Dynamic Visualization of Complex Space-Time Processes Applied to Daily PM2.5 Concentrations
Abstract Number:
2477
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Danielle Elterman (1), Dana Sylvan (2), Peter Craigmile (3)
Institutions:
(1) N/A, N/A, (2) Hunter College, City University of New York, N/A, (3) Hunter College, CUNY, N/A
Co-Author(s):
Dana Sylvan
Hunter College, City University of New York
First Author:
Presenting Author:
Abstract Text:
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.
Keywords:
Statistical Modeling|PM2.5 Pollution|Data Modeling| | |
Sponsors:
Lifetime Data Science Section
Tracks:
Miscellaneous
Can this be considered for alternate subtype?
Yes
Are you interested in volunteering to serve as a session chair?
No
I have read and understand that JSM participants must abide by the Participant Guidelines.
Yes
I understand that JSM participants must register and pay the appropriate registration fee by June 3, 2025. The registration fee is non-refundable.
I understand
You have unsaved changes.