complex spatial-temporal datasets, with plans to expand analysis to further regions.

Statistical Modeling

PM2.5 Pollution

Data Modeling 

Lifetime Data Science Section